Sample records for neural events leading
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Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung
2018-04-23
In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.
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Automatic Classification of volcano-seismic events based on Deep Neural Networks.
Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.
2017-12-01
Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.
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Forecasting solar proton event with artificial neural network
Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.
Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.
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Neural network real time event selection for the DIRAC experiment
Kokkas, P; Tauscher, Ludwig; Vlachos, S
2001-01-01
The neural network first level trigger for the DIRAC experiment at CERN is presented. Both the neural network algorithm used and its actual hardware implementation are described. The system uses the fast plastic scintillator information of the DIRAC spectrometer. In 210 ns it selects events with two particles having low relative momentum. Such events are selected with an efficiency of more than 0.94. The corresponding rate reduction for background events is a factor of 2.5. (10 refs).
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Shindigs, brunches, and rodeos: the neural basis of event words.
Bedny, Marina; Dravida, Swethasri; Saxe, Rebecca
2014-09-01
Events (e.g., "running" or "eating") constitute a basic type within human cognition and human language. We asked whether thinking about events, as compared to other conceptual categories, depends on partially independent neural circuits. Indirect evidence for this hypothesis comes from previous studies showing elevated posterior temporal responses to verbs, which typically label events. Neural responses to verbs could, however, be driven either by their grammatical or by their semantic properties. In the present experiment, we separated the effects of grammatical class (verb vs. noun) and semantic category (event vs. object) by measuring neural responses to event nouns (e.g., "the hurricane"). Participants rated the semantic relatedness of event nouns, as well as of two categories of object nouns-animals (e.g., "the alligator") and plants (e.g., "the acorn")-and three categories of verbs-manner of motion (e.g., "to roll"), emission (e.g., "to sparkle"), and perception (e.g., "to gaze"). As has previously been observed, we found larger responses to verbs than to object nouns in the left posterior middle (LMTG) and superior (LSTG) temporal gyri. Crucially, we also found that the LMTG responds more to event than to object nouns. These data suggest that part of the posterior lateral temporal response to verbs is driven by their semantic properties. By contrast, a more superior region, at the junction of the temporal and parietal cortices, responded more to verbs than to all nouns, irrespective of their semantic category. We concluded that the neural mechanisms engaged when thinking about event and object categories are partially dissociable.
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Neural networks for event filtering at D/O/
International Nuclear Information System (INIS)
Cutts, D.; Hoftun, J.S.; Sornborger, A.; Johnson, C.R.; Zeller, R.T.
1989-01-01
Neural networks may provide important tools for pattern recognition in high energy physics. We discuss an initial exploration of these techniques, presenting the result of network simulations of several filter algorithms. The D0 data acquisition system, a MicroVAX farm, will perform critical event selection; we describe a possible implementation of neural network algorithms in this system. 7 refs., 4 figs
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Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen
2017-10-01
This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.
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Event-driven simulation of neural population synchronization facilitated by electrical coupling.
Carrillo, Richard R; Ros, Eduardo; Barbour, Boris; Boucheny, Christian; Coenen, Olivier
2007-02-01
Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.
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Convolutional neural networks for event-related potential detection: impact of the architecture.
Cecotti, H
2017-07-01
The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.
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Automatic Seismic-Event Classification with Convolutional Neural Networks.
Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.
2017-12-01
Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and
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Multi-modular neural networks for the classification of e+e- hadronic events
International Nuclear Information System (INIS)
Proriol, J.
1994-01-01
Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)
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The neural basis of event simulation: an FMRI study.
Directory of Open Access Journals (Sweden)
Yukihito Yomogida
Full Text Available Event simulation (ES is the situational inference process in which perceived event features such as objects, agents, and actions are associated in the brain to represent the whole situation. ES provides a common basis for various cognitive processes, such as perceptual prediction, situational understanding/prediction, and social cognition (such as mentalizing/trait inference. Here, functional magnetic resonance imaging was used to elucidate the neural substrates underlying important subdivisions within ES. First, the study investigated whether ES depends on different neural substrates when it is conducted explicitly and implicitly. Second, the existence of neural substrates specific to the future-prediction component of ES was assessed. Subjects were shown contextually related object pictures implying a situation and performed several picture-word-matching tasks. By varying task goals, subjects were made to infer the implied situation implicitly/explicitly or predict the future consequence of that situation. The results indicate that, whereas implicit ES activated the lateral prefrontal cortex and medial/lateral parietal cortex, explicit ES activated the medial prefrontal cortex, posterior cingulate cortex, and medial/lateral temporal cortex. Additionally, the left temporoparietal junction plays an important role in the future-prediction component of ES. These findings enrich our understanding of the neural substrates of the implicit/explicit/predictive aspects of ES-related cognitive processes.
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Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.
Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R
2017-01-01
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under
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Event-driven processing for hardware-efficient neural spike sorting
Liu, Yan; Pereira, João L.; Constandinou, Timothy G.
2018-02-01
Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.
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Recognition of power quality events by using multiwavelet-based neural networks
Energy Technology Data Exchange (ETDEWEB)
Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai [School of Electrical Engineering, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon Ratchasima 30000 (Thailand)
2008-05-15
Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. (author)
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Event management for large scale event-driven digital hardware spiking neural networks.
Caron, Louis-Charles; D'Haene, Michiel; Mailhot, Frédéric; Schrauwen, Benjamin; Rouat, Jean
2013-09-01
The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in software, very few digital hardware architectures are found. This is because existing hardware solutions for event management scale badly with the number of events. This paper introduces the structured heap queue, a pipelined digital hardware data structure, and demonstrates its suitability for event management. The structured heap queue scales gracefully with the number of events, allowing the efficient implementation of large scale digital hardware event-driven SNNs. The scaling is linear for memory, logarithmic for logic resources and constant for processing time. The use of the structured heap queue is demonstrated on a field-programmable gate array (FPGA) with an image segmentation experiment and a SNN of 65,536 neurons and 513,184 synapses. Events can be processed at the rate of 1 every 7 clock cycles and a 406×158 pixel image is segmented in 200 ms. Copyright © 2013 Elsevier Ltd. All rights reserved.
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A Markovian event-based framework for stochastic spiking neural networks.
Touboul, Jonathan D; Faugeras, Olivier D
2011-11-01
In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.
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Excess lead in the neural retina in age-related macular degeneration.
Erie, Jay C; Good, Jonathan A; Butz, John A
2009-12-01
To measure lead and cadmium in retinal tissues of human donor eyes with and without age-related macular degeneration (AMD). Laboratory investigation. Lead and cadmium concentrations in retinal tissues (neural retina and retinal pigment epithelium [RPE]-choroid complex) in 25 subjects with AMD (50 donor eyes) and 36 normal subjects (72 donor eyes) were determined by using inductively coupled plasma-mass spectrometry. Severity of AMD was graded by using color fundus photographs and the Minnesota Grading System. Differences in metal concentrations were compared by using Wilcoxon rank-sum tests. The neural retinas of subjects with AMD had increased lead concentrations (median, 12.0 ng/g; 25% to 75% interquartile range, 8 to 18 ng/g; n = 25) compared with normal subjects (median, 8.0 ng/g; 25% to 75% interquartile range, 0 to 11 ng/g; P = .04; n = 36). There was no difference in lead concentration in the RPE-choroid complex between subjects with AMD (median, 198 ng/g; 25% to 75% interquartile range, 87 to 381 ng/g) and normal subjects (median, 172 ng/g; 25% to 75% interquartile range, 100 to 288 ng/g; P = .25). Cadmium concentration in the neural retina (median, 0.9 microg/g; 25% to 75% interquartile range, 0.7 to 1.8 microg/g) and RPE-choroid complex (median, 2.2 microg/g; 25% to 75% interquartile range, 1.8 to 3.7 microg/g) in subjects with AMD was not different from concentrations in the neural retina (median, 0.9 microg/g; 25% to 75% interquartile range, 0.7 to 1.4 microg/g; P = .32) and RPE-choroid complex (median, 1.5 microg/g; 25% to 75% interquartile range, 0.9 to 2.5 microg/g; P = .12) of normal subjects. AMD is associated with excess lead in the neural retina, and this relationship suggests that metal homeostasis in AMD eyes is different from normal.
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The ATLAS Experiment, ATLAS, First collisions, Splash events
2010-01-01
Image Captions CERN-EX-1011310 01 -- Event display of a highly asymmetric dijet event, with one jet with ET > 100 GeV and no evident recoiling jet, recorded by ATLAS in LHC lead-lead collisions. CERN-EX-1011310 02 -- Event display of a highly asymmetric dijet event, with one jet with ET > 100 GeV and no evident recoiling jet, recorded by ATLAS in LHC lead-lead collisions. CERN-EX-1011310 03 -- Event display of a highly asymmetric dijet event, with one jet with ET > 100 GeV and no evident recoiling jet, recorded by ATLAS in LHC lead-lead collisions. CERN-EX-1011310 04 -- LHC lead-lead collisions recorded by ATLAS with a candidate Z to μ+μ- decay. The two muons shown in purple are the candidates to originate from the Z decay. The transverse momenta of these two muons are 44 and 45 GeV, and the invariant mass of the dimuon system is 93 GeV. CERN-EX-1011310 05 -- LHC lead-lead collisions recorded by ATLAS with a candidate Z to μ+μ- decay. The two muons shown in red are the candidates to originate from the...
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Neural correlates of attentional and mnemonic processing in event-based prospective memory.
Knight, Justin B; Ethridge, Lauren E; Marsh, Richard L; Clementz, Brett A
2010-01-01
Prospective memory (PM), or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT), followed by a LDT with an embedded PM component. Event-based cues were constituted by color and lexicality (red words). Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP) revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.
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A robust neural network-based approach for microseismic event detection
Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel
2017-01-01
We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset
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Directory of Open Access Journals (Sweden)
Hyoung‐Gook Kim
2017-12-01
Full Text Available Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.
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Neural correlates of attentional and mnemonic processing in event-based prospective memory
Directory of Open Access Journals (Sweden)
Justin B Knight
2010-02-01
Full Text Available Prospective memory, or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT, followed by a LDT with an embedded prospective memory (PM component. Event-based cues were constituted by color and lexicality (red words. Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.
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Event classification with the electronic detectors of the OPERA experiment using neural networks
International Nuclear Information System (INIS)
Hierholzer, Martin C.
2012-02-01
The OPERA experiment searches for ν μ ν τ oscillations in appearance mode. It uses the emulsion cloud chamber (ECC) technique for a high spatial resolution combined with on-line components for event localisation and muon identification. The analysis of events in an ECC detector takes considerable time, especially in case of ν τ /ν e candidate events. A ranking of events by a probability for being a ν τ /ν e event can speed up the analysis of the OPERA experiment. An algorithm for such an event ranking based on a classification-type neural network is presented in this thesis. Almost all candidate events can be found within the first 30% of the analysed events if the described ranking is applied. This event ranking is currently applied for testing purposes by the OPERA collaboration, a decision on a full application for the whole analysis is pending. A similar neural network is used for discrimination between neutral and charged current events. This is used to observe neutrino oscillations in disappearance mode with the on-line components of the OPERA detector by measuring the energy dependence of the fraction of neutral current interactions. The confidence level of the observed oscillation effect is 87%. Assuming full mixing, the mass splitting has been determined to vertical stroke Δm 2 32 vertical stroke =2.8 -1.7 +1.4 .10 -3 eV 2 .
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Directory of Open Access Journals (Sweden)
Achmad Arwan
2016-07-01
Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results
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Alternative splicing events identified in human embryonic stem cells and neural progenitors.
Directory of Open Access Journals (Sweden)
Gene W Yeo
2007-10-01
Full Text Available Human embryonic stem cells (hESCs and neural progenitor (NP cells are excellent models for recapitulating early neuronal development in vitro, and are key to establishing strategies for the treatment of degenerative disorders. While much effort had been undertaken to analyze transcriptional and epigenetic differences during the transition of hESC to NP, very little work has been performed to understand post-transcriptional changes during neuronal differentiation. Alternative RNA splicing (AS, a major form of post-transcriptional gene regulation, is important in mammalian development and neuronal function. Human ESC, hESC-derived NP, and human central nervous system stem cells were compared using Affymetrix exon arrays. We introduced an outlier detection approach, REAP (Regression-based Exon Array Protocol, to identify 1,737 internal exons that are predicted to undergo AS in NP compared to hESC. Experimental validation of REAP-predicted AS events indicated a threshold-dependent sensitivity ranging from 56% to 69%, at a specificity of 77% to 96%. REAP predictions significantly overlapped sets of alternative events identified using expressed sequence tags and evolutionarily conserved AS events. Our results also reveal that focusing on differentially expressed genes between hESC and NP will overlook 14% of potential AS genes. In addition, we found that REAP predictions are enriched in genes encoding serine/threonine kinase and helicase activities. An example is a REAP-predicted alternative exon in the SLK (serine/threonine kinase 2 gene that is differentially included in hESC, but skipped in NP as well as in other differentiated tissues. Lastly, comparative sequence analysis revealed conserved intronic cis-regulatory elements such as the FOX1/2 binding site GCAUG as being proximal to candidate AS exons, suggesting that FOX1/2 may participate in the regulation of AS in NP and hESC. In summary, a new methodology for exon array analysis was introduced
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Crowley, Michael J; Wu, Jia; Molfese, Peter J; Mayes, Linda C
2010-01-01
This study examined neural activity with event-related potentials (ERPs) in middle childhood during a computer-simulated ball-toss game, Cyberball. After experiencing fair play initially, children were ultimately excluded by the other players. We focused specifically on “not my turn” events within fair play and rejection events within social exclusion. Dense-array ERPs revealed that rejection events are perceived rapidly. Condition differences (“not my turn” vs. rejection) were evident in a posterior ERP peaking at 420 ms consistent, with a larger P3 effect for rejection events indicating that in middle childhood rejection events are differentiated in <500 ms. Condition differences were evident for slow-wave activity (500-900 ms) in the medial frontal cortical region and the posterior occipital-parietal region, with rejection events more negative frontally and more positive posteriorly. Distress from the rejection experience was associated with a more negative frontal slow wave and a larger late positive slow wave, but only for rejection events. Source modeling with Geosouce software suggested that slow-wave neural activity in cortical regions previously identified in functional imaging studies of ostracism, including subgenual cortex, ventral anterior cingulate cortex, and insula, was greater for rejection events vs. “not my turn” events. © 2010 Psychology Press
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Time-lapse imaging of neural development: zebrafish lead the way into the fourth dimension.
Rieger, Sandra; Wang, Fang; Sagasti, Alvaro
2011-07-01
Time-lapse imaging is often the only way to appreciate fully the many dynamic cell movements critical to neural development. Zebrafish possess many advantages that make them the best vertebrate model organism for live imaging of dynamic development events. This review will discuss technical considerations of time-lapse imaging experiments in zebrafish, describe selected examples of imaging studies in zebrafish that revealed new features or principles of neural development, and consider the promise and challenges of future time-lapse studies of neural development in zebrafish embryos and adults. Copyright © 2011 Wiley-Liss, Inc.
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Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials.
Jin, Jia; Yu, Liping; Ma, Qingguo
2015-01-01
Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW) task and a boring watch-stop task (WS) to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 amplitude than that of the WS task. Furthermore, in the outcome feedback stage, the outcome of the SW task induced smaller FRN amplitude and larger P300 amplitude than that of the WS task. These results suggested that human intrinsic motivation did exist and that it can be detected at the neural level. Furthermore, intrinsic motivation could be quantitatively indexed by the amplitude of ERP components, such as N2, FRN, and P300, in the cue priming stage or feedback stage. Quantitative measurements would also be convenient for intrinsic motivation to be added as a candidate social factor in the construction of a machine learning model.
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Event classification with the electronic detectors of the OPERA experiment using neural networks
Energy Technology Data Exchange (ETDEWEB)
Hierholzer, Martin C.
2012-02-15
The OPERA experiment searches for {nu}{sub {mu}} <-> {nu}{sub {tau}} oscillations in appearance mode. It uses the emulsion cloud chamber (ECC) technique for a high spatial resolution combined with on-line components for event localisation and muon identification. The analysis of events in an ECC detector takes considerable time, especially in case of {nu}{sub {tau}}/{nu}{sub e} candidate events. A ranking of events by a probability for being a {nu}{sub {tau}}/{nu}{sub e} event can speed up the analysis of the OPERA experiment. An algorithm for such an event ranking based on a classification-type neural network is presented in this thesis. Almost all candidate events can be found within the first 30% of the analysed events if the described ranking is applied. This event ranking is currently applied for testing purposes by the OPERA collaboration, a decision on a full application for the whole analysis is pending. A similar neural network is used for discrimination between neutral and charged current events. This is used to observe neutrino oscillations in disappearance mode with the on-line components of the OPERA detector by measuring the energy dependence of the fraction of neutral current interactions. The confidence level of the observed oscillation effect is 87%. Assuming full mixing, the mass splitting has been determined to vertical stroke {delta}m{sup 2}{sub 32} vertical stroke =2.8{sub -1.7}{sup +1.4}.10{sup -3}eV{sup 2}.
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A robust neural network-based approach for microseismic event detection
Akram, Jubran
2017-08-17
We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.
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Not my future? Core values and the neural representation of future events.
Brosch, Tobias; Stussi, Yoann; Desrichard, Olivier; Sander, David
2018-06-01
Individuals with pronounced self-transcendence values have been shown to put greater weight on the long-term consequences of their actions when making decisions. Using functional magnetic resonance imaging, we investigated the neural mechanisms underlying the evaluation of events occurring several decades in the future as well as the role of core values in these processes. Thirty-six participants viewed a series of events, consisting of potential consequences of climate change, which could occur in the near future (around 2030), and thus would be experienced by the participants themselves, or in the far future (around 2080). We observed increased activation in anterior VMPFC (BA11), a region involved in encoding the personal significance of future events, when participants were envisioning far future events, demonstrating for the first time that the role of the VMPFC in future projection extends to the time scale of decades. Importantly, this activation increase was observed only in participants with pronounced self-transcendence values measured by self-report questionnaire, as shown by a statistically significant interaction of temporal distance and value structure. These findings suggest that future projection mechanisms are modulated by self-transcendence values to allow for a more extensive simulation of far future events. Consistent with this, these participants reported similar concern ratings for near and far future events, whereas participants with pronounced self-enhancement values were more concerned about near future events. Our findings provide a neural substrate for the tendency of individuals with pronounced self-transcendence values to consider the long-term consequences of their actions.
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Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
International Nuclear Information System (INIS)
Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.
2017-01-01
Here, we present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
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Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
Energy Technology Data Exchange (ETDEWEB)
Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anad?n, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C. -M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Caicedo, D. A. Martinez; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; S?ldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.
2017-03-01
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
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From sensation to percept: the neural signature of auditory event-related potentials.
Joos, Kathleen; Gilles, Annick; Van de Heyning, Paul; De Ridder, Dirk; Vanneste, Sven
2014-05-01
An external auditory stimulus induces an auditory sensation which may lead to a conscious auditory perception. Although the sensory aspect is well known, it is still a question how an auditory stimulus results in an individual's conscious percept. To unravel the uncertainties concerning the neural correlates of a conscious auditory percept, event-related potentials may serve as a useful tool. In the current review we mainly wanted to shed light on the perceptual aspects of auditory processing and therefore we mainly focused on the auditory late-latency responses. Moreover, there is increasing evidence that perception is an active process in which the brain searches for the information it expects to be present, suggesting that auditory perception requires the presence of both bottom-up, i.e. sensory and top-down, i.e. prediction-driven processing. Therefore, the auditory evoked potentials will be interpreted in the context of the Bayesian brain model, in which the brain predicts which information it expects and when this will happen. The internal representation of the auditory environment will be verified by sensation samples of the environment (P50, N100). When this incoming information violates the expectation, it will induce the emission of a prediction error signal (Mismatch Negativity), activating higher-order neural networks and inducing the update of prior internal representations of the environment (P300). Copyright © 2014 Elsevier Ltd. All rights reserved.
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Knox, Dayan; Stanfield, Briana R.; Staib, Jennifer M.; David, Nina P.; Keller, Samantha M.; DePietro, Thomas
2016-01-01
Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions…
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Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun; Zhongyang Fei; Chaoxu Guan; Huijun Gao; Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun
2018-06-01
This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended ( , , )-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.
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Tagging b and c quark events in e+e- collisions with neural networks
International Nuclear Information System (INIS)
Proriol, J.; Falvard, A.; Henrard, P.; Jousset, J.; Brandl, B.
1992-01-01
High purity samples of b quark events and, if possible, of c quark events are attempted to produce, and the width of Γ(Z 0 → bb-bar) is measured. The different variables and the method to select the most discriminating variables are given. The physical results obtained with these methods are recalled, and new results are presented with variables connected with the impact parameter. Some neural networks used throughout this work and some results on c quark events selection are also presented. (K.A.) 9 refs.; 6 figs
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Urtnasan, Erdenebayar; Park, Jong-Uk; Lee, Kyoung-Joung
2018-05-24
In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45,096 events) and evaluated on a test dataset (11,274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. The proposed CNN model reaches a mean -score of 93.0 for the training dataset and 87.0 for the test dataset. Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH. © 2018 Institute of Physics and Engineering in Medicine.
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Neural robust stabilization via event-triggering mechanism and adaptive learning technique.
Wang, Ding; Liu, Derong
2018-06-01
The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.
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International Nuclear Information System (INIS)
AllamehZadeh, Mostafa
2011-01-01
A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0–6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.
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Energy Technology Data Exchange (ETDEWEB)
AllamehZadeh, Mostafa, E-mail: dibaparima@yahoo.com [International Institute of Earthquake Engineering and Seismology (Iran, Islamic Republic of)
2011-12-15
A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.
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A Hierarchical Convolutional Neural Network for vesicle fusion event classification.
Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke
2017-09-01
Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
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An on-line non-leptonic neural trigger applied to an experiment looking for beauty
Baldanza, C; Cotta-Ramusino, A; D'Antone, I; Malferrari, L; Mazzanti, P; Odorici, F; Odorico, R; Zuffa, M; Bruschini, C; Musico, P; Novelli, P; Passaseo, M
1994-01-01
Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV 1t- on a Cu target, are presented. The neural trigger has been used to send on a special data stream (the Fast Stream) events to be analyzed with high priority. The non-leptonic signature uses microvertex detector data and was devised so as to enrich the fraction of events containing C3 secondary vertices (i.e, vertices having three tracks whith sum of electric charges equal to +1 or -1). The neural trigger module consists of a VME crate hosting two ET ANN analog neural chips from Intel. The neural trigger operated for two continuous weeks during the WA92 1 993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6-7.l (depending on the event sample considered), which multiplied by that already provided by the standard non-leptonic trigger leads to a global C3 enrichment factor of -1 50. In the event sample selected by the neural trigge...
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Energy Technology Data Exchange (ETDEWEB)
Attisha, Michael J. [Brown U.
2006-01-01
The Cryogenic Dark Matter Search (CDMS) experiment is designed to search for dark matter in the form of Weakly Interacting Massive Particles (WIMPs) via their elastic scattering interactions with nuclei. This dissertation presents the CDMS detector technology and the commissioning of two towers of detectors at the deep underground site in Soudan, Minnesota. CDMS detectors comprise crystals of Ge and Si at temperatures of 20 mK which provide ~keV energy resolution and the ability to perform particle identification on an event by event basis. Event identification is performed via a two-fold interaction signature; an ionization response and an athermal phonon response. Phonons and charged particles result in electron recoils in the crystal, while neutrons and WIMPs result in nuclear recoils. Since the ionization response is quenched by a factor ~ 3(2) in Ge(Si) for nuclear recoils compared to electron recoils, the relative amplitude of the two detector responses allows discrimination between recoil types. The primary source of background events in CDMS arises from electron recoils in the outer 50 µm of the detector surface which have a reduced ionization response. We develop a quantitative model of this ‘dead layer’ effect and successfully apply the model to Monte Carlo simulation of CDMS calibration data. Analysis of data from the two tower run March-August 2004 is performed, resulting in the world’s most sensitive limits on the spin-independent WIMP-nucleon cross-section, with a 90% C.L. upper limit of 1.6 × 10-43 cm2 on Ge for a 60 GeV WIMP. An approach to performing surface event discrimination using neural networks and wavelets is developed. A Bayesian methodology to classifying surface events using neural networks is found to provide an optimized method based on minimization of the expected dark matter limit. The discrete wavelet analysis of CDMS phonon pulses improves surface event discrimination in conjunction with the neural
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A statistical framework for evaluating neural networks to predict recurrent events in breast cancer
Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda
2010-07-01
Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.
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Discrimination of panti p → tanti t events by a neural network classifier
International Nuclear Information System (INIS)
Cherubini, A.; Odorico, R.
1992-01-01
Neural network and conventional statistical techniques are compared in the problem of discriminating panti p→tanti t events, with top quarks decaying into anything, from the associated hadronic background at the energy of the Fermilab collider. The NN we develop for this sake is an improved version of Kohonen's learning vector quantization scheme. Performance of the NN as a tanti t event classifier is found to be less satisfactory than that achievable by statistical methods. We conclude that the probable reasons for that are: i) The NN approach presents advantages only when dealing with event distributions in the feature space which substantially differ from Gaussians; ii) NN's require much larger training sets of events than statistical discrimination in order to give comparable results. (orig.)
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Differences in the neural signature of remembering schema-congruent and schema-incongruent events.
Brod, Garvin; Lindenberger, Ulman; Werkle-Bergner, Markus; Shing, Yee Lee
2015-08-15
New experiences are remembered in relation to one's existing world knowledge or schema. Recent research suggests that the medial prefrontal cortex (mPFC) supports the retrieval of schema-congruent information. However, the neural mechanisms supporting memory for information violating a schema have remained elusive, presumably because incongruity is inherently ambiguous in tasks that rely on world knowledge. We present a novel paradigm that experimentally induces hierarchically structured knowledge to directly contrast neural correlates that contribute to the successful retrieval of schema-congruent versus schema-incongruent information. We hypothesize that remembering incongruent events engages source memory networks including the lateral PFC. In a sample of young adults, we observed enhanced activity in the dorsolateral PFC (DLPFC), in the posterior parietal cortex, and in the striatum when successfully retrieving incongruent events, along with enhanced connectivity between DLPFC and striatum. In addition, we found enhanced mPFC activity for successfully retrieved events that are congruent with the induced schema, presumably reflecting a role of the mPFC in biasing retrieval towards schema-congruent episodes. We conclude that medial and lateral PFC contributions to memory retrieval differ by schema congruency, and highlight the utility of the new experimental paradigm for addressing developmental research questions. Copyright © 2015 Elsevier Inc. All rights reserved.
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NEVESIM: event-driven neural simulation framework with a Python interface.
Pecevski, Dejan; Kappel, David; Jonke, Zeno
2014-01-01
NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.
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Addis, Donna Rose; Wong, Alana T.; Schacter, Daniel L.
2006-01-01
People can consciously re-experience past events and pre-experience possible future events. This fMRI study examined the neural regions mediating the construction and elaboration of past and future events. Participants were cued with a noun for 20 seconds and instructed to construct a past or future event within a specified time period (week, year, 5–20 years). Once participants had the event in mind, they made a button press and for the remainder of the 20 seconds elaborated on the event. Im...
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Leading order determination of the gluon polarisation from DIS events with high-$p_T$ hadron pairs
Adolph, C; Alexakhin, V Yu; Alexandrov, Yu; Alexeev, G D; Amoroso, A; Antonov, A A; Austregesilo, A; Badelek, B; Balestra, F; Barth, J; Baum, G; Bedfer, Y; Bernhard, J; Bertini, R; Bettinelli, M; Bicker, K; Bieling, J; Birsa, R; Bisplinghoff, J; Bordalo, P; Bradamante, F; Braun, C; Bravar, A; Bressan, A; Burtin, E; Chaberny, D; Chiosso, M; Chung, S U; Cicuttin, A; Crespo, M L; Dalla Torre, S; Das, S; Dasgupta, S S; Denisov, O.Yu; Dhara, L; Donskov, S V; Doshita, N; Duic, V; Dunnweber, W; Dziewiecki, M; Efremov, A; Elia, C; Eversheim, P D; Eyrich, W; Faessler, M; Ferrero, A; Filin, A; Finger, M; jr., M.Finger; Fischer, H; Franco, C; von Hohenesche, N.du Fresne; Friedrich, J M; Garfagnini, R; Gautheron, F; Gavrichtchouk, O P; Gazda, R; Gerassimov, S; Geyer, R; Giorgi, M; Gnesi, I; Gobbo, B; Goertz, S; Grabmuller, S; Grasso, A; Grube, B; Gushterski, R; Guskov, A; Guthorl, T; Haas, F; von Harrach, D; Hedicke, S; Heinsius, F H; Herrmann, F; Hess, C; Hinterberger, F; Horikawa, N; Hoppner, Ch; d'Hose, N; Huber, S; Ishimoto, S; Ivanov, O; Ivanshin, Yu; Iwata, T; Jahn, R; Jasinski, P; Joosten, R; Kabuss, E; Kang, D; Ketzer, B; Khaustov, G V; Khokhlov, Yu.A; Kisselev, Yu; Klein, F; Klimaszewski, K; Koblitz, S; Koivuniemi, J H; Kolosov, V N; Kondo, K; Konigsmann, K; Konorov, I; Konstantinov, V F; Korzenev, A; Kotzinian, A M; Kouznetsov, O; Kramer, M; Kroumchtein, Z V; Kunne, F.; Kurek, K; Lauser, L; Le Goff, J M; Lednev, A A; Lehmann, A; Levorato, S; Lichtenstadt, J; Maggiora, A; Magnon, A; Makke, N; Mallot, G K; Mann, A; Marchand, C; Martin, A; Marzec, J; Matsuda, T; Meyer, W; Michigami, T; Mikhailov, Yu.V; Moinester, M A; Morreale, A; Mutter, A; Nagaytsev, A; Nagel, T; Nassalski, J P; Nerling, F; Neubert, S; Neyret, D; Nikolaenko, V I; Nowak, W D; Nunes, A S; Olshevsky, A G; Ostrick, M; Padee, A; Panknin, R; Panzieri, D; Parsamyan, B; Paul, S.; Perevalova, E; Pesaro, G; Peshekhonov, D V; Piragino, G; Platchkov, S; Pochodzalla, J; Polak, J; Polyakov, V A; Pontecorvo, G; Pretz, J; Procureur, S L; Quaresma, M; Quintans, C; Rajotte, J F; Ramos, S; Rapatsky, V; Reicherz, G; Richter, A; Rocco, E; Rondio, E; Rossiyskaya, N S; Ryabchikov, D I; Samoylenko, V D; Sandacz, A; Sapozhnikov, M G; Sarkar, S.; Savin, I A; Sbrizzai, G; Schiavon, P; Schill, C.; Schluter, T; Schmidt, K; Schmitt, L; Schonning, K; Schopferer, S; Schott, M; Shevchenko, O.Yu; Silva, L; Sinha, L; Sissakian, A N; Slunecka, M; Smirnov, G I; Sosio, S; Sozzi, F; Srnka, A; Stolarski, M; Sulc, M; Sulej, R; Sznajder, P; Takekawa, S; Wolbeek, J.Ter; Tessaro, S; Tessarotto, F; Tkatchev, L G; Uhl, S; Uman, I; Vandenbroucke, M; Virius, M; Vlassov, N V; Wang, L; Windmolders, R; Wislicki, W; Wollny, H; Zaremba, K; Zavertyaev, M; Zemlyanichkina, E; Ziembicki, M; Zhuravlev, N; Zvyagin, A
2013-01-01
We present a determination of the gluon polarisation Delta g/g in the nucleon, based on the longitudinal double-spin asymmetry of DIS events with a pair of large transverse-momentum hadrons in the final state. The data were obtained by the COMPASS experiment at CERN using a 160 GeV/c polarised muon beam scattering off a polarised ^6LiD target. The gluon polarisation is evaluated by a Neural Network approach for three intervals of the gluon momentum fraction x_g covering the range 0.04 < x_g < 0.27. The values obtained at leading order in QCD do not show any significant dependence on x_g. Their average is Delta g/g = 0.125 +/- 0.060 (stat.) +/- 0.063 (syst.) at x_g=0.09 and a scale of mu^2 = 3~(GeV/c)^2.
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Identification of canonical neural events during continuous gameplay of an 8-bit style video game.
Cavanagh, James F; Castellanos, Joel
2016-06-01
Cognitive neuroscience suffers from a unique and pervasive problem of generalizability. Since neural findings are often interpreted in the context of a specific manipulation during a carefully controlled task, it is hard to transfer knowledge from one task to another. In this report we address problems of generalizability with two methodological advancements. First, we aimed to transcend status quo experimental procedures with a continuous, engaging task environment. To this end, we created a novel 8-bit style continuous space shooter video game that elicits a multitude of goal-oriented events, such as crashing into a wall or blowing up an enemy with a missile. Second, we aimed to objectively define the psychological significance of these events. To achieve this aim, we used pattern classification of EEG data to derive predictive weights from carefully controlled pre-game exemplar events (oddball target detection and gambling wins and losses) and transferred those weights to EEG activities during video game events. All major goal-oriented events (crashes into the wall, crashes into an enemy, missile hit on an enemy) had a significant between-task transfer bias towards oddball target weights in the time range of the canonical P3, indicating the presence of similar salience detection processes. Missile hits on an enemy were specifically identified as gambling wins, confirming the hypothesis that this goal-oriented event was appetitive. These findings suggest that it is possible to identify the contribution of canonical neural activities during otherwise ambiguous and uncontrolled task performance. Copyright © 2016 Elsevier Inc. All rights reserved.
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Results from an on-line non-leptonic neural trigger implemented in an experiment looking for beauty
International Nuclear Information System (INIS)
Baldanza, C.; Musico, P.; Novelli, P.; Passaseo, M.
1995-01-01
Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV negative pions on a fixed Cu target, are presented. The neural trigger has been used to send events selected by means of a non-leptonic signature based on microvertex detector information to a special data stream, meant for early analysis. The non-leptonic signature, defined in a neural-network fashion, was devised so as to enrich the selected sample in the number of events containing C3 secondary vertices (i.e, vertices having three tracks with sum of electric charges equal to +1 or -1), which are sought for further analysis to identify charm and beauty non-leptonic decays. The neural trigger module consists of a VME crate hosting two MA16 digital neural chips from Siemens and two ETANN analog neural chips from Intel. During the experimental run, only the ETANN chips were operational. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6-7.1 (depending on the event sample considered), which multiplied by that already provided by the standard trigger leads to a global C3 enrichment factor of similar 150. In the event sample selected by the neural trigger, one every similar 7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 μs. (orig.)
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Results from an on-line non-leptonic neural trigger implemented in an experiment looking for beauty
Energy Technology Data Exchange (ETDEWEB)
Baldanza, C. [INFN, Bologna (Italy). ANNETTHE; Bisi, F. [INFN, Bologna (Italy). ANNETTHE; Cotta-Ramusino, A. [INFN, Bologna (Italy). ANNETTHE; D`Antone, I. [INFN, Bologna (Italy). ANNETTHE; Malferrari, L. [INFN, Bologna (Italy). ANNETTHE; Mazzanti, P. [INFN, Bologna (Italy). ANNETTHE; Odorici, F. [INFN, Bologna (Italy). ANNETTHE; Odorico, R. [INFN, Bologna (Italy). ANNETTHE; Zuffa, M. [INFN, Bologna (Italy). ANNETTHE; Bruschini, C. [Istituto Nazionale di Fisica Nucleare, Genoa (Italy); Musico, P. [Istituto Nazionale di Fisica Nucleare, Genoa (Italy); Novelli, P. [Istituto Nazionale di Fisica Nucleare, Genoa (Italy); Passaseo, M. [European Organization for Nuclear Research, Geneva (Switzerland)
1995-07-15
Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV negative pions on a fixed Cu target, are presented. The neural trigger has been used to send events selected by means of a non-leptonic signature based on microvertex detector information to a special data stream, meant for early analysis. The non-leptonic signature, defined in a neural-network fashion, was devised so as to enrich the selected sample in the number of events containing C3 secondary vertices (i.e, vertices having three tracks with sum of electric charges equal to +1 or -1), which are sought for further analysis to identify charm and beauty non-leptonic decays. The neural trigger module consists of a VME crate hosting two MA16 digital neural chips from Siemens and two ETANN analog neural chips from Intel. During the experimental run, only the ETANN chips were operational. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6-7.1 (depending on the event sample considered), which multiplied by that already provided by the standard trigger leads to a global C3 enrichment factor of similar 150. In the event sample selected by the neural trigger, one every similar 7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 {mu}s. (orig.).
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International Nuclear Information System (INIS)
Xu, Y; Meng, Y X; Xu, W W
2008-01-01
A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of the toy detector are generated in the signal region. The Bayesian Neural Networks (BNN) are applied to separate neutrino events from backgrounds in reactor neutrino experiments. As a result, the most neutrino events and uncorrelated background events in the signal region can be identified with BNN, and the part events each of the fast neutron and 8 He/ 9 Li backgrounds in the signal region can be identified with BNN. Then, the signal to noise ratio in the signal region is enhanced with BNN. The neutrino discrimination increases with the increase of the neutrino rate in the training sample. However, the background discriminations decrease with the decrease of the background rate in the training sample
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Identification of pp→K±π-+ K0(K0) events using artificial neural networks
International Nuclear Information System (INIS)
Pavlopoulos, P.; Polivka, G.; Vlachos, S.; Wendler, H.
1995-01-01
An artificial neural network classifier has been developed in order to separate on-line events containing a decaying neutral kaon. The proposed system performs better than classical selection methods in real-time applications. The biases due to the on-line selection are smaller than few per cent and are known. The system is robust against calibration variations and missing information. A hardware implementation of the algorithm allows an event selection in less than 40 μs and provides a considerable on-line rate reduction in tagged neutral kaon experiments. This classifier has been developed within the framework of the CPLEAR experiment. (orig.)
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Nishii, Nobuhiro; Miyoshi, Akihito; Kubo, Motoki; Miyamoto, Masakazu; Morimoto, Yoshimasa; Kawada, Satoshi; Nakagawa, Koji; Watanabe, Atsuyuki; Nakamura, Kazufumi; Morita, Hiroshi; Ito, Hiroshi
2018-03-01
Remote monitoring (RM) has been advocated as the new standard of care for patients with cardiovascular implantable electronic devices (CIEDs). RM has allowed the early detection of adverse clinical events, such as arrhythmia, lead failure, and battery depletion. However, lead failure was often identified only by arrhythmic events, but not impedance abnormalities. To compare the usefulness of arrhythmic events with conventional impedance abnormalities for identifying lead failure in CIED patients followed by RM. CIED patients in 12 hospitals have been followed by the RM center in Okayama University Hospital. All transmitted data have been analyzed and summarized. From April 2009 to March 2016, 1,873 patients have been followed by the RM center. During the mean follow-up period of 775 days, 42 lead failure events (atrial lead 22, right ventricular pacemaker lead 5, implantable cardioverter defibrillator [ICD] lead 15) were detected. The proportion of lead failures detected only by arrhythmic events, which were not detected by conventional impedance abnormalities, was significantly higher than that detected by impedance abnormalities (arrhythmic event 76.2%, 95% CI: 60.5-87.9%; impedance abnormalities 23.8%, 95% CI: 12.1-39.5%). Twenty-seven events (64.7%) were detected without any alert. Of 15 patients with ICD lead failure, none has experienced inappropriate therapy. RM can detect lead failure earlier, before clinical adverse events. However, CIEDs often diagnose lead failure as just arrhythmic events without any warning. Thus, to detect lead failure earlier, careful human analysis of arrhythmic events is useful. © 2017 Wiley Periodicals, Inc.
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Applying Bayesian neural networks to event reconstruction in reactor neutrino experiments
International Nuclear Information System (INIS)
Xu Ye; Xu Weiwei; Meng Yixiong; Zhu Kaien; Xu Wei
2008-01-01
A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The electron samples from the Monte-Carlo simulation of the toy detector have been reconstructed by the method of Bayesian neural networks (BNNs) and the standard algorithm, a maximum likelihood method (MLD), respectively. The result of the event reconstruction using BNN has been compared with the one using MLD. Compared to MLD, the uncertainties of the electron vertex are not improved, but the energy resolutions are significantly improved using BNN. And the improvement is more obvious for the high energy electrons than the low energy ones
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Differences in alarm events between disposable and reusable electrocardiography lead wires.
Albert, Nancy M; Murray, Terri; Bena, James F; Slifcak, Ellen; Roach, Joel D; Spence, Jackie; Burkle, Alicia
2015-01-01
Disposable electrocardiographic lead wires (ECG-LWs) may not be as durable as reusable ones. To examine differences in alarm events between disposable and reusable ECG-LWs. Two cardiac telemetry units were randomized to reusable ECG-LWs, and 2 units alternated between disposable and reusable ECG-LWs for 4 months. A remote monitoring team, blinded to ECG-LW type, assessed frequency and type of alarm events by using total counts and rates per 100 patient days. Event rates were compared by using generalized linear mixed-effect models for differences and noninferiority between wire types. In 1611 patients and 9385.5 patient days of ECG monitoring, patient characteristics were similar between groups. Rates of alarms for no telemetry, leads fail, or leads off were lower in disposable ECG-LWs (adjusted relative risk [95% CI], 0.71 [0.53-0.96]; noninferiority P < .001; superiority P = .03) and monitoring (artifact) alarms were significantly noninferior (adjusted relative risk [95% CI]: 0.88, [0.62-1.24], P = .02; superiority P = .44). No between-group differences existed in false or true crisis alarms. Disposable ECG-LWs were noninferior to reusable ECG-LWs for all false-alarm events (N [rate per 100 patient days], disposable 2029 [79.1] vs reusable 6673 [97.9]; adjusted relative risk [95% CI]: 0.81 [0.63-1.06], P = .002; superiority P = .12.) Disposable ECG-LWs with patented push-button design had superior performance in reducing alarms created by no telemetry, leads fail, or leads off and significant noninferiority in all false-alarm rates compared with reusable ECG-LWs. Fewer ECG alarms may save nurses time, decrease alarm fatigue, and improve patient safety. ©2015 American Association of Critical-Care Nurses.
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Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The present study tested whether neural sensitivity to salient emotional facial expressions was influenced by emotional expectations induced by a cue that validly predicted the expression of a subsequently presented target face. Event-related potentials (ERPs) elicited by fearful and neutral faces were recorded while participants performed a gender discrimination task under cued (‘expected’) and uncued (‘unexpected’) conditions. The behavioral results revealed that accuracy was lower for fearful compared with neutral faces in the unexpected condition, while accuracy was similar for fearful and neutral faces in the expected condition. ERP data revealed increased amplitudes in the P2 component and 200–250 ms interval for unexpected fearful versus neutral faces. By contrast, ERP responses were similar for fearful and neutral faces in the expected condition. These findings indicate that human neural sensitivity to fearful faces is modulated by emotional expectations. Although the neural system is sensitive to unpredictable emotionally salient stimuli, sensitivity to salient stimuli is reduced when these stimuli are predictable.
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ALADDIN: a neural model for event classification in dynamic processes
International Nuclear Information System (INIS)
Roverso, Davide
1998-02-01
ALADDIN is a prototype system which combines fuzzy clustering techniques and artificial neural network (ANN) models in a novel approach to the problem of classifying events in dynamic processes. The main motivation for the development of such a system derived originally from the problem of finding new principled methods to perform alarm structuring/suppression in a nuclear power plant (NPP) alarm system. One such method consists in basing the alarm structuring/suppression on a fast recognition of the event generating the alarms, so that a subset of alarms sufficient to efficiently handle the current fault can be selected to be presented to the operator, minimizing in this way the operator's workload in a potentially stressful situation. The scope of application of a system like ALADDIN goes however beyond alarm handling, to include diagnostic tasks in general. The eventual application of the system to domains other than NPPs was also taken into special consideration during the design phase. In this document we report on the first phase of the ALADDIN project which consisted mainly in a comparative study of a series of ANN-based approaches to event classification, and on the proposal of a first system prototype which is to undergo further tests and, eventually, be integrated in existing alarm, diagnosis, and accident management systems such as CASH, IDS, and CAMS. (author)
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Directory of Open Access Journals (Sweden)
Irina Popova
2013-08-01
Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.
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QCD event generators with next-to-leading order matrix-elements and parton showers
International Nuclear Information System (INIS)
Kurihara, Y.; Fujimoto, J.; Ishikawa, T.; Kato, K.; Kawabata, S.; Munehisa, T.; Tanaka, H.
2003-01-01
A new method to construct event-generators based on next-to-leading order QCD matrix-elements and leading-logarithmic parton showers is proposed. Matrix elements of loop diagram as well as those of a tree level can be generated using an automatic system. A soft/collinear singularity is treated using a leading-log subtraction method. Higher order resummation of the soft/collinear correction by the parton shower method is combined with the NLO matrix-element without any double-counting in this method. An example of the event generator for Drell-Yan process is given for demonstrating a validity of this method
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Neural network approach in multichannel auditory event-related potential analysis.
Wu, F Y; Slater, J D; Ramsay, R E
1994-04-01
Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.
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Cellular and molecular events leading to the development of skin cancer
International Nuclear Information System (INIS)
Melnikova, Vladislava O.; Ananthaswamy, Honnavara N.
2005-01-01
The transition from a normal cell to a neoplastic cell is a complex process and involves both genetic and epigenetic changes. The process of carcinogenesis begins when the DNA is damaged, which then leads to a cascade of events leading to the development of a tumor. Ultraviolet (UV) radiation causes DNA damage, inflammation, erythema, sunburn, immunosuppression, photoaging, gene mutations, and skin cancer. Upon DNA damage, the p53 tumor suppressor protein undergoes phosphorylation and translocation to the nucleus and aids in DNA repair or causes apoptosis. Excessive UV exposure overwhelms DNA repair mechanisms leading to induction of p53 mutations and loss of Fas-FasL interaction. Keratinocytes carrying p53 mutations acquire a growth advantage by virtue of their increased resistance to apoptosis. Thus, resistance to cell death is a key event in photocarcinogenesis and conversely, elimination of cells containing excessive UV-induced DNA damage is a key step in protecting against skin cancer development. Apoptosis-resistant keratinocytes undergo clonal expansion that eventually leads to formation of actinic keratoses and squamous cell carcinomas. In this article, we will review some of the cellular and molecular mechanisms involved in initiation and progression of UV-induced skin cancer
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Cellular and molecular events leading to the development of skin cancer
Energy Technology Data Exchange (ETDEWEB)
Melnikova, Vladislava O. [Department of Immunology, University of Texas M.D. Anderson Cancer Center, P.O. Box 301402, Unit 902, Houston, TX 77030 (United States); Ananthaswamy, Honnavara N. [Department of Immunology, University of Texas M.D. Anderson Cancer Center, P.O. Box 301402, Unit 902, Houston, TX 77030 (United States)]. E-mail: hanantha@mdanderson.org
2005-04-01
The transition from a normal cell to a neoplastic cell is a complex process and involves both genetic and epigenetic changes. The process of carcinogenesis begins when the DNA is damaged, which then leads to a cascade of events leading to the development of a tumor. Ultraviolet (UV) radiation causes DNA damage, inflammation, erythema, sunburn, immunosuppression, photoaging, gene mutations, and skin cancer. Upon DNA damage, the p53 tumor suppressor protein undergoes phosphorylation and translocation to the nucleus and aids in DNA repair or causes apoptosis. Excessive UV exposure overwhelms DNA repair mechanisms leading to induction of p53 mutations and loss of Fas-FasL interaction. Keratinocytes carrying p53 mutations acquire a growth advantage by virtue of their increased resistance to apoptosis. Thus, resistance to cell death is a key event in photocarcinogenesis and conversely, elimination of cells containing excessive UV-induced DNA damage is a key step in protecting against skin cancer development. Apoptosis-resistant keratinocytes undergo clonal expansion that eventually leads to formation of actinic keratoses and squamous cell carcinomas. In this article, we will review some of the cellular and molecular mechanisms involved in initiation and progression of UV-induced skin cancer.
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Events Leading to One Person's Career in Forest Entomology
John C. Moser
2000-01-01
Today, I'm going to discuss the subject that I know most about--the important events and the many mentors leading to my career in Forest Entomology at the Southern Research Station, as well as my past and present cooperators. This includes my views on the present state of SPB Research, as well as my future plans for the next 40 years.
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The neural basis of financial risk taking.
Kuhnen, Camelia M; Knutson, Brian
2005-09-01
Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.
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Specific neural basis of Chinese idioms processing: an event-related functional MRI study
International Nuclear Information System (INIS)
Chen Shaoqi; Zhang Yanzhen; Xiao Zhuangwei; Zhang Xuexin
2007-01-01
Objective: To address the neural basis of Chinese idioms processing with different kinds of stimuli using an event-related fMRI design. Methods: Sixteen native Chinese speakers were asked to perform a semantic decision task during fMRI scanning. Three kinds of stimuli were used: Real idioms (Real-idiom condition); Literally plausible phrases (Pseudo-idiom condition, the last character of a real idiom was replaced by a character with similar meaning); Literally implausible strings (Non-idiom condition, the last character of a real idiom was replaced by a character with unrelated meaning). Reaction time and correct rate were recorded at the same time. Results: The error rate was 2.6%, 5.2% and 0.9% (F=3.51, P 0.05) for real idioms, pseudo-idioms and wrong idioms, respectively. Similar neural network was activated in all of the three conditions. However, the right hippocampus was only activated in the real idiom condition, and significant activations were found in anterior portion of left inferior frontal gyms (BA47) in real-and pseudo-idiom conditions, but not in non-idiom condition. Conclusion: The right hippocampus plays a specific role in the particular wording of the Chinese idioms. And the left anterior inferior frontal gyms (BA47) may be engaged in the semantic processing of Chinese idioms. The results support the notion that there were specific neural bases for Chinese idioms processing. (authors)
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Botzung, Anne; Denkova, Ekaterina; Manning, Lilianne
2008-01-01
Functional MRI was used in healthy subjects to investigate the existence of common neural structures supporting re-experiencing the past and pre-experiencing the future. Past and future events evocation appears to involve highly similar patterns of brain activation including, in particular, the medial prefrontal cortex, posterior regions and the…
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Sridharan, Devarajan; Levitin, Daniel J; Chafe, Chris H; Berger, Jonathan; Menon, Vinod
2007-08-02
The real world presents our sensory systems with a continuous stream of undifferentiated information. Segmentation of this stream at event boundaries is necessary for object identification and feature extraction. Here, we investigate the neural dynamics of event segmentation in entire musical symphonies under natural listening conditions. We isolated time-dependent sequences of brain responses in a 10 s window surrounding transitions between movements of symphonic works. A strikingly right-lateralized network of brain regions showed peak response during the movement transitions when, paradoxically, there was no physical stimulus. Model-dependent and model-free analysis techniques provided converging evidence for activity in two distinct functional networks at the movement transition: a ventral fronto-temporal network associated with detecting salient events, followed in time by a dorsal fronto-parietal network associated with maintaining attention and updating working memory. Our study provides direct experimental evidence for dissociable and causally linked ventral and dorsal networks during event segmentation of ecologically valid auditory stimuli.
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Measurement of dijet photoproduction for events with a leading neutron at HERA
International Nuclear Information System (INIS)
Chekanov, S.; Derrick, M.; Magill, B.
2009-09-01
Differential cross sections for dijet photoproduction and this process in association with a leading neutron, e + +p→e + +jet+jet+X(+n), have been measured with the ZEUS detector at HERA using an integrated luminosity of 40 pb -1 . The fraction of dijet events with a leading neutron was studied as a function of different jet and event variables. Single- and double-differential cross sections are presented as a function of the longitudinal fraction of the proton momentum carried by the leading neutron, x L , and of its transverse momentum squared, p T 2 . The dijet data are compared to inclusive DIS and photoproduction results; they are all consistent with a simple pion-exchange model. The neutron yield as a function of x L was found to depend only on the fraction of the proton beam energy going into the forward region, independent of the hard process. No firm conclusion can be drawn on the presence of rescattering effects. (orig)
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Neural chips, neural computers and application in high and superhigh energy physics experiments
International Nuclear Information System (INIS)
Nikityuk, N.M.; )
2001-01-01
Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru
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Multi-core events in cosmic-ray induced interactions with lead at around 10 TeV
International Nuclear Information System (INIS)
Amato, N.; Arata, N.
1989-01-01
The analysis is made on the cosmic-ray induced interactions with lead at around 10 TeV on the basis of emulsion chamber data at Chacaltaya. A special attention is paid to the events detected as multi-cores under the spatial resolution of a few tens of microns. The observation of six double-core events and two triple-core events with the average invariant mass of 1.8 GeV/c 2 leads to the estimation on production frequency of such multicores as about 5% at 10 TeV at the atmospheric depth 540 gr/cm 2 . (author)
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Xie, Jiaheng; Liu, Xiao; Dajun Zeng, Daniel
2018-01-01
Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed. Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Measurement of dijet photoproduction for events with a leading neutron at HERA
Energy Technology Data Exchange (ETDEWEB)
Chekanov, S.; Derrick, M.; Magill, B. [Argonne National Lab., Argonne, IL (US)] (and others)
2009-09-15
Differential cross sections for dijet photoproduction and this process in association with a leading neutron, e{sup +}+p{yields}e{sup +}+jet+jet+X(+n), have been measured with the ZEUS detector at HERA using an integrated luminosity of 40 pb{sup -1}. The fraction of dijet events with a leading neutron was studied as a function of different jet and event variables. Single- and double-differential cross sections are presented as a function of the longitudinal fraction of the proton momentum carried by the leading neutron, x{sub L}, and of its transverse momentum squared, p{sub T}{sup 2}. The dijet data are compared to inclusive DIS and photoproduction results; they are all consistent with a simple pion-exchange model. The neutron yield as a function of x{sub L} was found to depend only on the fraction of the proton beam energy going into the forward region, independent of the hard process. No firm conclusion can be drawn on the presence of rescattering effects. (orig)
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Directory of Open Access Journals (Sweden)
Evangelos Stromatias
2017-06-01
Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.
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Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé
2017-01-01
This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.
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Leading indicators of community-based violent events among adults with mental illness.
Van Dorn, R A; Grimm, K J; Desmarais, S L; Tueller, S J; Johnson, K L; Swartz, M S
2017-05-01
The public health, public safety and clinical implications of violent events among adults with mental illness are significant; however, the causes and consequences of violence and victimization among adults with mental illness are complex and not well understood, which limits the effectiveness of clinical interventions and risk management strategies. This study examined interrelationships between violence, victimization, psychiatric symptoms, substance use, homelessness and in-patient treatment over time. Available data were integrated from four longitudinal studies of adults with mental illness. Assessments took place at baseline, and at 1, 3, 6, 9, 12, 15, 18, 24, 30 and 36 months, depending on the parent studies' protocol. Data were analysed with the autoregressive cross-lag model. Violence and victimization were leading indicators of each other and affective symptoms were a leading indicator of both. Drug and alcohol use were leading indicators of violence and victimization, respectively. All psychiatric symptom clusters - affective, positive, negative, disorganized cognitive processing - increased the likelihood of experiencing at least one subsequent symptom cluster. Sensitivity analyses identified few group-based differences in the magnitude of effects in this heterogeneous sample. Violent events demonstrated unique and shared indicators and consequences over time. Findings indicate mechanisms for reducing violent events, including trauma-informed therapy, targeting internalizing and externalizing affective symptoms with cognitive-behavioral and psychopharmacological interventions, and integrating substance use and psychiatric care. Finally, mental illness and violence and victimization research should move beyond demonstrating concomitant relationships and instead focus on lagged effects with improved spatio-temporal contiguity.
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Xu, Chengjin; Guan, Junjun; Bao, Ming; Lu, Jiangang; Ye, Wei
2018-01-01
Based on vibration signals detected by a phase-sensitive optical time-domain reflectometer distributed optical fiber sensing system, this paper presents an implement of time-frequency analysis and convolutional neural network (CNN), used to classify different types of vibrational events. First, spectral subtraction and the short-time Fourier transform are used to enhance time-frequency features of vibration signals and transform different types of vibration signals into spectrograms, which are input to the CNN for automatic feature extraction and classification. Finally, by replacing the soft-max layer in the CNN with a multiclass support vector machine, the performance of the classifier is enhanced. Experiments show that after using this method to process 4000 vibration signal samples generated by four different vibration events, namely, digging, walking, vehicles passing, and damaging, the recognition rates of vibration events are over 90%. The experimental results prove that this method can automatically make an effective feature selection and greatly improve the classification accuracy of vibrational events in distributed optical fiber sensing systems.
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Rehmert, Andrea E; Kisley, Michael A
2013-10-01
Older adults have demonstrated an avoidance of negative information, presumably with a goal of greater emotional satisfaction. Understanding whether avoidance of negative information is a voluntary, motivated choice or an involuntary, automatic response will be important to differentiate, as decision making often involves emotional factors. With the use of an emotional framing event-related potential (ERP) paradigm, the present study investigated whether older adults could alter neural responses to negative stimuli through verbal reframing of evaluative response options. The late positive potential (LPP) response of 50 older adults and 50 younger adults was recorded while participants categorized emotional images in one of two framing conditions: positive ("more or less positive") or negative ("more or less negative"). It was hypothesized that older adults would be able to overcome a presumed tendency to down-regulate neural responding to negative stimuli in the negative framing condition, thus leading to larger LPP wave amplitudes to negative images. A similar effect was predicted for younger adults, but for positively valenced images, such that LPP responses would be increased in the positive framing condition compared with the negative framing condition. Overall, younger adults' LPP wave amplitudes were modulated by framing condition, including a reduction in the negativity bias in the positive frame. Older adults' neural responses were not significantly modulated, even though task-related behavior supported the notion that older adults were able to successfully adopt the negative framing condition.
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Rehmert, Andrea E.; Kisley, Michael A.
2014-01-01
Older adults have demonstrated an avoidance of negative information presumably with a goal of greater emotional satisfaction. Understanding whether avoidance of negative information is a voluntary, motivated choice, or an involuntary, automatic response will be important to differentiate, as decision-making often involves emotional factors. With the use of an emotional framing event-related potential (ERP) paradigm, the present study investigated whether older adults could alter neural responses to negative stimuli through verbal reframing of evaluative response options. The late-positive potential (LPP) response of 50 older adults and 50 younger adults was recorded while participants categorized emotional images in one of two framing conditions: positive (“more or less positive”) or negative (“more or less negative”). It was hypothesized that older adults would be able to overcome a presumed tendency to down-regulate neural responding to negative stimuli in the negative framing condition thus leading to larger LPP wave amplitudes to negative images. A similar effect was predicted for younger adults but for positively valenced images such that LPP responses would be increased in the positive framing condition compared to the negative framing condition. Overall, younger adults' LPP wave amplitudes were modulated by framing condition, including a reduction in the negativity bias in the positive frame. Older adults' neural responses were not significantly modulated even though task-related behavior supported the notion that older adults were able to successfully adopt the negative framing condition. PMID:23731435
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Zhu, Wei; Wang, Dandan; Liu, Lu; Feng, Gang
2017-08-18
This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.
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Directory of Open Access Journals (Sweden)
S. Lee Hong
2012-09-01
Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.
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Cough event classification by pretrained deep neural network.
Liu, Jia-Ming; You, Mingyu; Wang, Zheng; Li, Guo-Zheng; Xu, Xianghuai; Qiu, Zhongmin
2015-01-01
Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. In this paper, we tried pretrained deep neural network in
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Neural Correlates of Processing Negative and Sexually Arousing Pictures
Bailey, Kira; West, Robert; Mullaney, Kellie M.
2012-01-01
Recent work has questioned whether the negativity bias is a distinct component of affective picture processing. The current study was designed to determine whether there are different neural correlates of processing positive and negative pictures using event-related brain potentials. The early posterior negativity and late positive potential were greatest in amplitude for erotic pictures. Partial Least Squares analysis revealed one latent variable that distinguished erotic pictures from neutral and positive pictures and another that differentiated negative pictures from neutral and positive pictures. The effects of orienting task on the neural correlates of processing negative and erotic pictures indicate that affective picture processing is sensitive to both stimulus-driven, and attentional or decision processes. The current data, together with other recent findings from our laboratory, lead to the suggestion that there are distinct neural correlates of processing negative and positive stimuli during affective picture processing. PMID:23029071
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Event-related theta synchronization predicts deficit in facial affect recognition in schizophrenia.
Csukly, Gábor; Stefanics, Gábor; Komlósi, Sarolta; Czigler, István; Czobor, Pál
2014-02-01
Growing evidence suggests that abnormalities in the synchronized oscillatory activity of neurons in schizophrenia may lead to impaired neural activation and temporal coding and thus lead to neurocognitive dysfunctions, such as deficits in facial affect recognition. To gain an insight into the neurobiological processes linked to facial affect recognition, we investigated both induced and evoked oscillatory activity by calculating the Event Related Spectral Perturbation (ERSP) and the Inter Trial Coherence (ITC) during facial affect recognition. Fearful and neutral faces as well as nonface patches were presented to 24 patients with schizophrenia and 24 matched healthy controls while EEG was recorded. The participants' task was to recognize facial expressions. Because previous findings with healthy controls showed that facial feature decoding was associated primarily with oscillatory activity in the theta band, we analyzed ERSP and ITC in this frequency band in the time interval of 140-200 ms, which corresponds to the N170 component. Event-related theta activity and phase-locking to facial expressions, but not to nonface patches, predicted emotion recognition performance in both controls and patients. Event-related changes in theta amplitude and phase-locking were found to be significantly weaker in patients compared with healthy controls, which is in line with previous investigations showing decreased neural synchronization in the low frequency bands in patients with schizophrenia. Neural synchrony is thought to underlie distributed information processing. Our results indicate a less effective functioning in the recognition process of facial features, which may contribute to a less effective social cognition in schizophrenia. PsycINFO Database Record (c) 2014 APA, all rights reserved.
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Directory of Open Access Journals (Sweden)
Guido Gigante
2015-11-01
Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.
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Pattern recognition of state variables by neural networks
International Nuclear Information System (INIS)
Faria, Eduardo Fernandes; Pereira, Claubia
1996-01-01
An artificial intelligence system based on artificial neural networks can be used to classify predefined events and emergency procedures. These systems are being used in different areas. In the nuclear reactors safety, the goal is the classification of events whose data can be processed and recognized by neural networks. In this works we present a preliminary simple system, using neural networks in the recognition of patterns the recognition of variables which define a situation. (author)
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Artificial Neural Network Models for Long Lead Streamflow Forecasts using Climate Information
Kumar, J.; Devineni, N.
2007-12-01
Information on season ahead stream flow forecasts is very beneficial for the operation and management of water supply systems. Daily streamflow conditions at any particular reservoir primarily depend on atmospheric and land surface conditions including the soil moisture and snow pack. On the other hand recent studies suggest that developing long lead streamflow forecasts (3 months ahead) typically depends on exogenous climatic conditions particularly Sea Surface Temperature conditions (SST) in the tropical oceans. Examples of some oceanic variables are El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Identification of such conditions that influence the moisture transport into a given basin poses many challenges given the nonlinear dependency between the predictors (SST) and predictand (stream flows). In this study, we apply both linear and nonlinear dependency measures to identify the predictors that influence the winter flows into the Neuse basin. The predictor identification approach here adopted uses simple correlation coefficients to spearman rank correlation measures for detecting nonlinear dependency. All these dependency measures are employed with a lag 3 time series of the high flow season (January - February - March) using 75 years (1928-2002) of stream flows recorded in to the Falls Lake, Neuse River Basin. Developing streamflow forecasts contingent on these exogenous predictors will play an important role towards improved water supply planning and management. Recently, the soft computing techniques, such as artificial neural networks (ANNs) have provided an alternative method to solve complex problems efficiently. ANNs are data driven models which trains on the examples given to it. The ANNs functions as universal approximators and are non linear in nature. This paper presents a study aiming towards using climatic predictors for 3 month lead time streamflow forecast. ANN models representing the physical process of the system are
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Stress, fatigue, and sleep quality leading up to and following a stressful life event.
Van Laethem, Michelle; Beckers, Debby G J; Dijksterhuis, Ap; Geurts, Sabine A E
2017-10-01
This study aims to examine (a) the time course of stress, fatigue, and sleep quality among PhD students awaiting a stressful event and (b) whether daily anticipation of this event influences day-level stress, fatigue, and sleep quality. Forty-four PhD students completed evening and morning questionnaires on eight days from 1 month before their dissertation defense until one month thereafter. Results showed increased stress leading up to the defense, while fatigue and sleep quality remained unchanged. Comparing the night before the defense with the night after, stress rapidly decreased, whereas fatigue and sleep quality increased. Following the defense, stress and sleep quality remained stable, whereas fatigue declined. Stress 1 month before the defense was higher than 1 month thereafter. Regarding day-level relations, stress was adversely affected by negative anticipation and favorably by positive outcome expectancy, whereas positive anticipation had no influence. Positive outcome expectancy was an important predictor of improved sleep quality. We conclude that stress may be elevated long before a stressful event takes place but that one can recover rather quickly from temporary stress. Positive outcome expectancy of a stressful event may be an important predictor of reduced day-level stress and improved day-level sleep quality leading up to a stressful event. Copyright © 2016 John Wiley & Sons, Ltd.
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Catastrophic events leading to de facto limits on liability
International Nuclear Information System (INIS)
Solomon, K.A.; Okrent, D.
1977-05-01
This study conducts an overview of large technological systems in society to ascertain prevalence, if any, of situations that can lead to catastrophic effects where the resultant liabilities far exceed the insurances or assets subject to suit in court, thereby imposing de facto limits on liability. Several potential situations are examined: dam rupture, aircraft crash into a sports stadium, chemical plant accident, shipping disaster, and a toxic drug disaster. All of these events are estimated to have probabilities per year similar to or larger than a major nuclear accident and they are found to involve potential liability far exceeding the available resources, such as insurance, corporation assets, or government revenues
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Combining neural networks and genetic algorithms for hydrological flow forecasting
Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr
2010-05-01
We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and
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International Nuclear Information System (INIS)
El Haddad, J.; Bruyère, D.; Ismaël, A.; Gallou, G.; Laperche, V.; Michel, K.; Canioni, L.; Bousquet, B.
2014-01-01
Artificial neural networks were applied to process data from on-site LIBS analysis of soil samples. A first artificial neural network allowed retrieving the relative amounts of silicate, calcareous and ores matrices into soils. As a consequence, each soil sample was correctly located inside the ternary diagram characterized by these three matrices, as verified by ICP-AES. Then a series of artificial neural networks were applied to quantify lead into soil samples. More precisely, two models were designed for classification purpose according to both the type of matrix and the range of lead concentrations. Then, three quantitative models were locally applied to three data subsets. This complete approach allowed reaching a relative error of prediction close to 20%, considered as satisfying in the case of on-site analysis. - Highlights: • Application of a series of artificial neural networks (ANN) to quantitative LIBS • Matrix-based classification of the soil samples by ANN • Concentration-based classification of the soil samples by ANN • Series of quantitative ANN models dedicated to the analysis of data subsets • Relative error of prediction lower than 20% for LIBS analysis of soil samples
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Runoff Modelling in Urban Storm Drainage by Neural Networks
DEFF Research Database (Denmark)
Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld
1995-01-01
A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....
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Neural network classification of quark and gluon jets
International Nuclear Information System (INIS)
Graham, M.A.; Jones, L.M.; Herbin, S.
1995-01-01
We demonstrate that there are characteristics common to quark jets and to gluon jets regardless of the interaction that produced them. The classification technique we use depends on the mass of the jet as well as the center-of-mass energy of the hard subprocess that produces the jet. In addition, we present the quark-gluon separability results of an artificial neural network trained on three-jet e + e - events at the Z 0 mass, using a back-propagation algorithm. The inputs to the network are the longitudinal momenta of the leading hadrons in the jet. We tested the network with quark and gluon jets from both e + e - →3 jets and bar pp→2 jets. Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts
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Briefly Cuing Memories Leads to Suppression of Their Neural Representations
Norman, Kenneth A.
2014-01-01
Previous studies have linked partial memory activation with impaired subsequent memory retrieval (e.g., Detre et al., 2013) but have not provided an account of this phenomenon at the level of memory representations: How does partial activation change the neural pattern subsequently elicited when the memory is cued? To address this question, we conducted a functional magnetic resonance imaging (fMRI) experiment in which participants studied word-scene paired associates. Later, we weakly reactivated some memories by briefly presenting the cue word during a rapid serial visual presentation (RSVP) task; other memories were more strongly reactivated or not reactivated at all. We tested participants' memory for the paired associates before and after RSVP. Cues that were briefly presented during RSVP triggered reduced levels of scene activity on the post-RSVP memory test, relative to the other conditions. We used pattern similarity analysis to assess how representations changed as a function of the RSVP manipulation. For briefly cued pairs, we found that neural patterns elicited by the same cue on the pre- and post-RSVP tests (preA–postA; preB–postB) were less similar than neural patterns elicited by different cues (preA–postB; preB–postA). These similarity reductions were predicted by neural measures of memory activation during RSVP. Through simulation, we show that our pattern similarity results are consistent with a model in which partial memory activation triggers selective weakening of the strongest parts of the memory. PMID:24899722
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Chondroitin sulfate effects on neural stem cell differentiation.
Canning, David R; Brelsford, Natalie R; Lovett, Neil W
2016-01-01
We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.
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Drug-Induced Liver Injury: Cascade of Events Leading to Cell Death, Apoptosis or Necrosis
Directory of Open Access Journals (Sweden)
Andrea Iorga
2017-05-01
Full Text Available Drug-induced liver injury (DILI can broadly be divided into predictable and dose dependent such as acetaminophen (APAP and unpredictable or idiosyncratic DILI (IDILI. Liver injury from drug hepatotoxicity (whether idiosyncratic or predictable results in hepatocyte cell death and inflammation. The cascade of events leading to DILI and the cell death subroutine (apoptosis or necrosis of the cell depend largely on the culprit drug. Direct toxins to hepatocytes likely induce oxidative organelle stress (such as endoplasmic reticulum (ER and mitochondrial stress leading to necrosis or apoptosis, while cell death in idiosyncratic DILI (IDILI is usually the result of engagement of the innate and adaptive immune system (likely apoptotic, involving death receptors (DR. Here, we review the hepatocyte cell death pathways both in direct hepatotoxicity such as in APAP DILI as well as in IDILI. We examine the known signaling pathways in APAP toxicity, a model of necrotic liver cell death. We also explore what is known about the genetic basis of IDILI and the molecular pathways leading to immune activation and how these events can trigger hepatotoxicity and cell death.
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Botzung, Anne; Labar, Kevin S; Kragel, Philip; Miles, Amanda; Rubin, David C
2010-01-01
To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.
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Directory of Open Access Journals (Sweden)
Anne Botzung
2010-05-01
Full Text Available To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI. During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets or from non-viewed portions of the same game (foils. After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan’s perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.
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Neural principles of memory and a neural theory of analogical insight
Lawson, David I.; Lawson, Anton E.
1993-12-01
Grossberg's principles of neural modeling are reviewed and extended to provide a neural level theory to explain how analogies greatly increase the rate of learning and can, in fact, make learning and retention possible. In terms of memory, the key point is that the mind is able to recognize and recall when it is able to match sensory input from new objects, events, or situations with past memory records of similar objects, events, or situations. When a match occurs, an adaptive resonance is set up in which the synaptic strengths of neurons are increased; thus a long term record of the new input is formed in memory. Systems of neurons called outstars and instars are presumably the underlying units that enable this to occur. Analogies can greatly facilitate learning and retention because they activate the outstars (i.e., the cells that are sampling the to-be-learned pattern) and cause the neural activity to grow exponentially by forming feedback loops. This increased activity insures the boost in synaptic strengths of neurons, thus causing storage and retention in long-term memory (i.e., learning).
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Directory of Open Access Journals (Sweden)
Yi Pang
Full Text Available Perinatal infection is a well-identified risk factor for a number of neurodevelopmental disorders, including brain white matter injury (WMI and Autism Spectrum Disorders (ASD. The underlying mechanisms by which early life inflammatory events cause aberrant neural, cytoarchitectural, and network organization, remain elusive. This study is aimed to investigate how systemic lipopolysaccharide (LPS-induced neuroinflammation affects microglia phenotypes and early neural developmental events in rats. We show here that LPS exposure at early postnatal day 3 leads to a robust microglia activation which is characterized with mixed microglial proinflammatory (M1 and anti-inflammatory (M2 phenotypes. More specifically, we found that microglial M1 markers iNOS and MHC-II were induced at relatively low levels in a regionally restricted manner, whereas M2 markers CD206 and TGFβ were strongly upregulated in a sub-set of activated microglia in multiple white and gray matter structures. This unique microglial response was associated with a marked decrease in naturally occurring apoptosis, but an increase in cell proliferation in the subventricular zone (SVZ and the dentate gyrus (DG of hippocampus. LPS exposure also leads to a significant increase in oligodendrocyte lineage population without causing discernible hypermyelination. Moreover, LPS-exposed rats exhibited significant impairments in communicative and cognitive functions. These findings suggest a possible role of M2-like microglial activation in abnormal neural development that may underlie ASD-like behavioral impairments.
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Huang, Siyuan; Han, Shihui
2014-01-01
Recent neuroimaging research has revealed stronger empathic neural responses to same-race compared to other-race individuals. Is the in-group favouritism in empathic neural responses specific to race identification or a more general effect of social identification-including those based on religious/irreligious beliefs? The present study investigated whether and how intergroup relationships based on religious/irreligious identifications modulate empathic neural responses to others' pain expressions. We recorded event-related brain potentials from Chinese Christian and atheist participants while they perceived pain or neutral expressions of Chinese faces that were marked as being Christians or atheists. We found that both Christian and atheist participants showed stronger neural activity to pain (versus neutral) expressions at 132-168 ms and 200-320 ms over the frontal region to those with the same (versus different) religious/irreligious beliefs. The in-group favouritism in empathic neural responses was also evident in a later time window (412-612 ms) over the central/parietal regions in Christian but not in atheist participants. Our results indicate that the intergroup relationship based on shared beliefs, either religious or irreligious, can lead to in-group favouritism in empathy for others' suffering.
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Consecutive Acupuncture Stimulations Lead to Significantly Decreased Neural Responses
Yeo, S.; Choe, I.H.; Noort, M.W.M.L. van den; Bosch, M.P.C.; Lim, S.
2010-01-01
Objective: Functional magnetic resonance imaging (fMRI), in combination with block design paradigms with consecutive acupuncture stimulations, has often been used to investigate the neural responses to acupuncture. In this study, we investigated whether previous acupuncture stimulations can affect
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Training Deep Spiking Neural Networks Using Backpropagation.
Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael
2016-01-01
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.
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Gallego, C.; Costa, A.; Cuerva, A.
2010-09-01
Since nowadays wind energy can't be neither scheduled nor large-scale storaged, wind power forecasting has been useful to minimize the impact of wind fluctuations. In particular, short-term forecasting (characterised by prediction horizons from minutes to a few days) is currently required by energy producers (in a daily electricity market context) and the TSO's (in order to keep the stability/balance of an electrical system). Within the short-term background, time-series based models (i.e., statistical models) have shown a better performance than NWP models for horizons up to few hours. These models try to learn and replicate the dynamic shown by the time series of a certain variable. When considering the power output of wind farms, ramp events are usually observed, being characterized by a large positive gradient in the time series (ramp-up) or negative (ramp-down) during relatively short time periods (few hours). Ramp events may be motivated by many different causes, involving generally several spatial scales, since the large scale (fronts, low pressure systems) up to the local scale (wind turbine shut-down due to high wind speed, yaw misalignment due to fast changes of wind direction). Hence, the output power may show unexpected dynamics during ramp events depending on the underlying processes; consequently, traditional statistical models considering only one dynamic for the hole power time series may be inappropriate. This work proposes a Regime Switching (RS) model based on Artificial Neural Nets (ANN). The RS-ANN model gathers as many ANN's as different dynamics considered (called regimes); a certain ANN is selected so as to predict the output power, depending on the current regime. The current regime is on-line updated based on a gradient criteria, regarding the past two values of the output power. 3 Regimes are established, concerning ramp events: ramp-up, ramp-down and no-ramp regime. In order to assess the skillness of the proposed RS-ANN model, a single
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Event generation for next to leading order chargino production at the international linear collider
Energy Technology Data Exchange (ETDEWEB)
Robens, T.
2006-10-15
At the International Linear Collider (ILC), parameters of supersymmetry (SUSY) can be determined with an experimental accuracy matching the precision of next-to-leading order (NLO) and higher-order theoretical predictions. Therefore, these contributions need to be included in the analysis of the parameters. We present a Monte-Carlo event generator for simulating chargino pair production at the ILC at next-to-leading order in the electroweak couplings. We consider two approaches of including photon radiation. A strict fixed-order approach allows for comparison and consistency checks with published semianalytic results in the literature. A version with soft- and hard-collinear resummation of photon radiation, which combines photon resummation with the inclusion of the NLO matrix element for the production process, avoids negative event weights, so the program can simulate physical (unweighted) event samples. Photons are explicitly generated throughout the range where they can be experimentally resolved. In addition, it includes further higher-order corrections unaccounted for by the fixed-order method. Inspecting the dependence on the cutoffs separating the soft and collinear regions, we evaluate the systematic errors due to soft and collinear approximations for NLO and higher-order contributions. In the resummation approach, the residual uncertainty can be brought down to the per-mil level, coinciding with the expected statistical uncertainty at the ILC. We closely investigate the two-photon phase space for the resummation method. We present results for cross sections and event generation for both approaches. (orig.)
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Next-to-next-leading order correction to 3-jet rate and event-shape ...
Indian Academy of Sciences (India)
The coupling constant, , was measured by two different methods: first by employing the three-jet observables. Combining all the data, the value of as at next-to-next leading order (NNLO) was determined to be 0.117 ± 0.004(hard) ± 0.006(theo). Secondly, from the event-shape distributions, the strong coupling constant, ...
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A cell junction pathology of neural stem cells leads to abnormal neurogenesis and hydrocephalus
Directory of Open Access Journals (Sweden)
Esteban M Rodríguez
2012-01-01
Full Text Available Most cells of the developing mammalian brain derive from the ventricular (VZ and the subventricular (SVZ zones. The VZ is formed by the multipotent radial glia/neural stem cells (NSCs while the SVZ harbors the rapidly proliferative neural precursor cells (NPCs. Evidence from human and animal models indicates that the common history of hydrocephalus and brain maldevelopment starts early in embryonic life with disruption of the VZ and SVZ. We propose that a "cell junction pathology" involving adherent and gap junctions is a final common outcome of a wide range of gene mutations resulting in proteins abnormally expressed by the VZ cells undergoing disruption. Disruption of the VZ during fetal development implies the loss of NSCs whereas VZ disruption during the perinatal period implies the loss of ependyma. The process of disruption occurs in specific regions of the ventricular system and at specific stages of brain development. This explains why only certain brain structures have an abnormal development, which in turn results in a specific neurological impairment of the newborn. Disruption of the VZ of the Sylvian aqueduct (SA leads to aqueductal stenosis and hydrocephalus, while disruption of the VZ of telencephalon impairs neurogenesis. We are currently investigating whether grafting of NSCs/neurospheres from normal rats into the CSF of hydrocephalic mutants helps to diminish/repair the outcomes of VZ disruption.
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International Nuclear Information System (INIS)
Saint-Laurent, D.; St-Laurent, J.; Hahni, M.; Chapados, C.; Ghaleb, B.
2010-01-01
Soils contaminated with hydrocarbons (C10,C50), polycyclic aromatic hydrocarbons (PAHs), and other contaminants (e.g., As, Cd, Cu, Pb) were recently discovered on the banks of the Saint-Francois and Massawippi rivers. Alluvial soils are contaminated over a distance of 100 kilometers, and the level of the contaminated-hydrocarbon layer in the soil profiles is among the highest at the Windsor and Richmond sites. Concentrations of lead and stable lead isotope ratios ( 204 Pb/ 206 Pb, 206 Pb/ 207 Pb, 208 Pb/ 20 '6Pb) are also used to identify contamination events. The maximum and minimum values detected in soil profiles for arsenic, cadmium, and lead vary from 3.01 to 37.88?mg kg -1 (As), 0.11 to 0.81?mg kg-1 (Cd) 12.32 to 149.13?mg kg -1 (Pb), respectively, while the 207 Pb/ 206 Pb isotopic ratio values are between 0.8545 and 0.8724 for all the profiles. The highest values of trace elements (As, Pb and Zn) were detected in the hydrocarbon layer (C10,C50), most often located at the bottom of the profiles (160, 200, and 220 cm in depth). The various peaks recorded in the soils and the position of the profiles suggest that various contaminants were transported by the river on several occasions and infiltrated the soil matrix or deposited on flood plains during successive floods. Atmospheric particles which entered the river or deposited on riverbanks must also be considered as another source of pollution recorded in soils
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Directory of Open Access Journals (Sweden)
Diane Saint-Laurent
2010-01-01
Full Text Available Soils contaminated with hydrocarbons (C10–C50, polycyclic aromatic hydrocarbons (PAHs, and other contaminants (e.g., As, Cd, Cu, Pb were recently discovered on the banks of the Saint-François and Massawippi rivers. Alluvial soils are contaminated over a distance of 100 kilometers, and the level of the contaminated-hydrocarbon layer in the soil profiles is among the highest at the Windsor and Richmond sites. Concentrations of lead and stable lead isotope ratios (204Pb/206Pb, 207Pb/206Pb, 208Pb/206Pb are also used to identify contamination events. The maximum and minimum values detected in soil profiles for arsenic, cadmium, and lead vary from 3.01 to 37.88 mg kg-1 (As, 0.11 to 0.81 mg kg-1 (Cd 12.32 to 149.13 mg kg-1 (Pb, respectively, while the 207Pb/206Pb isotopic ratio values are between 0.8545 and 0.8724 for all the profiles. The highest values of trace elements (As, Pb and Zn were detected in the hydrocarbon layer (C10–C50, most often located at the bottom of the profiles (160, 200, and 220 cm in depth. The various peaks recorded in the soils and the position of the profiles suggest that various contaminants were transported by the river on several occasions and infiltrated the soil matrix or deposited on floodplains during successive floods. Atmospheric particles which entered the river or deposited on riverbanks must also be considered as another source of pollution recorded in soils.
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Neural electrical activity and neural network growth.
Gafarov, F M
2018-05-01
The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Artificial Neural Network applied to lightning flashes
Gin, R. B.; Guedes, D.; Bianchi, R.
2013-05-01
The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a
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Comparison of three jet events to predictions from a next-to-leading order calculation
Energy Technology Data Exchange (ETDEWEB)
Brandl, Alexander [Univ. of New Mexico, Albuquerque, NM (United States)
2002-01-01
The properties of three-jet events in data of integrated luminosity 86±4 pb-1 from CDF Run 1b and with total transverse energy greater than 175 GeV have been analyzed and compared to predictions from a next-to-leading order perturbative QCD calculation.
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Directory of Open Access Journals (Sweden)
Yangmei Luo
Full Text Available To investigate perceptual and neural correlates of future self-appraisals as a function of temporal distance, event-related potentials (ERPs were recorded while participants (11 women, eight men made judgments about the applicability of trait adjectives to their near future selves (i.e., one month from now and their distant future selves (i.e., three years from now. Behavioral results indicated people used fewer positive adjectives, more negative adjectives, recalled more specific events coming to mind and felt more psychologically connected to the near future self than the distant future self. Electrophysiological results demonstrated that negative trait adjectives elicited more positive ERP deflections than did positive trait adjectives in the interval between 550 and 800 ms (late positive component within the near future self condition. However, within the same interval, there were no significant differences between negative and positive traits adjectives in the distant future self condition. The results suggest that negative emotional processing in future self-appraisals is modulated by temporal distance, consistent with predictions of construal level theory.
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Praet, J.; SANTERMANS, Eva; Reekmans, K.; de Vocht, N.; Le Blon, D.; Hoornaert, C.; Daans, J.; Goossens, H.; Berneman, Z.; HENS, Niel; Van der Linden, A.; Ponsaerts, P.
2014-01-01
Preclinical animal studies involving intracerebral (stem) cell grafting are gaining popularity in many laboratories due to the reported beneficial effects of cell grafting on various diseases or traumata of the central nervous system (CNS). In this chapter, we describe a histological workflow to characterize and quantify cellular events following neural and fibroblast(-like) stem cell grafting in healthy and demyelinated CNS tissue. First, we provide standardized protocols to isolate and cult...
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Neural network tagging in a toy model
International Nuclear Information System (INIS)
Milek, Marko; Patel, Popat
1999-01-01
The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed
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Reconstruction of t anti tH (H → bb) events using deep neural networks with the CMS detector
Energy Technology Data Exchange (ETDEWEB)
Rieger, Marcel; Erdmann, Martin; Fischer, Benjamin; Fischer, Robert; Heidemann, Fabian; Quast, Thorben; Rath, Yannik [III. Physikalisches Institut A, RWTH Aachen University (Germany)
2016-07-01
The measurement of Higgs boson production in association with top-quark pairs (t anti tH) is an important goal of Run 2 of the LHC as it allows for a direct measurement of the underlying Yukawa coupling. Due to the complex final state, however, the analysis of semi-leptonic t anti tH events with the Higgs boson decaying into a pair of bottom-quarks is challenging. A promising method for tackling jet parton associations are Deep Neural Networks (DNN). While being a widely spread machine learning algorithm in modern industry, DNNs are on the way to becoming established in high energy physics. We present a study on the reconstruction of the final state using DNNs, comparing to Boosted Decision Trees (BDT) as benchmark scenario. This is accomplished by generating permutations of simulated events and comparing them with truth information to extract reconstruction efficiencies.
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DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM
Directory of Open Access Journals (Sweden)
Marília Gonçalves Dutra da Silva
2016-04-01
Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.
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Neural representations of emotion are organized around abstract event features.
Skerry, Amy E; Saxe, Rebecca
2015-08-03
Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Yu, Jingwen; Wu, Yanqing; Yang, Peixin
2016-05-01
Aberrant epigenetic modifications are implicated in maternal diabetes-induced neural tube defects (NTDs). Because cellular stress plays a causal role in diabetic embryopathy, we investigated the possible role of the stress-resistant sirtuin (SIRT) family histone deacetylases. Among the seven sirtuins (SIRT1-7), pre-gestational maternal diabetes in vivo or high glucose in vitro significantly reduced the expression of SIRT 2 and SIRT6 in the embryo or neural stem cells, respectively. The down-regulation of SIRT2 and SIRT6 was reversed by superoxide dismutase 1 (SOD1) over-expression in the in vivo mouse model of diabetic embryopathy and the SOD mimetic, tempol and cell permeable SOD, PEGSOD in neural stem cell cultures. 2,3-dimethoxy-1,4-naphthoquinone (DMNQ), a superoxide generating agent, mimicked high glucose-suppressed SIRT2 and SIRT6 expression. The acetylation of histone 3 at lysine residues 56 (H3K56), H3K14, H3K9, and H3K27, putative substrates of SIRT2 and SIRT6, was increased by maternal diabetes in vivo or high glucose in vitro, and these increases were blocked by SOD1 over-expression or tempol treatment. SIRT2 or SIRT6 over-expression abrogated high glucose-suppressed SIRT2 or SIRT6 expression, and prevented the increase in acetylation of their histone substrates. The potent sirtuin activator (SRT1720) blocked high glucose-increased histone acetylation and NTD formation, whereas the combination of a pharmacological SIRT2 inhibitor and a pan SIRT inhibitor mimicked the effect of high glucose on increased histone acetylation and NTD induction. Thus, diabetes in vivo or high glucose in vitro suppresses SIRT2 and SIRT6 expression through oxidative stress, and sirtuin down-regulation-induced histone acetylation may be involved in diabetes-induced NTDs. The mechanism underlying pre-gestational diabetes-induced neural tube defects (NTDs) is still elusive. Our study unravels a new epigenetic mechanism in which maternal diabetes-induced oxidative stress represses
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Neural Global Pattern Similarity Underlies True and False Memories.
Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui
2016-06-22
The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.
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Pirinen, Jani; Putaala, Jukka; Aarnio, Karoliina; Aro, Aapo L; Sinisalo, Juha; Kaste, Markku; Haapaniemi, Elena; Tatlisumak, Turgut; Lehto, Mika
2016-11-01
Ischemic stroke (IS) in a young patient is a disaster and recurrent cardiovascular events could add further impairment. Identifying patients with high risk of such events is therefore important. The prognostic relevance of ECG for this population is unknown. A total of 690 IS patients aged 15-49 years were included. A 12-lead ECG was obtained 1-14 d after the onset of stroke. We adjusted for demographic factors, comorbidities, and stroke characteristics, Cox regression models were used to identify independent ECG parameters associated with long-term risks of (1) any cardiovascular event, (2) cardiac events, and (3) recurrent stroke. Median follow-up time was 8.8 years. About 26.4% of patients experienced a cardiovascular event, 14.5% had cardiac events, and 14.6% recurrent strokes. ECG parameters associated with recurrent cardiovascular events were bundle branch blocks, P-terminal force, left ventricular hypertrophy, and a broader QRS complex. Furthermore, more leftward P-wave axis, prolonged QTc, and P-wave duration >120 ms were associated with increased risks of cardiac events. No ECG parameters were independently associated with recurrent stroke. A 12-lead ECG can be used for risk prediction of cardiovascular events but not for recurrent stroke in young IS patients. KEY MESSAGES ECG is an easy, inexpensive, and useful tool for identifying young ischemic stroke patients with a high risk for recurrent cardiovascular events and it has a statistically significant association with these events even after adjusting for confounding factors. Bundle branch blocks, P-terminal force, broader QRS complex, LVH according to Cornell voltage duration criteria, more leftward P-wave axis, prolonged QTc, and P-wave duration >120 ms are predictors for future cardiovascular or cardiac events in these patients. No ECG parameters were independently associated with recurrent stroke.
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Engaging narratives evoke similar neural activity and lead to similar time perception.
Cohen, Samantha S; Henin, Simon; Parra, Lucas C
2017-07-04
It is said that we lose track of time - that "time flies" - when we are engrossed in a story. How does engagement with the story cause this distorted perception of time, and what are its neural correlates? People commit both time and attentional resources to an engaging stimulus. For narrative videos, attentional engagement can be represented as the level of similarity between the electroencephalographic responses of different viewers. Here we show that this measure of neural engagement predicted the duration of time that viewers were willing to commit to narrative videos. Contrary to popular wisdom, engagement did not distort the average perception of time duration. Rather, more similar brain responses resulted in a more uniform perception of time across viewers. These findings suggest that by capturing the attention of an audience, narrative videos bring both neural processing and the subjective perception of time into synchrony.
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Lack of beta1 integrins in enteric neural crest cells leads to a Hirschsprung-like phenotype
DEFF Research Database (Denmark)
Breau, Marie A; Pietri, Thomas; Eder, Olivier
2006-01-01
The enteric nervous system arises mainly from vagal and sacral neural crest cells that colonise the gut between 9.5 and 14 days of development in mice. Using the Cre-LoxP system, we removed beta1 integrins in the neural crest cells when they emerge from the neural tube. beta1-null enteric neural...
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Directory of Open Access Journals (Sweden)
Alejandro eGalvao-Carmona
2014-10-01
Full Text Available Background. The study of the attentional system remains a challenge for current neuroscience. The Attention Network Test (ANT was designed to study simultaneously three different attentional networks (alerting, orienting and executive based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event Related Potentials (ERPs and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioural measures. Results. This study shows that there is a basic level of alerting (tonic alerting in the no cue condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the no cue condition; a late modulation triggered by the central cue condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions. The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human
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Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding.
Packard, Pau A; Rodríguez-Fornells, Antoni; Bunzeck, Nico; Nicolás, Berta; de Diego-Balaguer, Ruth; Fuentemilla, Lluís
2017-01-11
As the stream of experience unfolds, our memory system rapidly transforms current inputs into long-lasting meaningful memories. A putative neural mechanism that strongly influences how input elements are transformed into meaningful memory codes relies on the ability to integrate them with existing structures of knowledge or schemas. However, it is not yet clear whether schema-related integration neural mechanisms occur during online encoding. In the current investigation, we examined the encoding-dependent nature of this phenomenon in humans. We showed that actively integrating words with congruent semantic information provided by a category cue enhances memory for words and increases false recall. The memory effect of such active integration with congruent information was robust, even with an interference task occurring right after each encoding word list. In addition, via electroencephalography, we show in 2 separate studies that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. That the neural signals of successful encoding of congruent and incongruent information followed similarly ∼200 ms later suggests that this earlier neural response contributed to memory formation. We propose that the encoding of events that are congruent with readily available contextual semantics can trigger an accelerated onset of the neural mechanisms, supporting the integration of semantic information with the event input. This faster onset would result in a long-lasting and meaningful memory trace for the event but, at the same time, make it difficult to distinguish it from plausible but never encoded events (i.e., related false memories). Conceptual or schema congruence has a strong influence on long-term memory. However, the question of whether schema-related integration neural mechanisms occur during online encoding has yet to be clarified. We investigated the neural mechanisms reflecting how the active
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Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed
2017-11-01
The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.
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Molecular events leading to HPV-induced high grade neoplasia
Directory of Open Access Journals (Sweden)
Saskia M. Wilting
2016-12-01
Full Text Available Cervical cancer is initiated by high-risk types of the human papillomavirus (hrHPV and develops via precursor stages, called cervical intraepithelial neoplasia (CIN. High-grade CIN lesions are considered true precancerous lesions when the viral oncogenes E6 and E7 are aberrantly expressed in the dividing cells. This results in abolishment of normal cell cycle control via p53 and pRb degradation. However, it has become clear that these viral oncogenes possess additional oncogenic properties, including interference with the DNA methylation machinery and mitotic checkpoints. Identification of the resulting molecular events leading to high-grade neoplasia will 1 increase our understanding of cervical carcinogenesis, 2 yield biomarkers for early diagnosis, and 3 identify therapeutic targets for HPV-induced (pre cancerous lesions.This review will briefly summarise current advances in our understanding of the molecular alterations in the host cell genome that occur during HPV-induced carcinogenesis.
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International Nuclear Information System (INIS)
Ensafi, Ali A.; Khayamian, T.; Benvidi, A.; Mirmomtaz, E.
2006-01-01
In this work, simultaneous determination of two groups of elements consisting of Pb(II)-Cd(II) and Cu(II)-Pb(II)-Cd(II) using adsorptive cathodic stripping voltammetry are described. The method is based on accumulation of these metal ions on mercury electrode using xylenol orange as a suitable complexing agent. The potential was scanned to the negative direction and the differential pulse stripping voltammograms were recorded. The instrumental and chemical factors were optimized using artificial neural network. The optimized conditions were obtained in pH of 5.5, xylenol orange concentration of 4.0 μM, accumulation potential of -0.50 V, accumulation time of 30 s, scan rate of 10 mV/s and pulse height of 70 mV. The relationship between the peak current versus concentration was linear over the range of 5.0-150.0 ng ml -1 for cadmium and 5.0-150.0 ng ml -1 for lead. The limits of detection were 0.98 and 1.18 ng ml -1 for lead and cadmium ions, respectively. In simultaneous determination of Cu(II), Pb(II) and Cd(II) there are inter-metallic interactions, which result a non-linear relationship between the peak current and the ionic concentration for each of the element. Therefore, an artificial neural network was used as the multivariate calibration method. The ANN was constructed with three neurons as the output layer for the simultaneous determination of the three elements. The constructed model was able to predict the concentration of the elements in the ranges of 1.0-50.0, 5.0-200.0 and 10.0-200.0 ng ml -1 , for Cu(II), Pb(II) and Cd(II), respectively
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Measurement of the top-quark mass with dilepton events selected using neuroevolution at CDF.
Aaltonen, T; Adelman, J; Akimoto, T; Albrow, M G; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Bednar, P; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Copic, K; Cordelli, M; Cortiana, G; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Koay, S A; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kusakabe, Y; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, S W; Leone, S; Lewis, J D; Lin, C S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lu, R-S; Lucchesi, D; Lueck, J; Luci, C; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlok, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shears, T; Shekhar, R; Shepard, P F; Sherman, D; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner-Kuhr, J; Wagner, W; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Whiteson, S; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zheng, Y; Zucchelli, S
2009-04-17
We report a measurement of the top-quark mass M_{t} in the dilepton decay channel tt[over ] --> bl;{'+} nu_{l};{'}b[over ]l;{-}nu[over ]_{l}. Events are selected with a neural network which has been directly optimized for statistical precision in top-quark mass using neuroevolution, a technique modeled on biological evolution. The top-quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0 fb;{-1} of pp[over ] collisions collected with the CDF II detector, yielding a measurement of M_{t} = 171.2 +/- 2.7(stat) +/- 2.9(syst) GeV / c;{2}.
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Measurement of the Top-Quark Mass with Dilepton Events Selected Using Neuroevolution at CDF
International Nuclear Information System (INIS)
Aaltonen, T.; Maki, T.; Mehtala, P.; Orava, R.; Osterberg, K.; Saarikko, H.; Remortel, N. van; Adelman, J.; Brubaker, E.; Fedorko, W. T.; Grosso-Pilcher, C.; Kim, Y. K.; Krop, D.; Kwang, S.; Paramonov, A. A.; Schmidt, M. A.; Shiraishi, S.; Shochet, M.; Wolfe, C.; Yang, U. K.
2009-01-01
We report a measurement of the top-quark mass M t in the dilepton decay channel tt→bl '+ ν l ' bl - ν l . Events are selected with a neural network which has been directly optimized for statistical precision in top-quark mass using neuroevolution, a technique modeled on biological evolution. The top-quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0 fb -1 of pp collisions collected with the CDF II detector, yielding a measurement of M t =171.2±2.7(stat)±2.9(syst) GeV/c 2
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Urata, Yuko; Yamashita, Wataru; Inoue, Takeshi; Agata, Kiyokazu
2018-06-14
Adult newts can regenerate large parts of their brain from adult neural stem cells (NSCs), but how adult NSCs reorganize brain structures during regeneration remains unclear. In development, elaborate brain structures are produced under broadly coordinated regulations of embryonic NSCs in the neural tube, whereas brain regeneration entails exquisite control of the reestablishment of certain brain parts, suggesting a yet-unknown mechanism directs NSCs upon partial brain excision. Here we report that upon one-quarter excision of the adult newt ( Pleurodeles waltl ) mesencephalon, active participation of local NSCs around specific brain subregions' boundaries leads to some imperfect and some perfect brain regeneration along an individual's rostrocaudal axis. Regeneration phenotypes depend on how the wound closing occurs using local NSCs, and perfect regeneration replicates development-like processes but takes more than one year. Our findings indicate that newt brain regeneration is supported by modularity of boundary-domain NSCs with self-organizing ability in neighboring fields. © 2018. Published by The Company of Biologists Ltd.
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Selection of Photon Gluon Fusion Events in DIS
International Nuclear Information System (INIS)
Kowalik, K.; Rondio, E.; Sulej, R.; Zaremba, K.
2001-01-01
A selection of the Photon Gluon Fusion (PGF) process with light quarks for deep inelastic scattering events is presented. This process is directly sensitive to gluon polarization and our goal is to find out the most effective selection on a sample of events simulated for the SMC experiment. We compare two general multi-class classification methods - Bayes method and neural network with a conventional selection procedure. The neural network algorithm presented here is a modification of method belonging to the family of directional minimization algorithms. This method is convenient and effective for photon gluon fusion selection and determination of gluon polarization. Finally we present the estimation for precision of gluon polarization for neural network method. (author)
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A convolutional neural network neutrino event classifier
International Nuclear Information System (INIS)
Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.
2016-01-01
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.
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Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N
2018-05-16
Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.
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NFIX Regulates Neural Progenitor Cell Differentiation During Hippocampal Morphogenesis
Heng, Yee Hsieh Evelyn; McLeay, Robert C.; Harvey, Tracey J.; Smith, Aaron G.; Barry, Guy; Cato, Kathleen; Plachez, Céline; Little, Erica; Mason, Sharon; Dixon, Chantelle; Gronostajski, Richard M.; Bailey, Timothy L.; Richards, Linda J.; Piper, Michael
2014-01-01
Neural progenitor cells have the ability to give rise to neurons and glia in the embryonic, postnatal and adult brain. During development, the program regulating whether these cells divide and self-renew or exit the cell cycle and differentiate is tightly controlled, and imbalances to the normal trajectory of this process can lead to severe functional consequences. However, our understanding of the molecular regulation of these fundamental events remains limited. Moreover, processes underpinning development of the postnatal neurogenic niches within the cortex remain poorly defined. Here, we demonstrate that Nuclear factor one X (NFIX) is expressed by neural progenitor cells within the embryonic hippocampus, and that progenitor cell differentiation is delayed within Nfix−/− mice. Moreover, we reveal that the morphology of the dentate gyrus in postnatal Nfix−/− mice is abnormal, with fewer subgranular zone neural progenitor cells being generated in the absence of this transcription factor. Mechanistically, we demonstrate that the progenitor cell maintenance factor Sry-related HMG box 9 (SOX9) is upregulated in the hippocampus of Nfix−/− mice and demonstrate that NFIX can repress Sox9 promoter-driven transcription. Collectively, our findings demonstrate that NFIX plays a central role in hippocampal morphogenesis, regulating the formation of neuronal and glial populations within this structure. PMID:23042739
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Maternal exposure to arsenic, cadmium, lead, and mercury and neural tube defects in offspring
International Nuclear Information System (INIS)
Brender, Jean D.; Suarez, Lucina; Felkner, Marilyn; Gilani, Zunera; Stinchcomb, David; Moody, Karen; Henry, Judy; Hendricks, Katherine
2006-01-01
Arsenic, cadmium, lead, and mercury are neurotoxins, and some studies suggest that these elements might also be teratogens. Using a case-control study design, we investigated the relation between exposure to these heavy metals and neural tube defects (NTDs) in offspring of Mexican-American women living in 1 of the 14 Texas counties bordering Mexico. A total of 184 case-women with NTD-affected pregnancies and 225 control-women with normal live births were interviewed about their environmental and occupational exposures during the periconceptional period. Biologic samples for blood lead and urinary arsenic, cadmium, and mercury were also obtained for a subset of these women. Overall, the median levels of these biomarkers for heavy metal exposure did not differ significantly (P>0.05) between case- and control-women. However, among women in the highest income group, case-women were nine times more likely (95% confidence interval (CI) 1.4-57) than control-women to have a urinary mercury >=5.62μg/L. Case-women were 4.2 times more likely (95% CI 1.1-16) to report burning treated wood during the periconceptional period than control-women. Elevated odds ratios (ORs) were observed for maternal and paternal occupational exposures to arsenic and mercury, but the 95% CIs were consistent with unity. The 95% CIs of the ORs were also consistent with unity for higher levels of arsenic, cadmium, lead, and mercury in drinking water and among women who lived within 2 miles at the time of conception to industrial facilities with reported emissions of any of these heavy metals. Our findings suggest that maternal exposures to arsenic, cadmium, or lead are probably not significant risk factors for NTDs in offspring. However, the elevated urinary mercury levels found in this population and exposures to the combustion of treated wood may warrant further investigation
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Directory of Open Access Journals (Sweden)
Rasoul Sali
2013-01-01
Full Text Available Objectives. Stressors have a serious role in precipitating mental and somatic disorders and are an interesting subject for many clinical and community-based studies. Hence, the proper and accurate measurement of them is very important. We revised the stressful life event (SLE questionnaire by adding weights to the events in order to measure and determine a cut point. Methods. A total of 4569 adults aged between 18 and 85 years completed the SLE questionnaire and the general health questionnaire-12 (GHQ-12. A hybrid model of genetic algorithm (GA and artificial neural networks (ANNs was applied to extract the relation between the stressful life events (evaluated by a 6-point Likert scale and the GHQ score as a response variable. In this model, GA is used in order to set some parameter of ANN for achieving more accurate results. Results. For each stressful life event, the number is defined as weight. Among all stressful life events, death of parents, spouse, or siblings is the most important and impactful stressor in the studied population. Sensitivity of 83% and specificity of 81% were obtained for the cut point 100. Conclusion. The SLE-revised (SLE-R questionnaire despite simplicity is a high-performance screening tool for investigating the stress level of life events and its management in both community and primary care settings. The SLE-R questionnaire is user-friendly and easy to be self-administered. This questionnaire allows the individuals to be aware of their own health status.
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Impact parameter determination in experimental analysis using neural network
International Nuclear Information System (INIS)
Haddad, F.; David, C.; Freslier, M.; Aichelin, J.; Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.
1997-01-01
A neural network is used to determine the impact parameter in 40 Ca + 40 Ca reactions. The effect of the detection efficiency as well as the model dependence of the training procedure have been studied carefully. An overall improvement of the impact parameter determination of 25 % is obtained using this technique. The analysis of Amphora 40 Ca+ 40 Ca data at 35 MeV per nucleon using a neural network shows two well separated classes of events among the selected 'complete' events. (authors)
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A cell junction pathology of neural stem cells leads to abnormal neurogenesis and hydrocephalus
Rodríguez, Esteban M; Guerra, María M; Vío, Karin; González, César; Ortloff, Alexander; Bátiz, Luis F; Rodríguez, Sara; Jara, María C; Muñoz, Rosa I; Ortega, Eduardo; Jaque, Jaime; Guerra, Francisco; Sival, Deborah A; den Dunnen, Wilfred F A; Jiménez, Antonio J; Domínguez-Pinos, María D; Pérez-Fígares, José M; McAllister, James P; Johanson, Conrad
2012-01-01
Most cells of the developing mammalian brain derive from the ventricular (VZ) and the subventricular (SVZ) zones. The VZ is formed by the multipotent radial glia/neural stem cells (NSCs) while the SVZ harbors the rapidly proliferative neural precursor cells (NPCs). Evidence from human and animal
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International Nuclear Information System (INIS)
Martini, Till; Uwer, Peter
2015-01-01
In this article we illustrate how event weights for jet events can be calculated efficiently at next-to-leading order (NLO) accuracy in QCD. This is a crucial prerequisite for the application of the Matrix Element Method in NLO. We modify the recombination procedure used in jet algorithms, to allow a factorisation of the phase space for the real corrections into resolved and unresolved regions. Using an appropriate infrared regulator the latter can be integrated numerically. As illustration, we reproduce differential distributions at NLO for two sample processes. As further application and proof of concept, we apply the Matrix Element Method in NLO accuracy to the mass determination of top quarks produced in e"+e"− annihilation. This analysis is relevant for a future Linear Collider. We observe a significant shift in the extracted mass depending on whether the Matrix Element Method is used in leading or next-to-leading order.
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Event generators for address event representation transmitters
Serrano-Gotarredona, Rafael; Serrano-Gotarredona, Teresa; Linares Barranco, Bernabe
2005-06-01
Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. In a typical AER transmitter chip, there is an array of neurons that generate events. They send events to a peripheral circuitry (let's call it "AER Generator") that transforms those events to neurons coordinates (addresses) which are put sequentially on an interchip high speed digital bus. This bus includes a parallel multi-bit address word plus a Rqst (request) and Ack (acknowledge) handshaking signals for asynchronous data exchange. There have been two main approaches published in the literature for implementing such "AER Generator" circuits. They differ on the way of handling event collisions coming from the array of neurons. One approach is based on detecting and discarding collisions, while the other incorporates arbitration for sequencing colliding events . The first approach is supposed to be simpler and faster, while the second is able to handle much higher event traffic. In this article we will concentrate on the second arbiter-based approach. Boahen has been publishing several techniques for implementing and improving the arbiter based approach. Originally, he proposed an arbitration squeme by rows, followed by a column arbitration. In this scheme, while one neuron was selected by the arbiters to transmit his event out of the chip, the rest of neurons in the array were
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QCD-Aware Neural Networks for Jet Physics
CERN. Geneva
2017-01-01
Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence. Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based neural network topology varies on an event-by-event basis. Our experiments highlight the flexibility of our method for building task-specific jet embeddings and show that recursive architectures are significantly more accurate and data efficient than previous image-based networks. We extend the analogy from individual jets (sentences) to full events (paragraphs), and show for the first time an event-level classifier operating...
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International Nuclear Information System (INIS)
Zio, Enrico; Pedroni, Nicola; Broggi, Matteo; Golea, Lucia Roxana
2009-01-01
In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships
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Kuprash, Oleg; The ATLAS collaboration
2017-01-01
A correct modelling of the underlying event in proton-proton collisions is important for the proper simulation of kinematic distributions of high-energy collisions. The ATLAS collaboration extended previous studies at 7 TeV with a leading track or jet or Z boson by a new study at 13 TeV, measuring the number and transverse-momentum sum of charged particles as a function of pseudorapidity and azimuthal angle in dependence of the reconstructed leading track. These measurements are sensitive to the underlying-event as well as the onset of hard emissions. The results are compared to predictions of several MC generators. + Inclusive four-jet events produced in proton--proton collisions at a center-of-mass energy of 7 TeV have been analyzed for the presence of hard double parton scattering collected with the ATLAS detector. The contribution of hard double parton scattering to the production of four-jet events has been extracted using an artificial neural network. The assumption was made that hard double parton scat...
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Dynamics of neural cryptography.
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
2007-05-01
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.
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Dynamics of neural cryptography
International Nuclear Information System (INIS)
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
2007-01-01
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible
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Dynamics of neural cryptography
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
2007-05-01
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.
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Measurement of the top quark mass with dilepton events selected using neuroevolution at CDF
International Nuclear Information System (INIS)
Aaltonen, T.; Helsinki Inst. of Phys.; Adelman, J.; Chicago U., EFI; Akimoto, T.; Tsukuba U.; Albrow, M.G.; Fermilab; Alvarez Gonzalez, B.; Cantabria U., Santander; Amerio, S.; Padua U.; Amidei, D.; Michigan U.; Anastassov, A.; Northwestern U.; Annovi, A.; Frascati; Antos, J.; Comenius U.; Apollinari, G.
2008-01-01
We report a measurement of the top quark mass M t in the dilepton decay channel t(bar t) to b(ell)(prime) + ν(prime) # ell# (bar b)(ell) - (bar ν) # ell#. Events are selected with a neural network which has been directly optimized for statistical precision in top quark mass using neuroevolution, a technique modeled on biological evolution. The top quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0 fb -1 of p(bar p) collisions collected with the CDF II detector, yielding a measurement of M t = 171.2 ± 2.7(stat.) ± 2.9(syst.) GeV/c 2
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Energy Technology Data Exchange (ETDEWEB)
Martini, Till; Uwer, Peter [Humboldt-Universität zu Berlin, Institut für Physik,Newtonstraße 15, 12489 Berlin (Germany)
2015-09-14
In this article we illustrate how event weights for jet events can be calculated efficiently at next-to-leading order (NLO) accuracy in QCD. This is a crucial prerequisite for the application of the Matrix Element Method in NLO. We modify the recombination procedure used in jet algorithms, to allow a factorisation of the phase space for the real corrections into resolved and unresolved regions. Using an appropriate infrared regulator the latter can be integrated numerically. As illustration, we reproduce differential distributions at NLO for two sample processes. As further application and proof of concept, we apply the Matrix Element Method in NLO accuracy to the mass determination of top quarks produced in e{sup +}e{sup −} annihilation. This analysis is relevant for a future Linear Collider. We observe a significant shift in the extracted mass depending on whether the Matrix Element Method is used in leading or next-to-leading order.
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Event Boundaries in Memory and Cognition.
Radvansky, Gabriel A; Zacks, Jeffrey M
2017-10-01
Research on event cognition is rapidly developing and is revealing fundamental aspects of human cognition. In this paper, we review recent and current work that is driving this field forward. We first outline the Event Horizon Model, which broadly describes the impact of event boundaries on cognition and memory. Then, we address recent work on event segmentation, the role of event cognition in working memory and long-term memory, including event model updating, and long term retention. Throughout we also consider how event cognition varies across individuals and groups of people and consider the neural mechanisms involved.
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Anomaly detection in an automated safeguards system using neural networks
International Nuclear Information System (INIS)
Whiteson, R.; Howell, J.A.
1992-01-01
An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs
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Neural fields theory and applications
Graben, Peter; Potthast, Roland; Wright, James
2014-01-01
With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...
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Rehmert, Andrea E.; Kisley, Michael A.
2013-01-01
Older adults have demonstrated an avoidance of negative information presumably with a goal of greater emotional satisfaction. Understanding whether avoidance of negative information is a voluntary, motivated choice, or an involuntary, automatic response will be important to differentiate, as decision-making often involves emotional factors. With the use of an emotional framing event-related potential (ERP) paradigm, the present study investigated whether older adults could alter neural respon...
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International Nuclear Information System (INIS)
Proriol, J.
1994-01-01
Five different methods are compared for selecting the most important variables with a view to classifying high energy physics events with neural networks. The different methods are: the F-test, Principal Component Analysis (PCA), a decision tree method: CART, weight evaluation, and Optimal Cell Damage (OCD). The neural networks use the variables selected with the different methods. We compare the percentages of events properly classified by each neural network. The learning set and the test set are the same for all the neural networks. (author)
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Impact parameter determination for 40Ca + 40Ca reactions using a neural network
International Nuclear Information System (INIS)
Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.
1995-01-01
A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs
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Well-being and Anticipation for Future Positive Events: Evidences from an fMRI Study.
Luo, Yangmei; Chen, Xuhai; Qi, Senqing; You, Xuqun; Huang, Xiting
2017-01-01
Anticipation for future confers great benefits to human well-being and mental health. However, previous work focus on how people's well-being correlate with brain activities during perception of emotional stimuli, rather than anticipation for the future events. Here, the current study investigated how well-being relates to neural circuitry underlying the anticipating process of future desired events. Using event-related functional magnetic resonance imaging, 40 participants were scanned while they were performing an emotion anticipation task, in which they were instructed to anticipate the positive or neutral events. The results showed that bilateral medial prefrontal cortex (MPFC) were activated during anticipation for positive events relative to neutral events, and the enhanced brain activation in MPFC was associated with higher level of well-being. The findings suggest a neural mechanism by which the anticipation process to future desired events correlates to human well-being, which provide a future-oriented view on the neural sources of well-being.
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The neural cell adhesion molecule
DEFF Research Database (Denmark)
Berezin, V; Bock, E; Poulsen, F M
2000-01-01
During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...
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The use of neural networks in the D0 data acquisition system
International Nuclear Information System (INIS)
Cutts, D.; Hoftun, J.S.; Sornborger, A.; Astur, R.V.; Johnson, C.R.; Zeller, R.T.
1989-01-01
We discuss the possible application of algorithms derived from neural networks to the D0 experiment. The D0 data acquisition system is based on a large farm of MicroVAXes, each independently performing real-time event filtering. A new generation of multiport memories in each MicroVAX node will enable special function processors to have direct access to event data. We describe an exploratory study of back propagation neural networks, such as might be configured in the nodes, for more efficient event filtering. 9 refs., 3 figs., 1 tab
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Neural networks, D0, and the SSC
International Nuclear Information System (INIS)
Barter, C.; Cutts, D.; Hoftun, J.S.; Partridge, R.A.; Sornborger, A.T.; Johnson, C.T.; Zeller, R.T.
1989-01-01
We outline several exploratory studies involving neural network simulations applied to pattern recognition in high energy physics. We describe the D0 data acquisition system and a natual means by which algorithms derived from neural networks techniques may be incorporated into recently developed hardware associated with the D0 MicroVAX farm nodes. Such applications to the event filtering needed by SSC detectors look interesting. 10 refs., 11 figs
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Combination cell therapy with mesenchymal stem cells and neural stem cells for brain stroke in rats.
Hosseini, Seyed Mojtaba; Farahmandnia, Mohammad; Razi, Zahra; Delavari, Somayeh; Shakibajahromi, Benafsheh; Sarvestani, Fatemeh Sabet; Kazemi, Sepehr; Semsar, Maryam
2015-05-01
Brain stroke is the second most important events that lead to disability and morbidity these days. Although, stroke is important, there is no treatment for curing this problem. Nowadays, cell therapy has opened a new window for treating central nervous system disease. In some previous studies the Mesenchymal stem cells and neural stem cells. In this study, we have designed an experiment to assess the combination cell therapy (Mesenchymal and Neural stem cells) effects on brain stroke. The Mesenchymal stem cells were isolated from adult rat bone marrow and the neural stem cells were isolated from ganglion eminence of rat embryo 14 days. The Mesenchymal stem cells were injected 1 day after middle cerebral artery occlusion (MCAO) and the neural stem cells transplanted 7 day after MCAO. After 28 days, the neurological outcomes and brain lesion volumes were evaluated. Also, the activity of Caspase 3 was assessed in different groups. The group which received combination cell therapy had better neurological examination and less brain lesion. Also the combination cell therapy group had the least Caspase 3 activity among the groups. The combination cell therapy is more effective than Mesenchymal stem cell therapy and neural stem cell therapy separately in treating the brain stroke in rats.
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Events leading to foreign material being left in the primary heat transport system
International Nuclear Information System (INIS)
Groom, S.H.; Benton, A.J.
1996-01-01
On October 6,1995, following an extensive maintenance outage which had included boiler primary side cleaning, a Primary Heat Transport (PHT) system pump run was started in preparation for ultrasonic feeder flow measurements. Wooden debris in the system resulted in failure of the shaft seals of the PHT Pump 1. The subsequent investigation and assessment of this event provided an understanding of both the pump shaft failure mechanism and the origin of the debris in the PHT system. The pump shaft failed as a result of friction-generated heat resulting from contact between the rotating shaft and the stationary seal housing. This contact was initiated by mechanical and hydraulic imbalance in the pump impeller caused by wooden debris lodged in the impeller. The origin of the wooden debris was a temporary plywood cover which was inadvertently left in a boiler following maintenance. This cover moved from the boiler to the pump impeller when the PHT pumps were started. The cover was not accounted for and verified as being removed prior to boiler closure, although a visual inspection was conducted. A detailed institutional process for component accounting and verification of removal of materials did not exist at the time of this event. Details of the methods used to establish alternative heat sinks, provide debris recovery facilities and to assess the fitness for duty of the heat transport system and fuel channels prior to reactor startup are discussed in detail elsewhere. This report will concentrate on the events leading up to and following the events which ultimately resulted in failure of the PHT pump shaft
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Directory of Open Access Journals (Sweden)
Aysha Basharat
2018-04-01
Full Text Available Multisensory integration is required for a number of daily living tasks where the inability to accurately identify simultaneity and temporality of multisensory events results in errors in judgment leading to poor decision-making and dangerous behavior. Previously, our lab discovered that older adults exhibited impaired timing of audiovisual events, particularly when making temporal order judgments (TOJs. Simultaneity judgments (SJs, however, were preserved across the lifespan. Here, we investigate the difference between the TOJ and SJ tasks in younger and older adults to assess neural processing differences between these two tasks and across the lifespan. Event-related potentials (ERPs were studied to determine between-task and between-age differences. Results revealed task specific differences in perceiving simultaneity and temporal order, suggesting that each task may be subserved via different neural mechanisms. Here, auditory N1 and visual P1 ERP amplitudes confirmed that unisensory processing of audiovisual stimuli did not differ between the two tasks within both younger and older groups, indicating that performance differences between tasks arise either from multisensory integration or higher-level decision-making. Compared to younger adults, older adults showed a sustained higher auditory N1 ERP amplitude response across SOAs, suggestive of broader response properties from an extended temporal binding window. Our work provides compelling evidence that different neural mechanisms subserve the SJ and TOJ tasks and that simultaneity and temporal order perception are coded differently and change with age.
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Shared memories reveal shared structure in neural activity across individuals
Chen, J.; Leong, Y.C.; Honey, C.J.; Yong, C.H.; Norman, K.A.; Hasson, U.
2016-01-01
Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a fifty-minute movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to forty minutes. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar between people, suggesting consistent spatial organization. In many high-order areas, patterns were more similar between people recalling the same event than between recall and perception, indicating systematic reshaping of percept into memory. These results reveal the existence of a common spatial organization for memories in high-level cortical areas, where encoded information is largely abstracted beyond sensory constraints; and that neural patterns during perception are altered systematically across people into shared memory representations for real-life events. PMID:27918531
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Baym, Carol L; Gonsalves, Brian D
2010-09-01
False memories can occur when people are exposed to misinformation about a past event. Of interest here are the neural mechanisms of this type of memory failure. In the present study, participants viewed photographic vignettes of common activities during an original event phase (OEP), while we monitored their brain activity using fMRI. Later, in a misinformation phase, participants viewed sentences describing the studied photographs, some of which contained information conflicting with that depicted in the photographs. One day later, participants returned for a surprise item memory recognition test for the content of the photographs. Results showed reliable creation of false memories, in that participants reported information that had been presented in the verbal misinformation but not in the photographs. Several regions were more active during the OEP for later accurate memory than for forgetting, but they were also more active for later false memories, indicating that false memories in this paradigm are not simply caused by failure to encode the original event. There was greater activation in the ventral visual stream for subsequent true memories than for subsequent false memories, however, suggesting that differences in encoding may contribute to later susceptibility to misinformation.
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Directory of Open Access Journals (Sweden)
Schwindling Jerome
2010-04-01
Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.
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Measurement of dijet cross sections for events with a leading neutron in photoproduction at HERA
International Nuclear Information System (INIS)
Breitweg, J.; Chekanov, S.; Derrick, M.; Krakauer, D.; Magill, S.; Musgrave, B.; Pellegrino, A.; Repond, J.; Stanek, R.; Yoshida, R.; Mattingly, M.C.K.; Antonioli, P.; Bari, G.; Basile, M.; Bellagamba, L.; Boscherini, D.; Bruni, A.; Bruni, G.; Cara Romeo, G.; Cifarelli, L.; Cindolo, F.; Contin, A.; Corradi, M.; De Pasquale, S.; Giusti, P.; Iacobucci, G.; Levi, G.; Margotti, A.; Massam, T.; Nania, R.; Palmonari, F.; Pesci, A.; Sartorelli, G.; Zichichi, A.; Amelung, C.; Bornheim, A.; Brock, I.; Coboeken, K.; Crittenden, J.; Deffner, R.; Hartmann, H.; Heinloth, K.; Hilger, E.; Irrgang, P.; Jakob, H.-P.; Kappes, A.; Katz, U.F.; Kerger, R.; Paul, E.; Rautenberg, J.; Schnurbusch, H.; Stifutkin, A.; Tandler, J.; Voss, K.C.; Weber, A.; Wieber, H.; Bailey, D.S.; Barret, O.; Brook, N.H.; Foster, B.; Heath, G.P.; Heath, H.F.; Rodrigues, E.; Scott, J.; Tapper, R.J.; Capua, M.; Schioppa, M.; Susinno, G.; Jeoung, H.Y.; Kim, J.Y.; Lee, J.H.; Lim, I.T.; Ma, K.J.; Pac, M.Y.; Caldwell, A.; Liu, W.; Liu, X.; Mellado, B.; Paganis, S.; Sampson, S.; Schmidke, W.B.; Sciulli, F.; Chwastowski, J.; Eskreys, A.; Figiel, J.; Klimek, K.; Olkiewicz, K.; Piotrzkowski, K.; Przybycien, M.B.; Stopa, P.; Zawiejski, L.; Bednarek, B.; Jelen, K.; Kisielewska, D.; Kowal, A.M.; Kowalski, T.; Przybycien, M.; Rulikowska-Zarebska, E.; Suszycki, L.; Szuba, D.; Kotanski, A.; Bauerdick, L.A.T.; Behrens, U.; Bienlein, J.K.; Borras, K.; Chiochia, V.; Dannheim, D.; Desler, K.; Drews, G.; Fox-Murphy, A.; Fricke, U.; Goebel, F.; Goers, S.; Goettlicher, P.; Graciani, R.; Haas, T.; Hain, W.; Hartner, G.F.; Hebbel, K.; Hillert, S.; Koch, W.; Koetz, U.; Kowalski, H.; Labes, H.; Loehr, B.; Mankel, R.; Martens, J.; Martinez, M.; Milite, M.; Moritz, M.; Notz, D.; Petrucci, M.C.; Polini, A.; Rohde, M.; Savin, A.A.; Schneekloth, U.; Selonke, F.; Sievers, M.; Stonjek, S.; Wolf, G.; Wollmer, U.; Youngman, C.; Zeuner, W.; Coldewey, C.; Lopez-Duran Viani, A.; Meyer, A.; Schlenstedt, S.; Straub, P.B.; Barbagli, G.; Gallo, E.; Parenti, A.; Pelfer, P.G.; Bamberger, A.; Benen, A.; Coppola, N.; Eisenhardt, S.; Markun, P.; Raach, H.; Woelfle, S.; Bussey, P.J.; Bell, M.; Doyle, A.T.; Glasman, C.; Lee, S.W.; Lupi, A.; Macdonald, N.; McCance, G.J.; Saxon, D.H.; Sinclair, L.E.; Skillicorn, I.O.; Waugh, R.; Bohnet, I.; Gendner, N.; Holm, U.; Meyer-Larsen, A.; Salehi, H.; Wick, K.; Carli, T.; Garfagnini, A.; Gialas, I.; Gladilin, L.K.; Kcira, D.; Klanner, R.; Lohrmann, E.; Goncalo, R.; Long, K.R.; Miller, D.B.; Tapper, A.D.; Walker, R.; Cloth, P.; Filges, D.; Ishii, T.; Kuze, M.; Nagano, K.; Tokushuku, K.; Yamada, S.; Yamazaki, Y.; Ahn, S.H.; Lee, S.B.; Park, S.K.; Lim, H.; Son, D.; Barreiro, F.; Garcia, G.; Gonzalez, O.; Labarga, L.; del Peso, J.; Redondo, I.; Terron, J.; Vazquez, M.; Barbi, M.; Corriveau, F.; Hanna, D.S.; Ochs, A.; Padhi, S.; Stairs, D.G.; Wing, M.; Tsurugai, T.; Antonov, A.; Bashkirov, V.; Danilov, M.; Dolgoshein, B.A.; Gladkov, D.; Sosnovtsev, V.; Suchkov, S.; Dementiev, R.K.; Ermolov, P.F.; Golubkov, Yu.A.; Katkov, I.I.; Khein, L.A.; Korotkova, N.A.; Korzhavina, I.A.; Kuzmin, V.A.; Lukina, O.Yu.; Proskuryakov, A.S.; Shcheglova, L.M.; Solomin, A.N.; Vlasov, N.N.; Zotkin, S.A.; Bokel, C.; Botje, M.; Bruemmer, N.; Engelen, J.; Grijpink, S.; Koffeman, E.; Kooijman, P.; Schagen, S.; van Sighem, A.; Tassi, E.; Tiecke, H.; Tuning, N.; Velthuis, J.J.; Vossebeld, J.; Wiggers, L.; de Wolf, E.; Bylsma, B.; Durkin, L.S.; Gilmore, J.; Ginsburg, C.M.; Kim, C.L.; Ling, T.Y.; Boogert, S.; Cooper-Sarkar, A.M.; Devenish, R.C.E.; Grosse-Knetter, J.; Matsushita, T.; Ruske, O.; Sutton, M.R.; Walczak, R.; Bertolin, A.; Brugnera, R.; Carlin, R.; Dal Corso, F.; Dusini, S.; Limentani, S.; Longhin, A.; Posocco, M.; Stanco, L.; Turcato, M.; Adamczyk, L.; Iannotti, L.; Oh, B.Y.; Okrasinski, J.R.; Saull, P.R.B.; Toothacker, W.S.; Whitmore, J.J.; Iga, Y.; D'Agostini, G.; Marini, G.; Nigro, A.; Cormack, C.; Hart, J.C.; McCubbin, N.A.; Shah, T.P.; Epperson, D.; Heusch, C.; Sadrozinski, H.F.-W.; Seiden, A.; Wichmann, R.; Williams, D.C.; Park, I.H.; Pavel, N.; Abramowicz , H.; Dagan, S.; Kananov, S.; Kreisel, A.; Levy, A.; Abe, T.; Fusayasu, T.; Kohno, T.; Umemori, K.; Yamashita, T.; Hamatsu, R.; Hirose, T.; Inuzuka, M.; Kitamura, S.; Matsuzawa, K.; Nishimura, T.; Arneodo, M.; Cartiglia, N.; Cirio, R.; Costa, M.; Ferrero, M.I.; Maselli, S.; Monaco, V.; Peroni, C.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Bailey, D.C.; Fagerstroem, C.-P.; Galea, R.; Koop, T.; Levman, G.M.; Martin, J.F.; Mirea, A.; Sabetfakhri, A.; Butterworth, J.M.; Hayes, M.E.; Heaphy, E.A.; Jones, T.W.; Lane, J.B.; West, B.J.; Ciborowski, J.; Ciesielski, R.; Grzelak, G.; Nowak, R.J.; Pawlak, J.M.; Pawlak, R.; Smalska, B.; Tymieniecka, T.; Wroblewski, A.K.; Zakrzewski, J.A.; Zarnecki, A.F.; Adamus, M.; Gadaj, T.; Deppe, O.; Eisenberg, Y.; Hochman, D.; Karshon, U.; Badgett, W.F.; Chapin, D.; Cross, R.; Foudas, C.; Mattingly, S.; Reeder, D.D.; Smith, W.H.; Vaiciulis, A.; Wildschek, T.; Wodarczyk, M.; Deshpande, A.; Dhawan, S.; Hughes, V.W.; Bhadra, S.; Catterall, C.; Cole, J.E.; Frisken, W.R.; Hall-Wilton, R.; Khakzad, M.; Menary, S.
2001-01-01
Differential cross sections for dijet photoproduction in association with a leading neutron using the reaction e + +p→e + +n+jet+jet+X r have been measured with the ZEUS detector at HERA using an integrated luminosity of 6.4 pb -1 . The fraction of dijet events with a leading neutron in the final state was studied as a function of the jet kinematic variables. The cross sections were measured for jet transverse energies E T jet >6 GeV, neutron energy E n >400 GeV, and neutron production angle θ n <0.8 mrad. The data are broadly consistent with factorization of the lepton and hadron vertices and with a simple one-pion-exchange model
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Yan, Huaicheng; Zhang, Hao; Yang, Fuwen; Zhan, Xisheng; Peng, Chen
2017-08-18
This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.
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Directory of Open Access Journals (Sweden)
Lisa Florence Seymour
2016-07-01
Full Text Available Shortage of computing skills is a global concern as it affects national development and business success. Yet, despite high job availability and high salaries in computing professions, insufficient numbers of students are choosing to study the various computing disciplines. This South African study looks at the Information Systems (IS major which is misunderstood by high school students. This retroductive case study identifies the events which lead students to change their major to IS. The study confirms the importance of interest in a major as well as the perceived high value of a major, which feature as dominant factors in the literature. Yet these are not the initial events that lead to students changing their major to IS. Events that initiate the process include losing passion for a previous major, experiencing difficulty in a previous major as well as enjoying the introductory IS course. The paper has practical advice for IS Departments and argues for a generic first year for students as well as a focus on enjoyment and skills aligned to IS professional practice in introductory IS courses. These findings can be generalised to other majors and, hence, the theoretical contribution adds to the literature on career choice in general.
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Seismic signal auto-detecing from different features by using Convolutional Neural Network
Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.
2017-12-01
We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.
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Ortigue, S; Bianchi-Demicheli, F; Hamilton, A F de C; Grafton, S T
2007-07-01
Throughout the ages, love has been defined as a motivated and goal-directed mechanism with explicit and implicit mechanisms. Recent evidence demonstrated that the explicit representation of love recruits subcorticocortical pathways mediating reward, emotion, and motivation systems. However, the neural basis of the implicit (unconscious) representation of love remains unknown. To assess this question, we combined event-related functional magnetic resonance imaging (fMRI) with a behavioral subliminal priming paradigm embedded in a lexical decision task. In this task, the name of either a beloved partner, a neutral friend, or a passionate hobby was subliminally presented before a target stimulus (word, nonword, or blank), and participants were required to decide if the target was a word or not. Behavioral results showed that subliminal presentation of either a beloved's name (love prime) or a passion descriptor (passion prime) enhanced reaction times in a similar fashion. Subliminal presentation of a friend's name (friend prime) did not show any beneficial effects. Functional results showed that subliminal priming with a beloved's name (as opposed to either a friend's name or a passion descriptor) specifically recruited brain areas involved in abstract representations of others and the self, in addition to motivation circuits shared with other sources of passion. More precisely, love primes recruited the fusiform and angular gyri. Our findings suggest that love, as a subliminal prime, involves a specific neural network that surpasses a dopaminergic-motivation system.
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Ballester, Joan; Bordoni, Simona; Petrova, Desislava; Rodó, Xavier
2015-04-01
Despite steady progress in the understanding of El Niño-Southern Oscillation (ENSO) in the past decades, questions remain on the exact mechanisms leading to the onset of El Niño (EN) events. Several authors have highlighted how the subsurface heat buildup in the western tropical Pacific and the recharged phase in equatorial heat content are intrinsic elements of ENSO variability, leading to those changes in zonal wind stress, sea surface temperature and thermocline tilt that characterize the growing and mature phases of EN. Here we use an ensemble of ocean and atmosphere assimilation products to identify the mechanisms contributing to the heat buildup that precedes EN events by about 18-24 months on average. Anomalous equatorward subsurface mass convergence due to meridional Sverdrup transport is found to be an important mechanism of thermocline deepening near and to the east of the dateline. In the warm pool, instead, surface horizontal convergence and downwelling motion have a leading role in subsurface warming, since equatorward mass convergence is weaker and counterbalanced by subsurface zonal divergence. The picture emerging from our results highlights the complexity of the three dimensional dynamic and thermodynamic structure of the tropical Pacific during the heat buildup leading to EN events.
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Introduction to neural networks
International Nuclear Information System (INIS)
Pavlopoulos, P.
1996-01-01
This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix
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Neural Activity during Encoding Predicts False Memories Created by Misinformation
Okado, Yoko; Stark, Craig E. L.
2005-01-01
False memories are often demonstrated using the misinformation paradigm, in which a person's recollection of a witnessed event is altered after exposure to misinformation about the event. The neural basis of this phenomenon, however, remains unknown. The authors used fMRI to investigate encoding processes during the viewing of an event and…
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Miyajima, Maki; Toyomaki, Atsuhito; Hashimoto, Naoki; Kusumi, Ichiro; Murohashi, Harumitsu; Koyama, Tsukasa
2012-08-01
Task switching is a well-known cognitive paradigm to explore task-set reconfiguration processes such as rule shifting. In particular, endogenous task switching is thought to differ qualitatively from stimulus-triggered exogenous task switching. However, no previous study has examined the neural substrate of endogenous task switching. The purpose of the present study is to explore the differences between event-related potential responses to exogenous and endogenous rule switching at cue stimulus. We modified two patterns of cued switching tasks: exogenous (bottom-up) rule switching and endogenous (top-down) rule switching. In each task cue stimulus was configured to induce switching or maintaining rule. In exogenous switching tasks, late positive deflection was larger in the switch rule condition than in the maintain rule condition. However, in endogenous switching tasks late positive deflection was unexpectedly larger in the maintain-rule condition than in the switch-rule condition. These results indicate that exogenous rule switching is explicit stimulus-driven processes, whereas endogenous rule switching is implicitly parallel processes independent of external stimulus.
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Measurement of dijet cross sections for events with a leading neutron in photoproduction at HERA
Energy Technology Data Exchange (ETDEWEB)
Breitweg, J.; Chekanov, S.; Derrick, M.; Krakauer, D.; Magill, S.; Musgrave, B.; Pellegrino, A.; Repond, J.; Stanek, R.; Yoshida, R.; Mattingly, M.C.K.; Antonioli, P.; Bari, G.; Basile, M.; Bellagamba, L.; Boscherini, D.; Bruni, A.; Bruni, G.; Cara Romeo, G.; Cifarelli, L.; Cindolo, F.; Contin, A.; Corradi, M.; De Pasquale, S.; Giusti, P.; Iacobucci, G.; Levi, G.; Margotti, A.; Massam, T.; Nania, R.; Palmonari, F.; Pesci, A.; Sartorelli, G.; Zichichi, A.; Amelung, C.; Bornheim, A.; Brock, I.; Coboeken, K.; Crittenden, J.; Deffner, R.; Hartmann, H.; Heinloth, K.; Hilger, E.; Irrgang, P.; Jakob, H.-P.; Kappes, A.; Katz, U.F.; Kerger, R.; Paul, E.; Rautenberg, J.; Schnurbusch, H.; Stifutkin, A.; Tandler, J.; Voss, K.C.; Weber, A.; Wieber, H.; Bailey, D.S.; Barret, O.; Brook, N.H.; Foster, B. E-mail: b.foster@bristol.ac.uk; Heath, G.P.; Heath, H.F.; Rodrigues, E.; Scott, J.; Tapper, R.J.; Capua, M.; Schioppa, M.; Susinno, G.; Jeoung, H.Y.; Kim, J.Y.; Lee, J.H.; Lim, I.T.; Ma, K.J.; Pac, M.Y.; Caldwell, A.; Liu, W.; Liu, X.; Mellado, B.; Paganis, S.; Sampson, S.; Schmidke, W.B.; Sciulli, F.; Chwastowski, J.; Eskreys, A.; Figiel, J.; Klimek, K.; Olkiewicz, K.; Piotrzkowski, K.; Przybycien, M.B.; Stopa, P.; Zawiejski, L.; Bednarek, B.; Jelen, K.; Kisielewska, D.; Kowal, A.M.; Kowalski, T.; Przybycien, M.; Rulikowska-Zarebska, E.; Suszycki, L.; Szuba, D.; Kotanski, A.; Bauerdick, L.A.T.; Behrens, U.; Bienlein, J.K.; Borras, K.; Chiochia, V.; Dannheim, D.; Desler, K.; Drews, G.; Fox-Murphy, A.; Fricke, U.; Goebel, F.; Goers, S.; Goettlicher, P.; Graciani, R.; Haas, T.; Hain, W.; Hartner, G.F.; Hebbel, K.; Hillert, S.; Koch, W.; Koetz, U.; Kowalski, H.; Labes, H.; Loehr, B.; Mankel, R.; Martens, J.; Martinez, M.; Milite, M.; Moritz, M.; Notz, D.; Petrucci, M.C.; Polini, A.; Rohde, M.; Savin, A.A.; Schneekloth, U.; Selonke, F.; Sievers, M.; Stonjek, S.; Wolf, G.; Wollmer, U.; Youngman, C.; Zeuner, W.; Coldewey, C.; Lopez-Duran Viani, A.; Meyer, A.; Schlenstedt, S.[and others
2001-02-26
Differential cross sections for dijet photoproduction in association with a leading neutron using the reaction e{sup +}+p{yields}e{sup +}+n+jet+jet+X{sub r} have been measured with the ZEUS detector at HERA using an integrated luminosity of 6.4 pb{sup -1}. The fraction of dijet events with a leading neutron in the final state was studied as a function of the jet kinematic variables. The cross sections were measured for jet transverse energies E{sub T}{sup jet}>6 GeV, neutron energy E{sub n}>400 GeV, and neutron production angle {theta}{sub n}<0.8 mrad. The data are broadly consistent with factorization of the lepton and hadron vertices and with a simple one-pion-exchange model.
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A dynamic neural field model of temporal order judgments.
Hecht, Lauren N; Spencer, John P; Vecera, Shaun P
2015-12-01
Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).
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Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica
Directory of Open Access Journals (Sweden)
Tamas Wani
2014-03-01
Full Text Available Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events. Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.
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THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE
Directory of Open Access Journals (Sweden)
António José Silva
2007-03-01
Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports
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Directory of Open Access Journals (Sweden)
A. El-Shafie
2011-03-01
Full Text Available Artificial neural networks (ANN have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces problems. One of the major shortcomings is the search for the optimal input pattern in order to enhance the forecasting capabilities for the output. The second challenge is the over-fitting problem during the training procedure and this occurs when ANN loses its generalization. In this research, autocorrelation and cross correlation analyses are suggested as a method for searching the optimal input pattern. On the other hand, two generalized methods namely, Regularized Neural Network (RNN and Ensemble Neural Network (ENN models are developed to overcome the drawbacks of classical ANN models. Using Generalized Neural Network (GNN helped avoid over-fitting of training data which was observed as a limitation of classical ANN models. Real inflow data collected over the last 130 years at Lake Nasser was used to train, test and validate the proposed model. Results show that the proposed GNN model outperforms non-generalized neural network and conventional auto-regressive models and it could provide accurate inflow forecasting.
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Semantic reasoning in zero example video event retrieval
Boer, M.H.T. de; Lu, Y.J.; Zhang, H.; Schutte, K.; Ngo, C.W.; Kraaij, W.
2017-01-01
Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneficial for the retrieval of high-level events using visual examples,
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2D neural hardware versus 3D biological ones
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-31
This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.
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Using a neural network in the search for the Higgs boson
International Nuclear Information System (INIS)
Hultqvist, K.; Jacobsson, R.; Johansson, K.E.
1995-01-01
The search for the Standard Model Higgs boson in high energy e + e - collisions requires analysis techniques which efficiently discriminate against the very large background. A classifier based on a feed-forward neural network has been extensively used in a search in the channel where the Higgs boson is produced in association with neutrinos. The method has significantly improved the sensitivity of the search. With a simple preselection based on event topology followed by a neural network we have obtained a combined background rejection factor of more than 29 000 and a selection efficiency for Higgs particle events of 54%, assuming a mass of 55 GeV/c 2 for the Higgs boson. We describe here the details of the analysis with emphasis on the neural network. (orig.)
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Perceived threat predicts the neural sequelae of combat stress
van Wingen, G. A.; Geuze, E.; Vermetten, E.; Fernández, G.
2011-01-01
Exposure to severe stressors increases the risk for psychiatric disorders in vulnerable individuals, but can lead to positive outcomes for others. However, it remains unknown how severe stress affects neural functioning in humans and what factors mediate individual differences in the neural sequelae
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Perceived threat predicts the neural sequelae of combat stress.
Wingen, G.A. van; Geuze, E.; Vermetten, E.; Fernandez, G.S.E.
2011-01-01
Exposure to severe stressors increases the risk for psychiatric disorders in vulnerable individuals, but can lead to positive outcomes for others. However, it remains unknown how severe stress affects neural functioning in humans and what factors mediate individual differences in the neural sequelae
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An Examination of the Neural Unreliability Thesis of Autism.
Butler, John S; Molholm, Sophie; Andrade, Gizely N; Foxe, John J
2017-01-01
An emerging neuropathological theory of Autism, referred to here as "the neural unreliability thesis," proposes greater variability in moment-to-moment cortical representation of environmental events, such that the system shows general instability in its impulse response function. Leading evidence for this thesis derives from functional neuroimaging, a methodology ill-suited for detailed assessment of sensory transmission dynamics occurring at the millisecond scale. Electrophysiological assessments of this thesis, however, are sparse and unconvincing. We conducted detailed examination of visual and somatosensory evoked activity using high-density electrical mapping in individuals with autism (N = 20) and precisely matched neurotypical controls (N = 20), recording large numbers of trials that allowed for exhaustive time-frequency analyses at the single-trial level. Measures of intertrial coherence and event-related spectral perturbation revealed no convincing evidence for an unreliability account of sensory responsivity in autism. Indeed, results point to robust, highly reproducible response functions marked for their exceedingly close correspondence to those in neurotypical controls. © The Author 2016. Published by Oxford University Press.
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1996-01-01
When a 33 TeV Lead nucleous projectile hits head on a Lead target it creates for a brief instant of time a system of quarks and gluons that more than 100'000 times hotter than the sun. As this fireball expands it reconstitutes normal matter creating thousands of particles (pions, kaons, protons, ....). The NA49 Time Projection Chambers extending over 13 m can record a large majority of the produced charged particles. The result of a complex analysis is shown here in a event display of the reconstructed tracks. The blue lines represent the boundaries of the detectors in a perspective view from the far end of the experiment towards the interaction point, where all the tracks originate.
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Tagging b quark events in ALEPH with neural networks
International Nuclear Information System (INIS)
Proriol, J.; Jousset, J.; Guicheney, C.; Falvard, A.; Henrard, P.; Pallin, D.; Perret, P.; Brandl, B.
1991-01-01
Comparison of different methods to tag b quark events are presented: multilayered perceptron (MLP), Learning Vector Quantization (LVQ), discriminant analysis, combination of any two of the above methods. The sample events come from the ALEPH Monte Carlo and data, from the 1990 ALEPH runs. (authors) 12 refs., 16 figs., 5 tabs
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Event Memory: A Theory of Memory for Laboratory, Autobiographical, and Fictional Events
Rubin, David C.; Umanath, Sharda
2015-01-01
An event memory is a mental construction of a scene recalled as a single occurrence. It therefore requires the hippocampus and ventral visual stream needed for all scene construction. The construction need not come with a sense of reliving or be made by a participant in the event, and it can be a summary of occurrences from more than one encoding. The mental construction, or physical rendering, of any scene must be done from a specific location and time; this introduces a ‘self’ located in space and time, which is a necessary, but need not be a sufficient, condition for a sense of reliving. We base our theory on scene construction rather than reliving because this allows the integration of many literatures and because there is more accumulated knowledge about scene construction’s phenomenology, behavior, and neural basis. Event memory differs from episodic memory in that it does not conflate the independent dimensions of whether or not a memory is relived, is about the self, is recalled voluntarily, or is based on a single encoding with whether it is recalled as a single occurrence of a scene. Thus, we argue that event memory provides a clearer contrast to semantic memory, which also can be about the self, be recalled voluntarily, and be from a unique encoding; allows for a more comprehensive dimensional account of the structure of explicit memory; and better accounts for laboratory and real world behavioral and neural results, including those from neuropsychology and neuroimaging, than does episodic memory. PMID:25330330
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Multivariate analysis methods to tag b quark events at LEP/SLC
International Nuclear Information System (INIS)
Brandl, B.; Falvard, A.; Guicheney, C.; Henrard, P.; Jousset, J.; Proriol, J.
1992-01-01
Multivariate analyses are applied to tag Z → bb-bar events at LEP/SLC. They are based on the specific b-event shape caused by the large b-quark mass. Discriminant analyses, classification trees and neural networks are presented and their performances are compared. It is shown that the neural network approach, due to its non-linearity, copes best with the complexity of the problem. As an example for an application of the developed methods the measurement of Γ(Z → bb-bar) is discussed. The usefulness of methods based on the global event shape is limited by the uncertainties introduced by the necessity of event simulation. As solution a double tag method is presented which can be applied to many tasks of LEP/SLC heavy flavour physics. (author) 29 refs.; 6 figs.; 1 tab
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Neural events that underlie remembering something that never happened.
Gonsalves, B; Paller, K A
2000-12-01
We induced people to experience a false-memory illusion by first asking them to visualize common objects when cued with the corresponding word; on some trials, a photograph of the object was presented 1800 ms after the cue word. We then tested their memory for the photographs. Posterior brain potentials in response to words at encoding were more positive if the corresponding object was later falsely remembered as a photograph. Similar brain potentials during the memory test were more positive for true than for false memories. These results implicate visual imagery in the generation of false memories and provide neural correlates of processing differences between true and false memories.
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Directory of Open Access Journals (Sweden)
Scott B Hu
Full Text Available Clinical deterioration (ICU transfer and cardiac arrest occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates.Retrospective cohort study.The hematologic malignancy unit in an academic medical center in the United States.Adult patients admitted to the hematologic malignancy unit from 2009 to 2010.None.Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features. A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest. The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS. Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively.We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.
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Optimal neural computations require analog processors
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-31
This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).
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Syed Ali, M; Vadivel, R; Saravanakumar, R
2018-06-01
This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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Sex differences in the neural basis of emotional memories.
Canli, Turhan; Desmond, John E; Zhao, Zuo; Gabrieli, John D E
2002-08-06
Psychological studies have found better memory in women than men for emotional events, but the neural basis for this difference is unknown. We used event-related functional MRI to assess whether sex differences in memory for emotional stimuli is associated with activation of different neural systems in men and women. Brain activation in 12 men and 12 women was recorded while they rated their experience of emotional arousal in response to neutral and emotionally negative pictures. In a recognition memory test 3 weeks after scanning, highly emotional pictures were remembered best, and remembered better by women than by men. Men and women activated different neural circuits to encode stimuli effectively into memory even when the analysis was restricted to pictures rated equally arousing by both groups. Men activated significantly more structures than women in a network that included the right amygdala, whereas women activated significantly fewer structures in a network that included the left amygdala. Women had significantly more brain regions where activation correlated with both ongoing evaluation of emotional experience and with subsequent memory for the most emotionally arousing pictures. Greater overlap in brain regions sensitive to current emotion and contributing to subsequent memory may be a neural mechanism for emotions to enhance memory more powerfully in women than in men.
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Criticality and avalanches in neural networks
International Nuclear Information System (INIS)
Zare, Marzieh; Grigolini, Paolo
2013-01-01
Highlights: • Temporal criticality is used as criticality indicator. • The Mittag–Leffler function is proposed as a proper form of temporal complexity. • The distribution of avalanche size becomes scale free in the supercritical state. • The scale-free distribution of avalanche sizes is an epileptic manifestation. -- Abstract: Experimental work, both in vitro and in vivo, reveals the occurrence of neural avalanches with an inverse power law distribution in size and time duration. These properties are interpreted as an evident manifestation of criticality, thereby suggesting that the brain is an operating near criticality complex system: an attractive theoretical perspective that according to Gerhard Werner may help to shed light on the origin of consciousness. However, a recent experimental observation shows no clear evidence for power-law scaling in awake and sleeping brain of mammals, casting doubts on the assumption that the brain works at criticality. This article rests on a model proposed by our group in earlier publications to generate neural avalanches with the time duration and size distribution matching the experimental results on neural networks. We now refine the analysis of the time distance between consecutive firing bursts and observe the deviation of the corresponding distribution from the Poisson statistics, as the system moves from the non-cooperative to the cooperative regime. In other words, we make the assumption that the genuine signature of criticality may emerge from temporal complexity rather than from the size and time duration of avalanches. We argue that the Mittag–Leffler (ML) exponential function is a satisfactory indicator of temporal complexity, namely of the occurrence of non-Poisson and renewal events. The assumption that the onset of criticality corresponds to the birth of renewal non-Poisson events establishes a neat distinction between the ML function and the power law avalanches generating regime. We find that
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Short-term synaptic plasticity and heterogeneity in neural systems
Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.
2013-01-01
We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.
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Event Recognition Based on Deep Learning in Chinese Texts.
Directory of Open Access Journals (Sweden)
Yajun Zhang
Full Text Available Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM. Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN, then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.
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Event Recognition Based on Deep Learning in Chinese Texts.
Zhang, Yajun; Liu, Zongtian; Zhou, Wen
2016-01-01
Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.
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Chatrchyan, Serguei; Sirunyan, Albert M; Tumasyan, Armen; Adam, Wolfgang; Aguilo, Ernest; Bergauer, Thomas; Dragicevic, Marko; Erö, Janos; Fabjan, Christian; Friedl, Markus; Fruehwirth, Rudolf; Ghete, Vasile Mihai; Hörmann, Natascha; Hrubec, Josef; Jeitler, Manfred; Kiesenhofer, Wolfgang; Knünz, Valentin; Krammer, Manfred; Krätschmer, Ilse; Liko, Dietrich; Mikulec, Ivan; Pernicka, Manfred; Rabady, Dinyar; Rahbaran, Babak; Rohringer, Christine; Rohringer, Herbert; Schöfbeck, Robert; Strauss, Josef; Taurok, Anton; Waltenberger, Wolfgang; Wulz, Claudia-Elisabeth; Mossolov, Vladimir; Shumeiko, Nikolai; Suarez Gonzalez, Juan; Bansal, Monika; Bansal, Sunil; Cornelis, Tom; De Wolf, Eddi A; Janssen, Xavier; Luyckx, Sten; Mucibello, Luca; Ochesanu, Silvia; Roland, Benoit; Rougny, Romain; Selvaggi, Michele; Van Haevermaet, Hans; Van Mechelen, Pierre; Van Remortel, Nick; Van Spilbeeck, Alex; Blekman, Freya; Blyweert, Stijn; D'Hondt, Jorgen; Gonzalez Suarez, Rebeca; Kalogeropoulos, Alexis; Maes, Michael; Olbrechts, Annik; Van Doninck, Walter; Van Mulders, Petra; Van Onsem, Gerrit Patrick; Villella, Ilaria; Clerbaux, Barbara; De Lentdecker, Gilles; Dero, Vincent; Gay, Arnaud; Hreus, Tomas; Léonard, Alexandre; Marage, Pierre Edouard; Mohammadi, Abdollah; Reis, Thomas; Thomas, Laurent; Vander Velde, Catherine; Vanlaer, Pascal; Wang, Jian; Adler, Volker; Beernaert, Kelly; Cimmino, Anna; Costantini, Silvia; Garcia, Guillaume; Grunewald, Martin; Klein, Benjamin; Lellouch, Jérémie; Marinov, Andrey; Mccartin, Joseph; Ocampo Rios, Alberto Andres; Ryckbosch, Dirk; Strobbe, Nadja; Thyssen, Filip; Tytgat, Michael; Walsh, Sinead; Yazgan, Efe; Zaganidis, Nicolas; Basegmez, Suzan; Bruno, Giacomo; Castello, Roberto; Ceard, Ludivine; Delaere, Christophe; Du Pree, Tristan; Favart, Denis; Forthomme, Laurent; Giammanco, Andrea; Hollar, Jonathan; Lemaitre, Vincent; Liao, Junhui; Militaru, Otilia; Nuttens, Claude; Pagano, Davide; Pin, Arnaud; Piotrzkowski, Krzysztof; Vizan Garcia, Jesus Manuel; Beliy, Nikita; Caebergs, Thierry; Daubie, Evelyne; Hammad, Gregory Habib; Alves, Gilvan; Correa Martins Junior, Marcos; Martins, Thiago; Pol, Maria Elena; Henrique Gomes E Souza, Moacyr; Aldá Júnior, Walter Luiz; Carvalho, Wagner; Custódio, Analu; Melo Da Costa, Eliza; De Jesus Damiao, Dilson; De Oliveira Martins, Carley; Fonseca De Souza, Sandro; Malbouisson, Helena; Malek, Magdalena; Matos Figueiredo, Diego; Mundim, Luiz; Nogima, Helio; Prado Da Silva, Wanda Lucia; Santoro, Alberto; Soares Jorge, Luana; Sznajder, Andre; Vilela Pereira, Antonio; Souza Dos Anjos, Tiago; Bernardes, Cesar Augusto; De Almeida Dias, Flavia; Tomei, Thiago; De Moraes Gregores, Eduardo; Lagana, Caio; Da Cunha Marinho, Franciole; Mercadante, Pedro G; Novaes, Sergio F; Padula, Sandra; Genchev, Vladimir; Iaydjiev, Plamen; Piperov, Stefan; Rodozov, Mircho; Stoykova, Stefka; Sultanov, Georgi; Tcholakov, Vanio; Trayanov, Rumen; Vutova, Mariana; Dimitrov, Anton; Hadjiiska, Roumyana; Kozhuharov, Venelin; Litov, Leander; Pavlov, Borislav; Petkov, Peicho; Bian, Jian-Guo; Chen, Guo-Ming; Chen, He-Sheng; Jiang, Chun-Hua; Liang, Dong; Liang, Song; Meng, Xiangwei; Tao, Junquan; Wang, Jian; Wang, Xianyou; Wang, Zheng; Xiao, Hong; Xu, Ming; Zang, Jingjing; Zhang, Zhen; Asawatangtrakuldee, Chayanit; Ban, Yong; Guo, Yifei; Li, Wenbo; Liu, Shuai; Mao, Yajun; Qian, Si-Jin; Teng, Haiyun; Wang, Dayong; Zhang, Linlin; Zou, Wei; Avila, Carlos; Gomez, Juan Pablo; Gomez Moreno, Bernardo; Osorio Oliveros, Andres Felipe; Sanabria, Juan Carlos; Godinovic, Nikola; Lelas, Damir; Plestina, Roko; Polic, Dunja; Puljak, Ivica; Antunovic, Zeljko; Kovac, Marko; Brigljevic, Vuko; Duric, Senka; Kadija, Kreso; Luetic, Jelena; Mekterovic, Darko; Morovic, Srecko; Attikis, Alexandros; Galanti, Mario; Mavromanolakis, Georgios; Mousa, Jehad; Nicolaou, Charalambos; Ptochos, Fotios; Razis, Panos A; Finger, Miroslav; Finger Jr, Michael; Assran, Yasser; Elgammal, Sherif; Ellithi Kamel, Ali; Mahmoud, Mohammed; Mahrous, Ayman; Radi, Amr; Kadastik, Mario; Müntel, Mait; Murumaa, Marion; Raidal, Martti; Rebane, Liis; Tiko, Andres; Eerola, Paula; Fedi, Giacomo; Voutilainen, Mikko; Härkönen, Jaakko; Heikkinen, Mika Aatos; Karimäki, Veikko; Kinnunen, Ritva; Kortelainen, Matti J; Lampén, Tapio; Lassila-Perini, Kati; Lehti, Sami; Lindén, Tomas; Luukka, Panja-Riina; Mäenpää, Teppo; Peltola, Timo; Tuominen, Eija; Tuominiemi, Jorma; Tuovinen, Esa; Ungaro, Donatella; Wendland, Lauri; Banzuzi, Kukka; Karjalainen, Ahti; Korpela, Arja; Tuuva, Tuure; Besancon, Marc; Choudhury, Somnath; Dejardin, Marc; Denegri, Daniel; Fabbro, Bernard; Faure, Jean-Louis; Ferri, Federico; Ganjour, Serguei; Givernaud, Alain; Gras, Philippe; Hamel de Monchenault, Gautier; Jarry, Patrick; Locci, Elizabeth; Malcles, Julie; Millischer, Laurent; Nayak, Aruna; Rander, John; Rosowsky, André; Titov, Maksym; Baffioni, Stephanie; Beaudette, Florian; Benhabib, Lamia; Bianchini, Lorenzo; Bluj, Michal; Busson, Philippe; Charlot, Claude; Daci, Nadir; Dahms, Torsten; Dalchenko, Mykhailo; Dobrzynski, Ludwik; Florent, Alice; Granier de Cassagnac, Raphael; Haguenauer, Maurice; Miné, Philippe; Mironov, Camelia; Naranjo, Ivo Nicolas; Nguyen, Matthew; Ochando, Christophe; Paganini, Pascal; Sabes, David; Salerno, Roberto; Sirois, Yves; Veelken, Christian; Zabi, Alexandre; Agram, Jean-Laurent; Andrea, Jeremy; Bloch, Daniel; Bodin, David; Brom, Jean-Marie; Cardaci, Marco; Chabert, Eric Christian; Collard, Caroline; Conte, Eric; Drouhin, Frédéric; Fontaine, Jean-Charles; Gelé, Denis; Goerlach, Ulrich; Juillot, Pierre; Le Bihan, Anne-Catherine; Van Hove, Pierre; Fassi, Farida; Mercier, Damien; Beauceron, Stephanie; Beaupere, Nicolas; Bondu, Olivier; Boudoul, Gaelle; Brochet, Sébastien; Chasserat, Julien; Chierici, Roberto; Contardo, Didier; Depasse, Pierre; El Mamouni, Houmani; Fay, Jean; Gascon, Susan; Gouzevitch, Maxime; Ille, Bernard; Kurca, Tibor; Lethuillier, Morgan; Mirabito, Laurent; Perries, Stephane; Sgandurra, Louis; Sordini, Viola; Tschudi, Yohann; Verdier, Patrice; Viret, Sébastien; Tsamalaidze, Zviad; Autermann, Christian; Beranek, Sarah; Calpas, Betty; Edelhoff, Matthias; Feld, Lutz; Heracleous, Natalie; Hindrichs, Otto; Jussen, Ruediger; Klein, Katja; Merz, Jennifer; Ostapchuk, Andrey; Perieanu, Adrian; Raupach, Frank; Sammet, Jan; Schael, Stefan; Sprenger, Daniel; Weber, Hendrik; Wittmer, Bruno; Zhukov, Valery; Ata, Metin; Caudron, Julien; Dietz-Laursonn, Erik; Duchardt, Deborah; Erdmann, Martin; Fischer, Robert; Güth, Andreas; Hebbeker, Thomas; Heidemann, Carsten; Hoepfner, Kerstin; Klingebiel, Dennis; Kreuzer, Peter; Merschmeyer, Markus; Meyer, Arnd; Olschewski, Mark; Papacz, Paul; Pieta, Holger; Reithler, Hans; Schmitz, Stefan Antonius; Sonnenschein, Lars; Steggemann, Jan; Teyssier, Daniel; Thüer, Sebastian; Weber, Martin; Bontenackels, Michael; Cherepanov, Vladimir; Erdogan, Yusuf; Flügge, Günter; Geenen, Heiko; Geisler, Matthias; Haj Ahmad, Wael; Hoehle, Felix; Kargoll, Bastian; Kress, Thomas; Kuessel, Yvonne; Lingemann, Joschka; Nowack, Andreas; Perchalla, Lars; Pooth, Oliver; Sauerland, Philip; Stahl, Achim; Aldaya Martin, Maria; Behr, Joerg; Behrenhoff, Wolf; Behrens, Ulf; Bergholz, Matthias; Bethani, Agni; Borras, Kerstin; Burgmeier, Armin; Cakir, Altan; Calligaris, Luigi; Campbell, Alan; Castro, Elena; Costanza, Francesco; Dammann, Dirk; Diez Pardos, Carmen; Eckerlin, Guenter; Eckstein, Doris; Flucke, Gero; Geiser, Achim; Glushkov, Ivan; Gunnellini, Paolo; Habib, Shiraz; Hauk, Johannes; Hellwig, Gregor; Jung, Hannes; Kasemann, Matthias; Katsas, Panagiotis; Kleinwort, Claus; Kluge, Hannelies; Knutsson, Albert; Krämer, Mira; Krücker, Dirk; Kuznetsova, Ekaterina; Lange, Wolfgang; Leonard, Jessica; Lohmann, Wolfgang; Lutz, Benjamin; Mankel, Rainer; Marfin, Ihar; Marienfeld, Markus; Melzer-Pellmann, Isabell-Alissandra; Meyer, Andreas Bernhard; Mnich, Joachim; Mussgiller, Andreas; Naumann-Emme, Sebastian; Novgorodova, Olga; Olzem, Jan; Perrey, Hanno; Petrukhin, Alexey; Pitzl, Daniel; Raspereza, Alexei; Ribeiro Cipriano, Pedro M; Riedl, Caroline; Ron, Elias; Rosin, Michele; Salfeld-Nebgen, Jakob; Schmidt, Ringo; Schoerner-Sadenius, Thomas; Sen, Niladri; Spiridonov, Alexander; Stein, Matthias; Walsh, Roberval; Wissing, Christoph; Blobel, Volker; Enderle, Holger; Erfle, Joachim; Gebbert, Ulla; Görner, Martin; Gosselink, Martijn; Haller, Johannes; Hermanns, Thomas; Höing, Rebekka Sophie; Kaschube, Kolja; Kaussen, Gordon; Kirschenmann, Henning; Klanner, Robert; Lange, Jörn; Nowak, Friederike; Peiffer, Thomas; Pietsch, Niklas; Rathjens, Denis; Sander, Christian; Schettler, Hannes; Schleper, Peter; Schlieckau, Eike; Schmidt, Alexander; Schröder, Matthias; Schum, Torben; Seidel, Markus; Sibille, Jennifer; Sola, Valentina; Stadie, Hartmut; Steinbrück, Georg; Thomsen, Jan; Vanelderen, Lukas; Barth, Christian; Berger, Joram; Böser, Christian; Chwalek, Thorsten; De Boer, Wim; Descroix, Alexis; Dierlamm, Alexander; Feindt, Michael; Guthoff, Moritz; Hackstein, Christoph; Hartmann, Frank; Hauth, Thomas; Heinrich, Michael; Held, Hauke; Hoffmann, Karl-Heinz; Husemann, Ulrich; Katkov, Igor; Komaragiri, Jyothsna Rani; Lobelle Pardo, Patricia; Martschei, Daniel; Mueller, Steffen; Müller, Thomas; Niegel, Martin; Nürnberg, Andreas; Oberst, Oliver; Oehler, Andreas; Ott, Jochen; Quast, Gunter; Rabbertz, Klaus; Ratnikov, Fedor; Ratnikova, Natalia; Röcker, Steffen; Schilling, Frank-Peter; Schott, Gregory; Simonis, Hans-Jürgen; Stober, Fred-Markus Helmut; Troendle, Daniel; Ulrich, Ralf; Wagner-Kuhr, Jeannine; Wayand, Stefan; Weiler, Thomas; Zeise, Manuel; Anagnostou, Georgios; Daskalakis, Georgios; Geralis, Theodoros; Kesisoglou, Stilianos; Kyriakis, Aristotelis; Loukas, Demetrios; Manolakos, Ioannis; Markou, Athanasios; Markou, Christos; Ntomari, Eleni; Gouskos, Loukas; Mertzimekis, Theodoros; Panagiotou, Apostolos; Saoulidou, Niki; Evangelou, Ioannis; Foudas, Costas; Kokkas, Panagiotis; Manthos, Nikolaos; Papadopoulos, Ioannis; Patras, Vaios; Bencze, Gyorgy; Hajdu, Csaba; Hidas, Pàl; Horvath, Dezso; Sikler, Ferenc; Veszpremi, Viktor; Vesztergombi, Gyorgy; Beni, Noemi; Czellar, Sandor; Molnar, Jozsef; Palinkas, Jozsef; Szillasi, Zoltan; Karancsi, János; Raics, Peter; Trocsanyi, Zoltan Laszlo; Ujvari, Balazs; Beri, Suman Bala; Bhatnagar, Vipin; Dhingra, Nitish; Gupta, Ruchi; Kaur, Manjit; Mehta, Manuk Zubin; Nishu, Nishu; Saini, Lovedeep Kaur; Sharma, Archana; Singh, Jasbir; Kumar, Ashok; Kumar, Arun; Ahuja, Sudha; Bhardwaj, Ashutosh; Choudhary, Brajesh C; Malhotra, Shivali; Naimuddin, Md; Ranjan, Kirti; Sharma, Varun; Shivpuri, Ram Krishen; Banerjee, Sunanda; Bhattacharya, Satyaki; Dutta, Suchandra; Gomber, Bhawna; Jain, Sandhya; Jain, Shilpi; Khurana, Raman; Sarkar, Subir; Sharan, Manoj; Abdulsalam, Abdulla; Dutta, Dipanwita; Kailas, Swaminathan; Kumar, Vineet; Mohanty, Ajit Kumar; Pant, Lalit Mohan; Shukla, Prashant; Aziz, Tariq; Ganguly, Sanmay; Guchait, Monoranjan; Gurtu, Atul; Maity, Manas; Majumder, Gobinda; Mazumdar, Kajari; Mohanty, Gagan Bihari; Parida, Bibhuti; Sudhakar, Katta; Wickramage, Nadeesha; Banerjee, Sudeshna; Dugad, Shashikant; Arfaei, Hessamaddin; Bakhshiansohi, Hamed; Etesami, Seyed Mohsen; Fahim, Ali; Hashemi, Majid; Hesari, Hoda; Jafari, Abideh; Khakzad, Mohsen; Mohammadi Najafabadi, Mojtaba; Paktinat Mehdiabadi, Saeid; Safarzadeh, Batool; Zeinali, Maryam; Abbrescia, Marcello; Barbone, Lucia; Calabria, Cesare; Chhibra, Simranjit Singh; Colaleo, Anna; Creanza, Donato; De Filippis, Nicola; De Palma, Mauro; Fiore, Luigi; Iaselli, Giuseppe; Maggi, Giorgio; Maggi, Marcello; Marangelli, Bartolomeo; My, Salvatore; Nuzzo, Salvatore; Pacifico, Nicola; Pompili, Alexis; Pugliese, Gabriella; Selvaggi, Giovanna; Silvestris, Lucia; Singh, Gurpreet; Venditti, Rosamaria; Verwilligen, Piet; Zito, Giuseppe; Abbiendi, Giovanni; Benvenuti, Alberto; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Grandi, Claudio; Guiducci, Luigi; Marcellini, Stefano; Masetti, Gianni; Meneghelli, Marco; Montanari, Alessandro; Navarria, Francesco; Odorici, Fabrizio; Perrotta, Andrea; Primavera, Federica; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gian Piero; Tosi, Nicolò; Travaglini, Riccardo; Albergo, Sebastiano; Cappello, Gigi; Chiorboli, Massimiliano; Costa, Salvatore; Potenza, Renato; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Frosali, Simone; Gallo, Elisabetta; Gonzi, Sandro; Meschini, Marco; Paoletti, Simone; Sguazzoni, Giacomo; Tropiano, Antonio; Benussi, Luigi; Bianco, Stefano; Colafranceschi, Stefano; Fabbri, Franco; Piccolo, Davide; Fabbricatore, Pasquale; Musenich, Riccardo; Tosi, Silvano; Benaglia, Andrea; De Guio, Federico; Di Matteo, Leonardo; Fiorendi, Sara; Gennai, Simone; Ghezzi, Alessio; Malvezzi, Sandra; Manzoni, Riccardo Andrea; Martelli, Arabella; Massironi, Andrea; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pedrini, Daniele; Ragazzi, Stefano; Redaelli, Nicola; Sala, Silvano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Carrillo Montoya, Camilo Andres; Cavallo, Nicola; De Cosa, Annapaola; Dogangun, Oktay; Fabozzi, Francesco; Iorio, Alberto Orso Maria; Lista, Luca; Meola, Sabino; Merola, Mario; Paolucci, Pierluigi; Azzi, Patrizia; Bacchetta, Nicola; Bisello, Dario; Branca, Antonio; Carlin, Roberto; Checchia, Paolo; Dorigo, Tommaso; Gasparini, Fabrizio; Gasparini, Ugo; Gozzelino, Andrea; Kanishchev, Konstantin; Lacaprara, Stefano; Lazzizzera, Ignazio; Margoni, Martino; Meneguzzo, Anna Teresa; Pazzini, Jacopo; Pozzobon, Nicola; Ronchese, Paolo; Simonetto, Franco; Torassa, Ezio; Tosi, Mia; Vanini, Sara; Zotto, Pierluigi; Zucchetta, Alberto; Zumerle, Gianni; Gabusi, Michele; Ratti, Sergio P; Riccardi, Cristina; Torre, Paola; Vitulo, Paolo; Biasini, Maurizio; Bilei, Gian Mario; Fanò, Livio; Lariccia, Paolo; Mantovani, Giancarlo; Menichelli, Mauro; Nappi, Aniello; Romeo, Francesco; Saha, Anirban; Santocchia, Attilio; Spiezia, Aniello; Taroni, Silvia; Azzurri, Paolo; Bagliesi, Giuseppe; Bernardini, Jacopo; Boccali, Tommaso; Broccolo, Giuseppe; Castaldi, Rino; D'Agnolo, Raffaele Tito; Dell'Orso, Roberto; Fiori, Francesco; Foà, Lorenzo; Giassi, Alessandro; Kraan, Aafke; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Rizzi, Andrea; Serban, Alin Titus; Spagnolo, Paolo; Squillacioti, Paola; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Del Re, Daniele; Diemoz, Marcella; Fanelli, Cristiano; Grassi, Marco; Longo, Egidio; Meridiani, Paolo; Micheli, Francesco; Nourbakhsh, Shervin; Organtini, Giovanni; Paramatti, Riccardo; Rahatlou, Shahram; Sigamani, Michael; Soffi, Livia; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Biino, Cristina; Cartiglia, Nicolo; Casasso, Stefano; Costa, Marco; Demaria, Natale; Mariotti, Chiara; Maselli, Silvia; Migliore, Ernesto; Monaco, Vincenzo; Musich, Marco; Obertino, Maria Margherita; Pastrone, Nadia; Pelliccioni, Mario; Potenza, Alberto; Romero, Alessandra; Ruspa, Marta; Sacchi, Roberto; Solano, Ada; Staiano, Amedeo; Belforte, Stefano; Candelise, Vieri; Casarsa, Massimo; Cossutti, Fabio; Della Ricca, Giuseppe; Gobbo, Benigno; Marone, Matteo; Montanino, Damiana; Penzo, Aldo; Schizzi, Andrea; Kim, Tae Yeon; Nam, Soon-Kwon; Chang, Sunghyun; Kim, Dong Hee; Kim, Gui Nyun; Kong, Dae Jung; Park, Hyangkyu; Son, Dong-Chul; Son, Taejin; Kim, Jae Yool; Kim, Zero Jaeho; Song, Sanghyeon; Choi, Suyong; Gyun, Dooyeon; Hong, Byung-Sik; Jo, Mihee; Kim, Hyunchul; Kim, Tae Jeong; Lee, Kyong Sei; Moon, Dong Ho; Park, Sung Keun; Roh, Youn; Choi, Minkyoo; Kim, Ji Hyun; Park, Chawon; Park, Inkyu; Park, Sangnam; Ryu, Geonmo; Choi, Young-Il; Choi, Young Kyu; Goh, Junghwan; Kim, Min Suk; Kwon, Eunhyang; Lee, Byounghoon; Lee, Jongseok; Lee, Sungeun; Seo, Hyunkwan; Yu, Intae; Bilinskas, Mykolas Jurgis; Grigelionis, Ignas; Janulis, Mindaugas; Juodagalvis, Andrius; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-de La Cruz, Ivan; Lopez-Fernandez, Ricardo; Martínez-Ortega, Jorge; Sánchez Hernández, Alberto; Villasenor-Cendejas, Luis Manuel; Carrillo Moreno, Salvador; Vazquez Valencia, Fabiola; Salazar Ibarguen, Humberto Antonio; Casimiro Linares, Edgar; Morelos Pineda, Antonio; Reyes-Santos, Marco A; Krofcheck, David; Bell, Alan James; Butler, Philip H; Doesburg, Robert; Reucroft, Steve; Silverwood, Hamish; Ahmad, Muhammad; Asghar, Muhammad Irfan; Butt, Jamila; Hoorani, Hafeez R; Khalid, Shoaib; Khan, Wajid Ali; Khurshid, Taimoor; Qazi, Shamona; Shah, Mehar Ali; Shoaib, Muhammad; Bialkowska, Helena; Boimska, Bozena; Frueboes, Tomasz; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Wrochna, Grzegorz; Zalewski, Piotr; Brona, Grzegorz; Bunkowski, Karol; Cwiok, Mikolaj; Dominik, Wojciech; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Misiura, Maciej; Almeida, Nuno; Bargassa, Pedrame; David Tinoco Mendes, Andre; Faccioli, Pietro; Ferreira Parracho, Pedro Guilherme; Gallinaro, Michele; Seixas, Joao; Varela, Joao; Vischia, Pietro; Belotelov, Ivan; Bunin, Pavel; Golutvin, Igor; Gorbunov, Ilya; Kamenev, Alexey; Karjavin, Vladimir; Kozlov, Guennady; Lanev, Alexander; Malakhov, Alexander; Moisenz, Petr; Palichik, Vladimir; Perelygin, Victor; Savina, Maria; Shmatov, Sergey; Smirnov, Vitaly; Volodko, Anton; Zarubin, Anatoli; Evstyukhin, Sergey; Golovtsov, Victor; Ivanov, Yury; Kim, Victor; Levchenko, Petr; Murzin, Victor; Oreshkin, Vadim; Smirnov, Igor; Sulimov, Valentin; Uvarov, Lev; Vavilov, Sergey; Vorobyev, Alexey; Vorobyev, Andrey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Kirsanov, Mikhail; Krasnikov, Nikolai; Matveev, Viktor; Pashenkov, Anatoli; Tlisov, Danila; Toropin, Alexander; Epshteyn, Vladimir; Erofeeva, Maria; Gavrilov, Vladimir; Kossov, Mikhail; Lychkovskaya, Natalia; Popov, Vladimir; Safronov, Grigory; Semenov, Sergey; Shreyber, Irina; Stolin, Viatcheslav; Vlasov, Evgueni; Zhokin, Alexander; Belyaev, Andrey; Boos, Edouard; Dubinin, Mikhail; Dudko, Lev; Ershov, Alexander; Gribushin, Andrey; Klyukhin, Vyacheslav; Kodolova, Olga; Lokhtin, Igor; Markina, Anastasia; Obraztsov, Stepan; Perfilov, Maxim; Petrushanko, Sergey; Popov, Andrey; Sarycheva, Ludmila; Savrin, Viktor; Snigirev, Alexander; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Leonidov, Andrey; Mesyats, Gennady; Rusakov, Sergey V; Vinogradov, Alexey; Azhgirey, Igor; Bayshev, Igor; Bitioukov, Sergei; Grishin, Viatcheslav; Kachanov, Vassili; Konstantinov, Dmitri; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Sobol, Andrei; Tourtchanovitch, Leonid; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Djordjevic, Milos; Ekmedzic, Marko; Krpic, Dragomir; Milosevic, Jovan; Aguilar-Benitez, Manuel; Alcaraz Maestre, Juan; Arce, Pedro; Battilana, Carlo; Calvo, Enrique; Cerrada, Marcos; Chamizo Llatas, Maria; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Domínguez Vázquez, Daniel; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Ferrando, Antonio; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Merino, Gonzalo; Puerta Pelayo, Jesus; Quintario Olmeda, Adrián; Redondo, Ignacio; Romero, Luciano; Santaolalla, Javier; Senghi Soares, Mara; Willmott, Carlos; Albajar, Carmen; Codispoti, Giuseppe; de Trocóniz, Jorge F; Brun, Hugues; Cuevas, Javier; Fernandez Menendez, Javier; Folgueras, Santiago; Gonzalez Caballero, Isidro; Lloret Iglesias, Lara; Piedra Gomez, Jonatan; Brochero Cifuentes, Javier Andres; Cabrillo, Iban Jose; Calderon, Alicia; Chuang, Shan-Huei; Duarte Campderros, Jordi; Felcini, Marta; Fernandez, Marcos; Gomez, Gervasio; Gonzalez Sanchez, Javier; Graziano, Alberto; Jorda, Clara; Lopez Virto, Amparo; Marco, Jesus; Marco, Rafael; Martinez Rivero, Celso; Matorras, Francisco; Munoz Sanchez, Francisca Javiela; Rodrigo, Teresa; Rodríguez-Marrero, Ana Yaiza; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Auzinger, Georg; Bachtis, Michail; Baillon, Paul; Ball, Austin; Barney, David; Benitez, Jose F; Bernet, Colin; Bianchi, Giovanni; Bloch, Philippe; Bocci, Andrea; Bonato, Alessio; Botta, Cristina; Breuker, Horst; Camporesi, Tiziano; Cerminara, Gianluca; Christiansen, Tim; Coarasa Perez, Jose Antonio; D'Enterria, David; Dabrowski, Anne; De Roeck, Albert; Di Guida, Salvatore; Dobson, Marc; Dupont-Sagorin, Niels; Elliott-Peisert, Anna; Frisch, Benjamin; Funk, Wolfgang; Georgiou, Georgios; Giffels, Manuel; Gigi, Dominique; Gill, Karl; Giordano, Domenico; Girone, Maria; Giunta, Marina; Glege, Frank; Gomez-Reino Garrido, Robert; Govoni, Pietro; Gowdy, Stephen; Guida, Roberto; Gundacker, Stefan; Hammer, Josef; Hansen, Magnus; Harris, Philip; Hartl, Christian; Harvey, John; Hegner, Benedikt; Hinzmann, Andreas; Innocente, Vincenzo; Janot, Patrick; Kaadze, Ketino; Karavakis, Edward; Kousouris, Konstantinos; Lecoq, Paul; Lee, Yen-Jie; Lenzi, Piergiulio; Lourenco, Carlos; Magini, Nicolo; Maki, Tuula; Malberti, Martina; Malgeri, Luca; Mannelli, Marcello; Masetti, Lorenzo; Meijers, Frans; Mersi, Stefano; Meschi, Emilio; Moser, Roland; Mozer, Matthias Ulrich; Mulders, Martijn; Musella, Pasquale; Nesvold, Erik; Orsini, Luciano; Palencia Cortezon, Enrique; Perez, Emmanuelle; Perrozzi, Luca; Petrilli, Achille; Pfeiffer, Andreas; Pierini, Maurizio; Pimiä, Martti; Piparo, Danilo; Polese, Giovanni; Quertenmont, Loic; Racz, Attila; Reece, William; Rodrigues Antunes, Joao; Rolandi, Gigi; Rovelli, Chiara; Rovere, Marco; Sakulin, Hannes; Santanastasio, Francesco; Schäfer, Christoph; Schwick, Christoph; Segoni, Ilaria; Sekmen, Sezen; Sharma, Archana; Siegrist, Patrice; Silva, Pedro; Simon, Michal; Sphicas, Paraskevas; Spiga, Daniele; Tsirou, Andromachi; Veres, Gabor Istvan; Vlimant, Jean-Roch; Wöhri, Hermine Katharina; Worm, Steven; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Gabathuler, Kurt; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; König, Stefan; Kotlinski, Danek; Langenegger, Urs; Meier, Frank; Renker, Dieter; Rohe, Tilman; Bäni, Lukas; Bortignon, Pierluigi; Buchmann, Marco-Andrea; Casal, Bruno; Chanon, Nicolas; Deisher, Amanda; Dissertori, Günther; Dittmar, Michael; Donegà, Mauro; Dünser, Marc; Eller, Philipp; Eugster, Jürg; Freudenreich, Klaus; Grab, Christoph; Hits, Dmitry; Lecomte, Pierre; Lustermann, Werner; Marini, Andrea Carlo; Martinez Ruiz del Arbol, Pablo; Mohr, Niklas; Moortgat, Filip; Nägeli, Christoph; Nef, Pascal; Nessi-Tedaldi, Francesca; Pandolfi, Francesco; Pape, Luc; Pauss, Felicitas; Peruzzi, Marco; Ronga, Frederic Jean; Rossini, Marco; Sala, Leonardo; Sanchez, Ann - Karin; Starodumov, Andrei; Stieger, Benjamin; Takahashi, Maiko; Tauscher, Ludwig; Thea, Alessandro; Theofilatos, Konstantinos; Treille, Daniel; Urscheler, Christina; Wallny, Rainer; Weber, Hannsjoerg Artur; Wehrli, Lukas; Amsler, Claude; Chiochia, Vincenzo; De Visscher, Simon; Favaro, Carlotta; Ivova Rikova, Mirena; Kilminster, Benjamin; Millan Mejias, Barbara; Otiougova, Polina; Robmann, Peter; Snoek, Hella; Tupputi, Salvatore; Verzetti, Mauro; Chang, Yuan-Hann; Chen, Kuan-Hsin; Ferro, Cristina; Kuo, Chia-Ming; Li, Syue-Wei; Lin, Willis; Lu, Yun-Ju; Singh, Anil; Volpe, Roberta; Yu, Shin-Shan; Bartalini, Paolo; Chang, Paoti; Chang, You-Hao; Chang, Yu-Wei; Chao, Yuan; Chen, Kai-Feng; Dietz, Charles; Grundler, Ulysses; Hou, George Wei-Shu; Hsiung, Yee; Kao, Kai-Yi; Lei, Yeong-Jyi; Lu, Rong-Shyang; Majumder, Devdatta; Petrakou, Eleni; Shi, Xin; Shiu, Jing-Ge; Tzeng, Yeng-Ming; Wan, Xia; Wang, Minzu; Asavapibhop, Burin; Srimanobhas, Norraphat; Adiguzel, Aytul; Bakirci, Mustafa Numan; Cerci, Salim; Dozen, Candan; Dumanoglu, Isa; Eskut, Eda; Girgis, Semiray; Gokbulut, Gul; Gurpinar, Emine; Hos, Ilknur; Kangal, Evrim Ersin; Karaman, Turker; Karapinar, Guler; Kayis Topaksu, Aysel; Onengut, Gulsen; Ozdemir, Kadri; Ozturk, Sertac; Polatoz, Ayse; Sogut, Kenan; Sunar Cerci, Deniz; Tali, Bayram; Topakli, Huseyin; Vergili, Latife Nukhet; Vergili, Mehmet; Akin, Ilina Vasileva; Aliev, Takhmasib; Bilin, Bugra; Bilmis, Selcuk; Deniz, Muhammed; Gamsizkan, Halil; Guler, Ali Murat; Ocalan, Kadir; Ozpineci, Altug; Serin, Meltem; Sever, Ramazan; Surat, Ugur Emrah; Yalvac, Metin; Yildirim, Eda; Zeyrek, Mehmet; Gülmez, Erhan; Isildak, Bora; Kaya, Mithat; Kaya, Ozlem; Ozkorucuklu, Suat; Sonmez, Nasuf; Cankocak, Kerem; Levchuk, Leonid; Brooke, James John; Clement, Emyr; Cussans, David; Flacher, Henning; Frazier, Robert; Goldstein, Joel; Grimes, Mark; Heath, Greg P; Heath, Helen F; Kreczko, Lukasz; Metson, Simon; Newbold, Dave M; Nirunpong, Kachanon; Poll, Anthony; Senkin, Sergey; Smith, Vincent J; Williams, Thomas; Basso, Lorenzo; Bell, Ken W; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Jackson, James; Kennedy, Bruce W; Olaiya, Emmanuel; Petyt, David; Radburn-Smith, Benjamin Charles; Shepherd-Themistocleous, Claire; Tomalin, Ian R; Womersley, William John; Bainbridge, Robert; Ball, Gordon; Beuselinck, Raymond; Buchmuller, Oliver; Colling, David; Cripps, Nicholas; Cutajar, Michael; Dauncey, Paul; Davies, Gavin; Della Negra, Michel; Ferguson, William; Fulcher, Jonathan; Futyan, David; Gilbert, Andrew; Guneratne Bryer, Arlo; Hall, Geoffrey; Hatherell, Zoe; Hays, Jonathan; Iles, Gregory; Jarvis, Martyn; Karapostoli, Georgia; Lyons, Louis; Magnan, Anne-Marie; Marrouche, Jad; Mathias, Bryn; Nandi, Robin; Nash, Jordan; Nikitenko, Alexander; Pela, Joao; Pesaresi, Mark; Petridis, Konstantinos; Pioppi, Michele; Raymond, David Mark; Rogerson, Samuel; Rose, Andrew; Ryan, Matthew John; Seez, Christopher; Sharp, Peter; Sparrow, Alex; Stoye, Markus; Tapper, Alexander; Vazquez Acosta, Monica; Virdee, Tejinder; Wakefield, Stuart; Wardle, Nicholas; Whyntie, Tom; Chadwick, Matthew; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Leggat, Duncan; Leslie, Dawn; Martin, William; Reid, Ivan; Symonds, Philip; Teodorescu, Liliana; Turner, Mark; Hatakeyama, Kenichi; Liu, Hongxuan; Scarborough, Tara; Charaf, Otman; Henderson, Conor; Rumerio, Paolo; Avetisyan, Aram; Bose, Tulika; Fantasia, Cory; Heister, Arno; St John, Jason; Lawson, Philip; Lazic, Dragoslav; Rohlf, James; Sperka, David; Sulak, Lawrence; Alimena, Juliette; Bhattacharya, Saptaparna; Christopher, Grant; Cutts, David; Demiragli, Zeynep; Ferapontov, Alexey; Garabedian, Alex; Heintz, Ulrich; Jabeen, Shabnam; Kukartsev, Gennadiy; Laird, Edward; Landsberg, Greg; Luk, Michael; Narain, Meenakshi; Nguyen, Duong; Segala, Michael; Sinthuprasith, Tutanon; Speer, Thomas; Breedon, Richard; Breto, Guillermo; Calderon De La Barca Sanchez, Manuel; Chauhan, Sushil; Chertok, Maxwell; Conway, John; Conway, Rylan; Cox, Peter Timothy; Dolen, James; Erbacher, Robin; Gardner, Michael; Houtz, Rachel; Ko, Winston; Kopecky, Alexandra; Lander, Richard; Mall, Orpheus; Miceli, Tia; Pellett, Dave; Ricci-Tam, Francesca; Rutherford, Britney; Searle, Matthew; Smith, John; Squires, Michael; Tripathi, Mani; Vasquez Sierra, Ricardo; Yohay, Rachel; Andreev, Valeri; Cline, David; Cousins, Robert; Duris, Joseph; Erhan, Samim; Everaerts, Pieter; Farrell, Chris; Hauser, Jay; Ignatenko, Mikhail; Jarvis, Chad; Rakness, Gregory; Schlein, Peter; Traczyk, Piotr; Valuev, Vyacheslav; Weber, Matthias; Babb, John; Clare, Robert; Dinardo, Mauro Emanuele; Ellison, John Anthony; Gary, J William; Giordano, Ferdinando; Hanson, Gail; Liu, Hongliang; Long, Owen Rosser; Luthra, Arun; Nguyen, Harold; Paramesvaran, Sudarshan; Sturdy, Jared; Sumowidagdo, Suharyo; Wilken, Rachel; Wimpenny, Stephen; Andrews, Warren; Branson, James G; Cerati, Giuseppe Benedetto; Cittolin, Sergio; Evans, David; Holzner, André; Kelley, Ryan; Lebourgeois, Matthew; Letts, James; Macneill, Ian; Mangano, Boris; Padhi, Sanjay; Palmer, Christopher; Petrucciani, Giovanni; Pieri, Marco; Sani, Matteo; Sharma, Vivek; Simon, Sean; Sudano, Elizabeth; Tadel, Matevz; Tu, Yanjun; Vartak, Adish; Wasserbaech, Steven; Würthwein, Frank; Yagil, Avraham; Yoo, Jaehyeok; Barge, Derek; Bellan, Riccardo; Campagnari, Claudio; D'Alfonso, Mariarosaria; Danielson, Thomas; Flowers, Kristen; Geffert, Paul; Golf, Frank; Incandela, Joe; Justus, Christopher; Kalavase, Puneeth; Kovalskyi, Dmytro; Krutelyov, Vyacheslav; Lowette, Steven; Magaña Villalba, Ricardo; Mccoll, Nickolas; Pavlunin, Viktor; Ribnik, Jacob; Richman, Jeffrey; Rossin, Roberto; Stuart, David; To, Wing; West, Christopher; Apresyan, Artur; Bornheim, Adolf; Chen, Yi; Di Marco, Emanuele; Duarte, Javier; Gataullin, Marat; Ma, Yousi; Mott, Alexander; Newman, Harvey B; Rogan, Christopher; Spiropulu, Maria; Timciuc, Vladlen; Veverka, Jan; Wilkinson, Richard; Xie, Si; Yang, Yong; Zhu, Ren-Yuan; Azzolini, Virginia; Calamba, Aristotle; Carroll, Ryan; Ferguson, Thomas; Iiyama, Yutaro; Jang, Dong Wook; Liu, Yueh-Feng; Paulini, Manfred; Vogel, Helmut; Vorobiev, Igor; Cumalat, John Perry; Drell, Brian Robert; Ford, William T; Gaz, Alessandro; Luiggi Lopez, Eduardo; Smith, James; Stenson, Kevin; Ulmer, Keith; Wagner, Stephen Robert; Alexander, James; Chatterjee, Avishek; Eggert, Nicholas; Gibbons, Lawrence Kent; Heltsley, Brian; Hopkins, Walter; Khukhunaishvili, Aleko; Kreis, Benjamin; Mirman, Nathan; Nicolas Kaufman, Gala; Patterson, Juliet Ritchie; Ryd, Anders; Salvati, Emmanuele; Sun, Werner; Teo, Wee Don; Thom, Julia; Thompson, Joshua; Tucker, Jordan; Vaughan, Jennifer; Weng, Yao; Winstrom, Lucas; Wittich, Peter; Winn, Dave; Abdullin, Salavat; Albrow, Michael; Anderson, Jacob; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Burkett, Kevin; Butler, Joel Nathan; Chetluru, Vasundhara; Cheung, Harry; Chlebana, Frank; Elvira, Victor Daniel; Fisk, Ian; Freeman, Jim; Gao, Yanyan; Green, Dan; Gutsche, Oliver; Hanlon, Jim; Harris, Robert M; Hirschauer, James; Hooberman, Benjamin; Jindariani, Sergo; Johnson, Marvin; Joshi, Umesh; Klima, Boaz; Kunori, Shuichi; Kwan, Simon; Leonidopoulos, Christos; Linacre, Jacob; Lincoln, Don; Lipton, Ron; Lykken, Joseph; Maeshima, Kaori; Marraffino, John Michael; Maruyama, Sho; Mason, David; McBride, Patricia; Mishra, Kalanand; Mrenna, Stephen; Musienko, Yuri; Newman-Holmes, Catherine; O'Dell, Vivian; Prokofyev, Oleg; Sexton-Kennedy, Elizabeth; Sharma, Seema; Spalding, William J; Spiegel, Leonard; Taylor, Lucas; Tkaczyk, Slawek; Tran, Nhan Viet; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vidal, Richard; Whitmore, Juliana; Wu, Weimin; Yang, Fan; Yun, Jae Chul; Acosta, Darin; Avery, Paul; Bourilkov, Dimitri; Chen, Mingshui; Cheng, Tongguang; Das, Souvik; De Gruttola, Michele; Di Giovanni, Gian Piero; Dobur, Didar; Drozdetskiy, Alexey; Field, Richard D; Fisher, Matthew; Fu, Yu; Furic, Ivan-Kresimir; Gartner, Joseph; Hugon, Justin; Kim, Bockjoo; Konigsberg, Jacobo; Korytov, Andrey; Kropivnitskaya, Anna; Kypreos, Theodore; Low, Jia Fu; Matchev, Konstantin; Milenovic, Predrag; Mitselmakher, Guenakh; Muniz, Lana; Park, Myeonghun; Remington, Ronald; Rinkevicius, Aurelijus; Sellers, Paul; Skhirtladze, Nikoloz; Snowball, Matthew; Yelton, John; Zakaria, Mohammed; Gaultney, Vanessa; Hewamanage, Samantha; Lebolo, Luis Miguel; Linn, Stephan; Markowitz, Pete; Martinez, German; Rodriguez, Jorge Luis; Adams, Todd; Askew, Andrew; Bochenek, Joseph; Chen, Jie; Diamond, Brendan; Gleyzer, Sergei V; Haas, Jeff; Hagopian, Sharon; Hagopian, Vasken; Jenkins, Merrill; Johnson, Kurtis F; Prosper, Harrison; Veeraraghavan, Venkatesh; Weinberg, Marc; Baarmand, Marc M; Dorney, Brian; Hohlmann, Marcus; Kalakhety, Himali; Vodopiyanov, Igor; Yumiceva, Francisco; Adams, Mark Raymond; Anghel, Ioana Maria; Apanasevich, Leonard; Bai, Yuting; Bazterra, Victor Eduardo; Betts, Russell Richard; Bucinskaite, Inga; Callner, Jeremy; Cavanaugh, Richard; Evdokimov, Olga; Gauthier, Lucie; Gerber, Cecilia Elena; Hofman, David Jonathan; Khalatyan, Samvel; Lacroix, Florent; O'Brien, Christine; Silkworth, Christopher; Strom, Derek; Turner, Paul; Varelas, Nikos; Akgun, Ugur; Albayrak, Elif Asli; Bilki, Burak; Clarida, Warren; Duru, Firdevs; Griffiths, Scott; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Newsom, Charles Ray; Norbeck, Edwin; Onel, Yasar; Ozok, Ferhat; Sen, Sercan; Tan, Ping; Tiras, Emrah; Wetzel, James; Yetkin, Taylan; Yi, Kai; Barnett, Bruce Arnold; Blumenfeld, Barry; Bolognesi, Sara; Fehling, David; Giurgiu, Gavril; Gritsan, Andrei; Guo, Zijin; Hu, Guofan; Maksimovic, Petar; Swartz, Morris; Whitbeck, Andrew; Baringer, Philip; Bean, Alice; Benelli, Gabriele; Kenny Iii, Raymond Patrick; Murray, Michael; Noonan, Daniel; Sanders, Stephen; Stringer, Robert; Tinti, Gemma; Wood, Jeffrey Scott; Barfuss, Anne-Fleur; Bolton, Tim; Chakaberia, Irakli; Ivanov, Andrew; Khalil, Sadia; Makouski, Mikhail; Maravin, Yurii; Shrestha, Shruti; Svintradze, Irakli; Gronberg, Jeffrey; Lange, David; Rebassoo, Finn; Wright, Douglas; Baden, Drew; Calvert, Brian; Eno, Sarah Catherine; Gomez, Jaime; Hadley, Nicholas John; Kellogg, Richard G; Kirn, Malina; Kolberg, Ted; Lu, Ying; Marionneau, Matthieu; Mignerey, Alice; Pedro, Kevin; Peterman, Alison; Skuja, Andris; Temple, Jeffrey; Tonjes, Marguerite; Tonwar, Suresh C; Apyan, Aram; Bauer, Gerry; Bendavid, Joshua; Busza, Wit; Butz, Erik; Cali, Ivan Amos; Chan, Matthew; Dutta, Valentina; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Kim, Yongsun; Klute, Markus; Krajczar, Krisztian; Levin, Andrew; Luckey, Paul David; Ma, Teng; Nahn, Steve; Paus, Christoph; Ralph, Duncan; Roland, Christof; Roland, Gunther; Rudolph, Matthew; Stephans, George; Stöckli, Fabian; Sumorok, Konstanty; Sung, Kevin; Velicanu, Dragos; Wenger, Edward Allen; Wolf, Roger; Wyslouch, Bolek; Yang, Mingming; Yilmaz, Yetkin; Yoon, Sungho; Zanetti, Marco; Zhukova, Victoria; Cooper, Seth; Dahmes, Bryan; De Benedetti, Abraham; Franzoni, Giovanni; Gude, Alexander; Kao, Shih-Chuan; Klapoetke, Kevin; Kubota, Yuichi; Mans, Jeremy; Pastika, Nathaniel; Rusack, Roger; Sasseville, Michael; Singovsky, Alexander; Tambe, Norbert; Turkewitz, Jared; Cremaldi, Lucien Marcus; Kroeger, Rob; Perera, Lalith; Rahmat, Rahmat; Sanders, David A; Avdeeva, Ekaterina; Bloom, Kenneth; Bose, Suvadeep; Claes, Daniel R; Dominguez, Aaron; Eads, Michael; Keller, Jason; Kravchenko, Ilya; Lazo-Flores, Jose; Malik, Sudhir; Snow, Gregory R; Godshalk, Andrew; Iashvili, Ia; Jain, Supriya; Kharchilava, Avto; Kumar, Ashish; Rappoccio, Salvatore; Alverson, George; Barberis, Emanuela; Baumgartel, Darin; Chasco, Matthew; Haley, Joseph; Nash, David; Orimoto, Toyoko; Trocino, Daniele; Wood, Darien; Zhang, Jinzhong; Anastassov, Anton; Hahn, Kristan Allan; Kubik, Andrew; Lusito, Letizia; Mucia, Nicholas; Odell, Nathaniel; Ofierzynski, Radoslaw Adrian; Pollack, Brian; Pozdnyakov, Andrey; Schmitt, Michael Henry; Stoynev, Stoyan; Velasco, Mayda; Won, Steven; Antonelli, Louis; Berry, Douglas; Brinkerhoff, Andrew; Chan, Kwok Ming; Hildreth, Michael; Jessop, Colin; Karmgard, Daniel John; Kolb, Jeff; Lannon, Kevin; Luo, Wuming; Lynch, Sean; Marinelli, Nancy; Morse, David Michael; Pearson, Tessa; Planer, Michael; Ruchti, Randy; Slaunwhite, Jason; Valls, Nil; Wayne, Mitchell; Wolf, Matthias; Bylsma, Ben; Durkin, Lloyd Stanley; Hill, Christopher; Hughes, Richard; Kotov, Khristian; Ling, Ta-Yung; Puigh, Darren; Rodenburg, Marissa; Vuosalo, Carl; Williams, Grayson; Winer, Brian L; Berry, Edmund; Elmer, Peter; Halyo, Valerie; Hebda, Philip; Hegeman, Jeroen; Hunt, Adam; Jindal, Pratima; Koay, Sue Ann; Lopes Pegna, David; Lujan, Paul; Marlow, Daniel; Medvedeva, Tatiana; Mooney, Michael; Olsen, James; Piroué, Pierre; Quan, Xiaohang; Raval, Amita; Saka, Halil; Stickland, David; Tully, Christopher; Werner, Jeremy Scott; Zuranski, Andrzej; Brownson, Eric; Lopez, Angel; Mendez, Hector; Ramirez Vargas, Juan Eduardo; Alagoz, Enver; Barnes, Virgil E; Benedetti, Daniele; Bolla, Gino; Bortoletto, Daniela; De Mattia, Marco; Everett, Adam; Hu, Zhen; Jones, Matthew; Koybasi, Ozhan; Kress, Matthew; Laasanen, Alvin T; Leonardo, Nuno; Maroussov, Vassili; Merkel, Petra; Miller, David Harry; Neumeister, Norbert; Shipsey, Ian; Silvers, David; Svyatkovskiy, Alexey; Vidal Marono, Miguel; Yoo, Hwi Dong; Zablocki, Jakub; Zheng, Yu; Guragain, Samir; Parashar, Neeti; Adair, Antony; Akgun, Bora; Boulahouache, Chaouki; Ecklund, Karl Matthew; Geurts, Frank JM; Li, Wei; Padley, Brian Paul; Redjimi, Radia; Roberts, Jay; Zabel, James; Betchart, Burton; Bodek, Arie; Chung, Yeon Sei; Covarelli, Roberto; de Barbaro, Pawel; Demina, Regina; Eshaq, Yossof; Ferbel, Thomas; Garcia-Bellido, Aran; Goldenzweig, Pablo; Han, Jiyeon; Harel, Amnon; Miner, Daniel Carl; Vishnevskiy, Dmitry; Zielinski, Marek; Bhatti, Anwar; Ciesielski, Robert; Demortier, Luc; Goulianos, Konstantin; Lungu, Gheorghe; Malik, Sarah; Mesropian, Christina; Arora, Sanjay; Barker, Anthony; Chou, John Paul; Contreras-Campana, Christian; Contreras-Campana, Emmanuel; Duggan, Daniel; Ferencek, Dinko; Gershtein, Yuri; Gray, Richard; Halkiadakis, Eva; Hidas, Dean; Lath, Amitabh; Panwalkar, Shruti; Park, Michael; Patel, Rishi; Rekovic, Vladimir; Robles, Jorge; Rose, Keith; Salur, Sevil; Schnetzer, Steve; Seitz, Claudia; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Walker, Matthew; Cerizza, Giordano; Hollingsworth, Matthew; Spanier, Stefan; Yang, Zong-Chang; York, Andrew; Eusebi, Ricardo; Flanagan, Will; Gilmore, Jason; Kamon, Teruki; Khotilovich, Vadim; Montalvo, Roy; Osipenkov, Ilya; Pakhotin, Yuriy; Perloff, Alexx; Roe, Jeffrey; Safonov, Alexei; Sakuma, Tai; Sengupta, Sinjini; Suarez, Indara; Tatarinov, Aysen; Toback, David; Akchurin, Nural; Damgov, Jordan; Dragoiu, Cosmin; Dudero, Phillip Russell; Jeong, Chiyoung; Kovitanggoon, Kittikul; Lee, Sung Won; Libeiro, Terence; Volobouev, Igor; Appelt, Eric; Delannoy, Andrés G; Florez, Carlos; Greene, Senta; Gurrola, Alfredo; Johns, Willard; Kurt, Pelin; Maguire, Charles; Melo, Andrew; Sharma, Monika; Sheldon, Paul; Snook, Benjamin; Tuo, Shengquan; Velkovska, Julia; Arenton, Michael Wayne; Balazs, Michael; Boutle, Sarah; Cox, Bradley; Francis, Brian; Goodell, Joseph; Hirosky, Robert; Ledovskoy, Alexander; Lin, Chuanzhe; Neu, Christopher; Wood, John; Gollapinni, Sowjanya; Harr, Robert; Karchin, Paul Edmund; Kottachchi Kankanamge Don, Chamath; Lamichhane, Pramod; Sakharov, Alexandre; Anderson, Michael; Belknap, Donald; Borrello, Laura; Carlsmith, Duncan; Cepeda, Maria; Dasu, Sridhara; Friis, Evan; Gray, Lindsey; Grogg, Kira Suzanne; Grothe, Monika; Hall-Wilton, Richard; Herndon, Matthew; Hervé, Alain; Klabbers, Pamela; Klukas, Jeffrey; Lanaro, Armando; Lazaridis, Christos; Loveless, Richard; Mohapatra, Ajit; Ojalvo, Isabel; Palmonari, Francesco; Pierro, Giuseppe Antonio; Ross, Ian; Savin, Alexander; Smith, Wesley H; Swanson, Joshua
2013-04-02
In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4.98 inverse femtobarns of proton-proton collisions at the center of mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (missing ET > 40 GeV) and total hadronic transverse energy (HT > 120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observation, yielding limits in the context of the constrained mininal supersymmetric standard model and on a set of simplified models.
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Flood forecasting within urban drainage systems using NARX neural network.
Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi
2017-11-01
Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.
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A Neural Network Approach to Muon Triggering in ATLAS
Livneh, Ran; CERN. Geneva
2007-01-01
The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to make precise decisions in a few nano-seconds. This poses a complicated inverse problem, arising from the inhomogeneous nature of the magnetic fields in ATLAS. This thesis presents a study of an application of Artificial Neural Networks to the muon triggering problem in the ATLAS end-cap. A comparison with realistic results from the ATLAS first level trigger simulation was in favour of the neural network, but this is mainly due to superior resolution available off-line. Other options for applying a neural network to this problem are discussed.
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Neural crest cells: from developmental biology to clinical interventions.
Noisa, Parinya; Raivio, Taneli
2014-09-01
Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.
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The neural correlates of cognitive reappraisal during emotional autobiographical memory recall.
Holland, Alisha C; Kensinger, Elizabeth A
2013-01-01
We used fMRI to investigate the neural processes engaged as individuals down- and up-regulated the emotions associated with negative autobiographical memories (AMs) using cognitive reappraisal strategies. Our analyses examined neural activity during three separate phases, as participants (a) viewed a reappraisal instruction (i.e., Decrease, Increase, Maintain), (b) searched for an AM referenced by a self-generated cue, and (c) elaborated upon the details of the AM being held in mind. Decreasing emotional intensity primarily engaged activity in regions previously implicated in cognitive control (e.g., dorsal and ventral lateral pFC), emotion generation and processing (e.g., amygdala, insula), and visual imagery (e.g., precuneus) as participants searched for and retrieved events. In contrast, increasing emotional intensity engaged similar regions during the instruction phase (i.e., before a memory cue was presented) and again as individuals later elaborated upon the details of the events they had recalled. These findings confirm that reappraisal can modulate neural activity during the recall of personally relevant events, although the time course of this modulation appears to depend on whether individuals are attempting to down- or up-regulate their emotions.
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Construction and Updating of Event Models in Auditory Event Processing
Huff, Markus; Maurer, Annika E.; Brich, Irina; Pagenkopf, Anne; Wickelmaier, Florian; Papenmeier, Frank
2018-01-01
Humans segment the continuous stream of sensory information into distinct events at points of change. Between 2 events, humans perceive an event boundary. Present theories propose changes in the sensory information to trigger updating processes of the present event model. Increased encoding effort finally leads to a memory benefit at event…
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Estimation of neural energy in microelectrode signals
Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.
2004-09-01
We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.
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Tracking and vertex finding with drift chambers and neural networks
International Nuclear Information System (INIS)
Lindsey, C.
1991-09-01
Finding tracks, track vertices and event vertices with neural networks from drift chamber signals is discussed. Simulated feed-forward neural networks have been trained with back-propagation to give track parameters using Monte Carlo simulated tracks in one case and actual experimental data in another. Effects on network performance of limited weight resolution, noise and drift chamber resolution are given. Possible implementations in hardware are discussed. 7 refs., 10 figs
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Morelli, Valentina; Palmieri, Serena; Lania, Andrea; Tresoldi, Alberto; Corbetta, Sabrina; Cairoli, Elisa; Eller-Vainicher, Cristina; Arosio, Maura; Copetti, Massimiliano; Grossi, Enzo; Chiodini, Iacopo
2017-07-01
The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incidentaloma (AI) by standard statistics and artificial neural networks (ANNs). We analyzed a retrospective record of 518 AI patients. Data regarding cortisol levels after 1 mg dexamethasone suppression (1 mg DST) and the presence of obesity (OB), hypertension (AH), type-2 diabetes (T2DM), dyslipidemia (DL), familial CVE history, smoking habit and CVE were collected. The receiver-operating characteristic curve analysis suggested that 1 mg DST, at a cut-off of 1.8 µg/dL, had the best accuracy for detecting patients with increased CVE risk. In patients with 1 mg-DST ≥1.8 µg/dL (DST+, n = 223), age and prevalence of AH, T2DM, DL and CVE (66 years, 74.5, 25.9, 41.4 and 26.8% respectively) were higher than that of patients with 1 mg-DST ≤1.8 µg/dL (61.9 years, 60.7, 18.5, 32.9 and 10%, respectively, P Cortisol after 1 mg-DST is independently associated with the CVE occurrence. The ANNs might help for assessing the CVE risk in AI patients. © 2017 European Society of Endocrinology.
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Directory of Open Access Journals (Sweden)
Virginie eBonnamain
2012-04-01
Full Text Available Neural transplantation is a promising therapeutic strategy for neurodegenerative diseases and other affections of the central nervous system (CNS like Parkinson and Huntington diseases, multiple sclerosis or stroke. If cell replacement therapy already went through clinical trials for some of these diseases using fetal human neuroblasts, several important limitations led to the search for alternative cell sources that would be more suitable for intracerebral transplantation. Taking into account logistical and ethical issues linked to the use of tissue derived from human fetuses, and the immunologically special status of the CNS allowing the occurrence of deleterious immune reactions, Neural Stem/Progenitor Cells (NSPCs appear as an interesting cell source candidate. In addition to their ability for replacing cell populations lost during the pathological events, NSPCs also display surprising therapeutic effects of neuroprotection and immunomodulation. A better knowledge of the mechanisms involved in these specific characteristics will hopefully lead in the future to a successful use of NSPCs in regenerative medicine for CNS affections.
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Furley, Philip; Schnuerch, Robert; Gibbons, Henning
2017-08-01
Observers of sports can reliably estimate who is leading or trailing based on nonverbal cues. Most likely, this is due to an adaptive mechanism of detecting motivationally relevant signals such as high status, superiority, and dominance. We reasoned that the relevance of leading athletes should lead to a sustained attentional prioritization. To test this idea, we recorded electroencephalography while 45 participants saw brief stills of athletes and estimated whether they were leading or trailing. Based on these recordings, we assessed event-related potentials and focused on the late positive complex (LPC), a well-established signature of controlled attention to motivationally relevant visual stimuli. Confirming our expectation, we found that LPC amplitude was significantly enhanced for leading as compared to trailing athletes. Moreover, this modulation was significantly related to behavioral performance on the score-estimation task. The present data suggest that subtle cues related to athletic supremacy are reliably differentiated in the human brain, involving a strong attentional orienting toward leading athletes. This mechanism might be part of an adaptive cognitive strategy that guides human social behavior.
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Neural control of magnetic suspension systems
Gray, W. Steven
1993-01-01
The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.
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Directory of Open Access Journals (Sweden)
Monica Dhar
Full Text Available The goal of the present study was to shed light on the respective contributions of three important action monitoring brain regions (i.e. cingulate cortex, insula, and orbitofrontal cortex during the conscious detection of response errors. To this end, fourteen healthy adults performed a speeded Go/Nogo task comprising Nogo trials of varying levels of difficulty, designed to elicit aware and unaware errors. Error awareness was indicated by participants with a second key press after the target key press. Meanwhile, electromyogram (EMG from the response hand was recorded in addition to high-density scalp electroencephalogram (EEG. In the EMG-locked grand averages, aware errors clearly elicited an error-related negativity (ERN reflecting error detection, and a later error positivity (Pe reflecting conscious error awareness. However, no Pe was recorded after unaware errors or hits. These results are in line with previous studies suggesting that error awareness is associated with generation of the Pe. Source localisation results confirmed that the posterior cingulate motor area was the main generator of the ERN. However, inverse solution results also point to the involvement of the left posterior insula during the time interval of the Pe, and hence error awareness. Moreover, consecutive to this insular activity, the right orbitofrontal cortex (OFC was activated in response to aware and unaware errors but not in response to hits, consistent with the implication of this area in the evaluation of the value of an error. These results reveal a precise sequence of activations in these three non-overlapping brain regions following error commission, enabling a progressive differentiation between aware and unaware errors as a function of time elapsed, thanks to the involvement first of interoceptive or proprioceptive processes (left insula, later leading to the detection of a breach in the prepotent response mode (right OFC.
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Event Segmentation Improves Event Memory up to One Month Later
Flores, Shaney; Bailey, Heather R.; Eisenberg, Michelle L.; Zacks, Jeffrey M.
2017-01-01
When people observe everyday activity, they spontaneously parse it into discrete meaningful events. Individuals who segment activity in a more normative fashion show better subsequent memory for the events. If segmenting events effectively leads to better memory, does asking people to attend to segmentation improve subsequent memory? To answer…
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Directory of Open Access Journals (Sweden)
Hirokata Fukushima
Full Text Available BACKGROUND: The neural system of our closest living relative, the chimpanzee, is a topic of increasing research interest. However, electrophysiological examinations of neural activity during visual processing in awake chimpanzees are currently lacking. METHODOLOGY/PRINCIPAL FINDINGS: In the present report, skin-surface event-related brain potentials (ERPs were measured while a fully awake chimpanzee observed photographs of faces and objects in two experiments. In Experiment 1, human faces and stimuli composed of scrambled face images were displayed. In Experiment 2, three types of pictures (faces, flowers, and cars were presented. The waveforms evoked by face stimuli were distinguished from other stimulus types, as reflected by an enhanced early positivity appearing before 200 ms post stimulus, and an enhanced late negativity after 200 ms, around posterior and occipito-temporal sites. Face-sensitive activity was clearly observed in both experiments. However, in contrast to the robustly observed face-evoked N170 component in humans, we found that faces did not elicit a peak in the latency range of 150-200 ms in either experiment. CONCLUSIONS/SIGNIFICANCE: Although this pilot study examined a single subject and requires further examination, the observed scalp voltage patterns suggest that selective processing of faces in the chimpanzee brain can be detected by recording surface ERPs. In addition, this non-invasive method for examining an awake chimpanzee can be used to extend our knowledge of the characteristics of visual cognition in other primate species.
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Evidence for a neural law of effect.
Athalye, Vivek R; Santos, Fernando J; Carmena, Jose M; Costa, Rui M
2018-03-02
Thorndike's law of effect states that actions that lead to reinforcements tend to be repeated more often. Accordingly, neural activity patterns leading to reinforcement are also reentered more frequently. Reinforcement relies on dopaminergic activity in the ventral tegmental area (VTA), and animals shape their behavior to receive dopaminergic stimulation. Seeking evidence for a neural law of effect, we found that mice learn to reenter more frequently motor cortical activity patterns that trigger optogenetic VTA self-stimulation. Learning was accompanied by gradual shaping of these patterns, with participating neurons progressively increasing and aligning their covariance to that of the target pattern. Motor cortex patterns that lead to phasic dopaminergic VTA activity are progressively reinforced and shaped, suggesting a mechanism by which animals select and shape actions to reliably achieve reinforcement. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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International Nuclear Information System (INIS)
Korosec, D.
2000-01-01
The events in the nuclear industry are investigated from the license point of view and from the regulatory side too. It is well known the importance of the event investigation. One of the main goals of such investigation is to prevent the circumstances leading to the event and the consequences of the event. The protection of the nuclear workers against nuclear hazard, and the protection of general public against dangerous effects of an event could be achieved by systematic approach to the event investigation. Both, the nuclear safety regulatory body and the licensee shall ensure that operational significant events are investigated in a systematic and technically sound manner to gather information pertaining to the probable causes of the event. One of the results should be appropriate feedback regarding the lessons of the experience to the regulatory body, nuclear industry and general public. In the present paper a general description of systematic approach to the event investigation is presented. The systematic approach to the event investigation works best where cooperation is present among the different divisions of the nuclear facility or regulatory body. By involving management and supervisors the safety office can usually improve their efforts in the whole process. The end result shall be a program which serves to prevent events and reduce the time and efforts solving the root cause which initiated each event. Selection of the proper method for the investigation and an adequate review of the findings and conclusions lead to the higher level of the overall nuclear safety. (author)
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Incorporating an optical waveguide into a neural interface
Energy Technology Data Exchange (ETDEWEB)
Tolosa, Vanessa; Delima, Terri L.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tooker, Angela C.
2016-11-08
An optical waveguide integrated into a multielectrode array (MEA) neural interface includes a device body, at least one electrode in the device body, at least one electrically conducting lead coupled to the at least one electrode, at least one optical channel in the device body, and waveguide material in the at least one optical channel. The fabrication of a neural interface device includes the steps of providing a device body, providing at least one electrode in the device body, providing at least one electrically conducting lead coupled to the at least one electrode, providing at least one optical channel in the device body, and providing a waveguide material in the at least one optical channel.
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Enhancing the top-quark signal at Fermilab Tevatron using neural nets
International Nuclear Information System (INIS)
Ametller, L.; Garrido, L.; Talavera, P.
1994-01-01
We show, in agreement with previous studies, that neural nets can be useful for top-quark analysis at the Fermilab Tevatron. The main features of t bar t and background events in a mixed sample are projected on a single output, which controls the efficiency, purity, and statistical significance of the t bar t signal. We consider a feed-forward multilayer neural net for the CDF reported top-quark mass, using six kinematical variables as inputs. Our main results are based on the exhaustive comparison of the neural net performances with those obtainable from the standard experimental analysis, by imposing different sets of linear cuts over the same variables, showing how the neural net approach improves the standard analysis results
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Kujawa, Autumn; Hajcak, Greg; Danzig, Allison P; Black, Sarah R; Bromet, Evelyn J; Carlson, Gabrielle A; Kotov, Roman; Klein, Daniel N
2016-09-01
Natural disasters expose entire communities to stress and trauma, leading to increased risk for psychiatric symptoms. Yet, the majority of exposed individuals are resilient, highlighting the importance of identifying underlying factors that contribute to outcomes. The current study was part of a larger prospective study of children in Long Island, New York (n = 260). At age 9, children viewed unpleasant and pleasant images while the late positive potential (LPP), an event-related potential component that reflects sustained attention toward salient information, was measured. Following the event-related potential assessment, Hurricane Sandy, the second costliest hurricane in United States history, hit the region. Eight weeks after the hurricane, mothers reported on exposure to hurricane-related stress and children's internalizing and externalizing symptoms. Symptoms were reassessed 8 months after the hurricane. The LPP predicted both internalizing and externalizing symptoms after accounting for prehurricane symptomatology and interacted with stress to predict externalizing symptoms. Among children exposed to higher levels of hurricane-related stress, enhanced neural reactivity to unpleasant images predicted greater externalizing symptoms 8 weeks after the disaster, while greater neural reactivity to pleasant images predicted lower externalizing symptoms. Moreover, interactions between the LPP and stress continued to predict externalizing symptoms 8 months after the hurricane. Results indicate that heightened neural reactivity and attention toward unpleasant information, as measured by the LPP, predispose children to psychiatric symptoms when exposed to higher levels of stress related to natural disasters, while greater reactivity to and processing of pleasant information may be a protective factor. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
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Optimization of blanking process using neural network simulation
International Nuclear Information System (INIS)
Hambli, R.
2005-01-01
The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)
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Neural responses to macronutrients: hedonic and homeostatic mechanisms.
Tulloch, Alastair J; Murray, Susan; Vaicekonyte, Regina; Avena, Nicole M
2015-05-01
The brain responds to macronutrients via intricate mechanisms. We review how the brain's neural systems implicated in homeostatic control of feeding and hedonic responses are influenced by the ingestion of specific types of food. We discuss how these neural systems are dysregulated in preclinical models of obesity. Findings from these studies can increase our understanding of overeating and, perhaps in some cases, the development of obesity. In addition, a greater understanding of the neural circuits affected by the consumption of specific macronutrients, and by obesity, might lead to new treatments and strategies for preventing unhealthy weight gain. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.
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The neural circuits of innate fear: detection, integration, action, and memorization
Silva, Bianca A.; Gross, Cornelius T.
2016-01-01
How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145
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Neural plasticity and its initiating conditions in tinnitus.
Roberts, L E
2018-03-01
Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.
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Directory of Open Access Journals (Sweden)
Lei Chen
2017-06-01
Full Text Available Event-based runoff–pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS prediction and evaluation. The artificial neural network (ANN was used to extend the runoff–pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.
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Bech, Joan; Berenguer, Marc
2014-05-01
Operational quantitative precipitation forecasts (QPF) are provided routinely by weather services or hydrological authorities, particularly those responsible for densely populated regions of small catchments, such as those typically found in Mediterranean areas prone to flash-floods. Specific rainfall values are used as thresholds for issuing warning levels considering different time frameworks (mid-range, short-range, 24h, 1h, etc.), for example 100 mm in 24h or 60 mm in 1h. There is a clear need to determine how feasible is a specific rainfall value for a given lead-time, in particular for very short range forecasts or nowcasts typically obtained from weather radar observations (Pierce et al 2012). In this study we assess which specific nowcast lead-times can be provided for a number of heavy precipitation events (HPE) that affected Catalonia (NE Spain). The nowcasting system we employed generates QPFs through the extrapolation of rainfall fields observed with weather radar following a Lagrangian approach developed and tested successfully in previous studies (Berenguer et al. 2005, 2011).Then QPFs up to 3h are compared with two quality controlled observational data sets: weather radar quantitative precipitation estimates (QPE) and raingauge data. Several high-impact weather HPE were selected including the 7 September 2005 Llobregat Delta river tornado outbreak (Bech et al. 2007) or the 2 November 2008 supercell tornadic thunderstorms (Bech et al. 2011) both producing, among other effects, local flash floods. In these two events there were torrential rainfall rates (30' amounts exceeding 38.2 and 12.3 mm respectively) and 24h accumulation values above 100 mm. A number of verification scores are used to characterize the evolution of precipitation forecast quality with time, which typically presents a decreasing trend but showing an strong dependence on the selected rainfall threshold and integration period. For example considering correlation factors, 30
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Speaking Two Languages Enhances an Auditory but Not a Visual Neural Marker of Cognitive Inhibition
Directory of Open Access Journals (Sweden)
Mercedes Fernandez
2014-09-01
Full Text Available The purpose of the present study was to replicate and extend our original findings of enhanced neural inhibitory control in bilinguals. We compared English monolinguals to Spanish/English bilinguals on a non-linguistic, auditory Go/NoGo task while recording event-related brain potentials. New to this study was the visual Go/NoGo task, which we included to investigate whether enhanced neural inhibition in bilinguals extends from the auditory to the visual modality. Results confirmed our original findings and revealed greater inhibition in bilinguals compared to monolinguals. As predicted, compared to monolinguals, bilinguals showed increased N2 amplitude during the auditory NoGo trials, which required inhibitory control, but no differences during the Go trials, which required a behavioral response and no inhibition. Interestingly, during the visual Go/NoGo task, event related brain potentials did not distinguish the two groups, and behavioral responses were similar between the groups regardless of task modality. Thus, only auditory trials that required inhibitory control revealed between-group differences indicative of greater neural inhibition in bilinguals. These results show that experience-dependent neural changes associated with bilingualism are specific to the auditory modality and that the N2 event-related brain potential is a sensitive marker of this plasticity.
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Feng, Shufang; Shi, Tianyao; Qiu, Jiangxia; Yang, Haihong; Wu, Yan; Zhou, Wenxia; Wang, Wei; Wu, Haitao
2017-10-01
It is well known that Notch1 signaling plays a crucial role in embryonic neural development and adult neurogenesis. The latest evidence shows that Notch1 also plays a critical role in synaptic plasticity in mature hippocampal neurons. So far, deeper insights into the function of Notch1 signaling during the different steps of adult neurogenesis are still lacking, and the mechanisms by which Notch1 dysfunction is associated with brain disorders are also poorly understood. In the current study, we found that Notch1 was highly expressed in the adult-born immature neurons in the hippocampal dentate gyrus. Using a genetic approach to selectively ablate Notch1 signaling in late immature precursors in the postnatal hippocampus by cross-breeding doublecortin (DCX) + neuron-specific proopiomelanocortin (POMC)-α Cre mice with floxed Notch1 mice, we demonstrated a previously unreported pivotal role of Notch1 signaling in survival and function of adult newborn neurons in the dentate gyrus. Moreover, behavioral and functional studies demonstrated that POMC-Notch1 -/- mutant mice showed anxiety and depressive-like behavior with impaired synaptic transmission properties in the dentate gyrus. Finally, our mechanistic study showed significantly compromised phosphorylation of cAMP response element-binding protein (CREB) in Notch1 mutants, suggesting that the dysfunction of Notch1 mutants is associated with the disrupted pCREB signaling in postnatally generated immature neurons in the dentate gyrus.-Feng, S., Shi, T., Qiu, J., Yang, H., Wu, Y., Zhou, W., Wang, W., Wu, H. Notch1 deficiency in postnatal neural progenitor cells in the dentate gyrus leads to emotional and cognitive impairment. © FASEB.
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International Nuclear Information System (INIS)
Polifka, Richard
2011-08-01
An inclusive dijet production in diffractive deep-inelastic scattering is measured. The diffractive selection is based on tagging of the leading proton in the Forward Proton Spectrometer. The statistics of events obtained during the HERA II running period (integrated luminosity of 156.7 pb -1 ) enables the measurement of jet final states with leading proton for the first time. The data cover the phase space of x P 2 and 4≤ Q 2 ≤110 GeV 2 . The dijet data are compared with the next to leading order predictions of the quantum chromodynamics (QCD). The phase space of diffractive dijets is in this analysis by factor of 3 in x P larger than in previous measurements. The QCD predictions based on the DGLAP parton evolution describe the measured data well even in a non-DGLAP enriched phase space where one on the jets goes into the region close to the direction of the outgoing proton. The measured single-differential cross sections are compared to several Monte Carlo models with different treatment of diffractive exchange implemented. (orig.)
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Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression
Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi
2011-01-01
Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half…
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Neural tube defects – disorders of neurulation and related embryonic processes
Copp, Andrew J.; Greene, Nicholas D. E.
2014-01-01
Neural tube defects (NTDs) are severe congenital malformations affecting 1 in every 1000 pregnancies. ‘Open’ NTDs result from failure of primary neurulation as seen in anencephaly, myelomeningocele (open spina bifida) and craniorachischisis. Degeneration of the persistently open neural tube in utero leads to loss of neurological function below the lesion level. ‘Closed’ NTDs are skin-covered disorders of spinal cord structure, ranging from asymptomatic spina bifida occulta to severe spinal cord tethering, and usually traceable to disruption of secondary neurulation. ‘Herniation’ NTDs are those in which meninges, with or without brain or spinal cord tissue, become exteriorised through a pathological opening in the skull or vertebral column (e.g. encephalocele and meningocele). NTDs have multifactorial etiology, with genes and environmental factors interacting to determine individual risk of malformation. While over 200 mutant genes cause open NTDs in mice, much less is known about the genetic causation of human NTDs. Recent evidence has implicated genes of the planar cell polarity signalling pathway in a proportion of cases. The embryonic development of NTDs is complex, with diverse cellular and molecular mechanisms operating at different levels of the body axis. Molecular regulatory events include the BMP and Sonic hedgehog pathways which have been implicated in control of neural plate bending. Primary prevention of NTDs has been implemented clinically following the demonstration that folic acid, when taken as a peri-conceptional supplement, can prevent many cases. Not all NTDs respond to folic acid, however, and adjunct therapies are required for prevention of this folic acid-resistant category. PMID:24009034
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Leveraging Long-term Seismic Catalogs for Automated Real-time Event Classification
Linville, L.; Draelos, T.; Pankow, K. L.; Young, C. J.; Alvarez, S.
2017-12-01
We investigate the use of labeled event types available through reviewed seismic catalogs to produce automated event labels on new incoming data from the crustal region spanned by the cataloged events. Using events cataloged by the University of Utah Seismograph Stations between October, 2012 and June, 2017, we calculate the spectrogram for a time window that spans the duration of each event as seen on individual stations, resulting in 110k event spectrograms (50% local earthquakes examples, 50% quarry blasts examples). Using 80% of the randomized example events ( 90k), a classifier is trained to distinguish between local earthquakes and quarry blasts. We explore variations of deep learning classifiers, incorporating elements of convolutional and recurrent neural networks. Using a single-layer Long Short Term Memory recurrent neural network, we achieve 92% accuracy on the classification task on the remaining 20K test examples. Leveraging the decisions from a group of stations that detected the same event by using the median of all classifications in the group increases the model accuracy to 96%. Additional data with equivalent processing from 500 more recently cataloged events (July, 2017), achieves the same accuracy as our test data on both single-station examples and multi-station medians, suggesting that the model can maintain accurate and stable classification rates on real-time automated events local to the University of Utah Seismograph Stations, with potentially minimal levels of re-training through time.
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Time-of-flight discrimination between gamma-rays and neutrons by using artificial neural networks
International Nuclear Information System (INIS)
Akkoyun, S.
2013-01-01
Highlights: ► Time-of-flight (tof) is an obvious method for separation between gamma and neutron particles. ► tof distributions are obtained by neural networks. ► Neural network method is consistent with the experimental results. ► Neural networks can classify different events for discrimination. - Abstract: In gamma-ray spectroscopy, a number of neutrons are emitted from the nuclei together with the gamma-rays. These neutrons influence gamma-ray spectra. An obvious method for discrimination between neutrons and gamma-rays is based on the time-of-flight (tof) technique. In this work, the tof distributions of gamma-rays and neutrons were obtained both experimentally and by using artificial neural networks (ANNs). It was shown that, ANN can correctly classify gamma-ray and neutron events. Also, for highly nonlinear detector response for tof, we have constructed consistent empirical physical formulas (EPFs) by appropriate ANNs. These ANN–EPFs can be used to derive further physical functions which could be relevant to discrimination between gamma-rays and neutrons
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δ-Protocadherins: Organizers of neural circuit assembly.
Light, Sarah E W; Jontes, James D
2017-09-01
The δ-protocadherins comprise a small family of homophilic cell adhesion molecules within the larger cadherin superfamily. They are essential for neural development as mutations in these molecules give rise to human neurodevelopmental disorders, such as schizophrenia and epilepsy, and result in behavioral defects in animal models. Despite their importance to neural development, a detailed understanding of their mechanisms and the ways in which their loss leads to changes in neural function is lacking. However, recent results have begun to reveal roles for the δ-protocadherins in both regulation of neurogenesis and lineage-dependent circuit assembly, as well as in contact-dependent motility and selective axon fasciculation. These evolutionarily conserved mechanisms could have a profound impact on the robust assembly of the vertebrate nervous system. Future work should be focused on unraveling the molecular mechanisms of the δ-protocadherins and understanding how this family functions broadly to regulate neural development. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Application of neural network to CT
International Nuclear Information System (INIS)
Ma, Xiao-Feng; Takeda, Tatsuoki
1999-01-01
This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)
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Stacked Heterogeneous Neural Networks for Time Series Forecasting
Directory of Open Access Journals (Sweden)
Florin Leon
2010-01-01
Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.
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Neural Alterations in Acquired Age-Related Hearing Loss
Directory of Open Access Journals (Sweden)
Raksha Anand Mudar
2016-06-01
Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.
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Cui, Fang; Zhu, Xiangru; Luo, Yuejia
2017-08-01
Two hypotheses have been proposed regarding the response that is triggered by observing others' pain: the "empathizing hypothesis" and the "threat value of pain hypothesis." The former suggests that observing others' pain triggers an empathic response. The latter suggests that it activates the threat-detection system. In the present study, participants were instructed to observe pictures that showed an anonymous hand or foot in a painful or non-painful situation in a threatening or friendly social context. Event-related potentials were recorded when the participants passively observed these pictures in different contexts. We observed an interaction between context and picture in the early automatic N1 component, in which the painful pictures elicited a larger amplitude than the non-painful pictures only in the threatening context and not in the friendly context. We also observed an interaction between context and picture in the late P3 component, in which the painful pictures elicited a larger amplitude than the non-painful pictures only in the friendly context and not in the threatening context. These results indicate that specific social contexts can modulate the neural responses to observing others' pain. The "empathic hypothesis" and "threat value of pain hypothesis" are not mutually exclusive and do not contradict each other but rather work in different temporal stages.
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The K-PG boundary: how geological events lead to collapse of marine primary producers
Hir guillaume, Le; frederic, Fluteau; yves, Goddéris
2017-04-01
The cause(s) of Cretaceous/Paleogene (K-Pg) mass extinction event is a matter of debate since three decades. A first scenario connects the K-Pg crisis with the Chicxulub impact while the second scenario evokes the emplacement of the Deccan traps in India as the cause for the K-Pg biodiversity collapse. Pierazzo et al. (1998) estimated that the extraterrestrial bolide lead to an instantaneously CO2 degassing ranging from 880 Gt to 2,960 Gt into the atmosphere, together with a massive release of SO2 ranging from 150 to 460 Gt.. Self et al. (2006, 2008) and Chenet et al. (2009) suggested that the emplacement of the Deccan traps released 15,000 Gt to 35,000 Gt of CO2 and 6,800 Gt to 17,000 Gt of SO2 over a 250 kyr-long period (Schoene et al., 2015). To decipher and quantify the long term environmental consequences of both events, we tested different scenarios: a pulse-like magmatic degassing, a bolide impact, and a combination of both. To understand the environmental changes and quantify biodiversity responses, we improve GEOCLIM, a coupled climate-carbon numerical model, by implementing a biodiversity model in which marine species are described by specific death/born rates, sensitivity to abiotic factors (temperature, pH, dissolved O2, calcite saturation state) and feeding relationships, each of these characteristics is assigned randomly. Preliminary simulations accounting for the eruption of the Deccan traps show that successive cooling events (S-aerosols effect) combined with a progressive acidification of surface water (caused by CO2 and SO2 injections) cause a major collapse of the marine biomass. Additional simulations in which Chicxulub impact, different community structures of primary producers will be discussed.
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A New Controller to Enhance PV System Performance Based on Neural Network
Directory of Open Access Journals (Sweden)
Roshdy A AbdelRassoul
2017-06-01
Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.
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PWR system simulation and parameter estimation with neural networks
Energy Technology Data Exchange (ETDEWEB)
Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr
2002-11-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.
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PWR system simulation and parameter estimation with neural networks
International Nuclear Information System (INIS)
Akkurt, Hatice; Colak, Uener
2002-01-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected
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Transient analysis for PWR reactor core using neural networks predictors
International Nuclear Information System (INIS)
Gueray, B.S.
2001-01-01
In this study, transient analysis for a Pressurized Water Reactor core has been performed. A lumped parameter approximation is preferred for that purpose, to describe the reactor core together with mechanism which play an important role in dynamic analysis. The dynamic behavior of the reactor core during transients is analyzed considering the transient initiating events, wich are an essential part of Safety Analysis Reports. several transients are simulated based on the employed core model. Simulation results are in accord the physical expectations. A neural network is developed to predict the future response of the reactor core, in advance. The neural network is trained using the simulation results of a number of representative transients. Structure of the neural network is optimized by proper selection of transfer functions for the neurons. Trained neural network is used to predict the future responses following an early observation of the changes in system variables. Estimated behaviour using the neural network is in good agreement with the simulation results for various for types of transients. Results of this study indicate that the designed neural network can be used as an estimator of the time dependent behavior of the reactor core under transient conditions
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Background rejection in NEXT using deep neural networks
Renner, J.
2017-01-01
We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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Background rejection in NEXT using deep neural networks
International Nuclear Information System (INIS)
Renner, J.; Farbin, A.; Vidal, J. Muñoz; Benlloch-Rodríguez, J. M.; Botas, A.
2017-01-01
Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.
Faisan, Sylvain; Thoraval, Laurent; Armspach, Jean-Paul; Foucher, Jack R; Metz-Lutz, Marie-Noëlle; Heitz, Fabrice
2005-01-01
Most methods used in functional MRI (fMRI) brain mapping require restrictive assumptions about the shape and timing of the fMRI signal in activated voxels. Consequently, fMRI data may be partially and misleadingly characterized, leading to suboptimal or invalid inference. To limit these assumptions and to capture the broad range of possible activation patterns, a novel statistical fMRI brain mapping method is proposed. It relies on hidden semi-Markov event sequence models (HSMESMs), a special class of hidden Markov models (HMMs) dedicated to the modeling and analysis of event-based random processes. Activation detection is formulated in terms of time coupling between (1) the observed sequence of hemodynamic response onset (HRO) events detected in the voxel's fMRI signal and (2) the "hidden" sequence of task-induced neural activation onset (NAO) events underlying the HROs. Both event sequences are modeled within a single HSMESM. The resulting brain activation model is trained to automatically detect neural activity embedded in the input fMRI data set under analysis. The data sets considered in this article are threefold: synthetic epoch-related, real epoch-related (auditory lexical processing task), and real event-related (oddball detection task) fMRI data sets. Synthetic data: Activation detection results demonstrate the superiority of the HSMESM mapping method with respect to a standard implementation of the statistical parametric mapping (SPM) approach. They are also very close, sometimes equivalent, to those obtained with an "ideal" implementation of SPM in which the activation patterns synthesized are reused for analysis. The HSMESM method appears clearly insensitive to timing variations of the hemodynamic response and exhibits low sensitivity to fluctuations of its shape (unsustained activation during task). Real epoch-related data: HSMESM activation detection results compete with those obtained with SPM, without requiring any prior definition of the expected
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Blind Source Separation of Event-Related EEG/MEG.
Metsomaa, Johanna; Sarvas, Jukka; Ilmoniemi, Risto Juhani
2017-09-01
Blind source separation (BSS) can be used to decompose complex electroencephalography (EEG) or magnetoencephalography data into simpler components based on statistical assumptions without using a physical model. Applications include brain-computer interfaces, artifact removal, and identifying parallel neural processes. We wish to address the issue of applying BSS to event-related responses, which is challenging because of nonstationary data. We introduce a new BSS approach called momentary-uncorrelated component analysis (MUCA), which is tailored for event-related multitrial data. The method is based on approximate joint diagonalization of multiple covariance matrices estimated from the data at separate latencies. We further show how to extend the methodology for autocovariance matrices and how to apply BSS methods suitable for piecewise stationary data to event-related responses. We compared several BSS approaches by using simulated EEG as well as measured somatosensory and transcranial magnetic stimulation (TMS) evoked EEG. Among the compared methods, MUCA was the most tolerant one to noise, TMS artifacts, and other challenges in the data. With measured somatosensory data, over half of the estimated components were found to be similar by MUCA and independent component analysis. MUCA was also stable when tested with several input datasets. MUCA is based on simple assumptions, and the results suggest that MUCA is robust with nonideal data. Event-related responses and BSS are valuable and popular tools in neuroscience. Correctly designed BSS is an efficient way of identifying artifactual and neural processes from nonstationary event-related data.
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Cyclone track forecasting based on satellite images using artificial neural networks
Kovordanyi, Rita; Roy, Chandan
2009-01-01
Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on sate...
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Higher incentives can impair performance: neural evidence on reinforcement and rationality.
Achtziger, Anja; Alós-Ferrer, Carlos; Hügelschäfer, Sabine; Steinhauser, Marco
2015-11-01
Standard economic thinking postulates that increased monetary incentives should increase performance. Human decision makers, however, frequently focus on past performance, a form of reinforcement learning occasionally at odds with rational decision making. We used an incentivized belief-updating task from economics to investigate this conflict through measurements of neural correlates of reward processing. We found that higher incentives fail to improve performance when immediate feedback on decision outcomes is provided. Subsequent analysis of the feedback-related negativity, an early event-related potential following feedback, revealed the mechanism behind this paradoxical effect. As incentives increase, the win/lose feedback becomes more prominent, leading to an increased reliance on reinforcement and more errors. This mechanism is relevant for economic decision making and the debate on performance-based payment. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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Neural network based multiscale image restoration approach
de Castro, Ana Paula A.; da Silva, José D. S.
2007-02-01
This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.
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Therapeutic physical exercise in neural injury: friend or foe?
Park, Kanghui; Lee, Seunghoon; Hong, Yunkyung; Park, Sookyoung; Choi, Jeonghyun; Chang, Kyu-Tae; Kim, Joo-Heon; Hong, Yonggeun
2015-12-01
[Purpose] The intensity of therapeutic physical exercise is complex and sometimes controversial in patients with neural injuries. This review assessed whether therapeutic physical exercise is beneficial according to the intensity of the physical exercise. [Methods] The authors identified clinically or scientifically relevant articles from PubMed that met the inclusion criteria. [Results] Exercise training can improve body strength and lead to the physiological adaptation of skeletal muscles and the nervous system after neural injuries. Furthermore, neurophysiological and neuropathological studies show differences in the beneficial effects of forced therapeutic exercise in patients with severe or mild neural injuries. Forced exercise alters the distribution of muscle fiber types in patients with neural injuries. Based on several animal studies, forced exercise may promote functional recovery following cerebral ischemia via signaling molecules in ischemic brain regions. [Conclusions] This review describes several types of therapeutic forced exercise and the controversy regarding the therapeutic effects in experimental animals versus humans with neural injuries. This review also provides a therapeutic strategy for physical therapists that grades the intensity of forced exercise according to the level of neural injury.
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Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC
Lange, Torben; Kasieczka, Gregor
2018-01-01
In this analysis the usage of deep neural networks for an improved event selection forthe top-quark-mass measurement in the t¯ muon+jets channel for events at the CMS ext√periment for the LHC run II with a center of mass energy s = 13 TeV was investigated.The composition of the event selection with respect to different jet-assignment permutationtypes was found to have a strong influence on the systematic uncertainty of the top-quarkmass measurement. A selection based on the output of neural network trained on classifyingevent permutations of the t¯ muon+jets final state into these permutation types could thentbe used to improve the systematical uncertainty of the current mass measurement from asystematical uncertainty of around 630 MeV to 560 MeV.
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Symptom based diagnostic system using artificial neural networks
International Nuclear Information System (INIS)
Santosh; Vinod, Gopika; Saraf, R.K.
2003-01-01
Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)
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Neural Network Classifiers for Local Wind Prediction.
Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz
2004-05-01
This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.
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Neural predictors of emotional inertia in daily life.
Waugh, Christian E; Shing, Elaine Z; Avery, Bradley M; Jung, Youngkyoo; Whitlow, Christopher T; Maldjian, Joseph A
2017-09-01
Assessing emotional dynamics in the brain offers insight into the fundamental neural and psychological mechanisms underlying emotion. One such dynamic is emotional inertia-the influence of one's emotional state at one time point on one's emotional state at a subsequent time point. Emotion inertia reflects emotional rigidity and poor emotion regulation as evidenced by its relationship to depression and neuroticism. In this study, we assessed changes in cerebral blood flow (CBF) from before to after an emotional task and used these changes to predict stress, positive and negative emotional inertia in daily life events. Cerebral blood flow changes in the lateral prefrontal cortex (lPFC) predicted decreased non-specific emotional inertia, suggesting that the lPFC may feature a general inhibitory mechanism responsible for limiting the impact that an emotional state from one event has on the emotional state of a subsequent event. CBF changes in the ventromedial prefrontal cortex and lateral occipital cortex were associated with positive emotional inertia and negative/stress inertia, respectively. These data advance the blossoming literature on the temporal dynamics of emotion in the brain and on the use of neural indices to predict mental health-relevant behavior in daily life. © The Author (2017). Published by Oxford University Press.
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Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.
Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio; Latham, Peter E; Fellin, Tommaso
2017-02-08
The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information. We show how this framework leads to a new statistical analysis of neural activity recorded during behavior that can identify such neural codes, and we discuss how to combine intersection-based analysis of neural recordings with intervention on neural activity to determine definitively whether specific neural activity features are involved in a task. Copyright © 2017 Elsevier Inc. All rights reserved.
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Schmidt, F.; Liu, S.
2016-12-01
Source water quality plays an important role for the safety of drinking water and early detection of its contamination is vital to taking appropriate countermeasures. However, compared to drinking water, it is more difficult to detect contamination events because its environment is less controlled and numerous natural causes contribute to a high variability of the background values. In this project, Artificial Neural Networks (ANNs) and a Contamination Event Detection Process (CED Process) were used to identify events in river water. The ANN models the response of basic water quality sensors obtained in laboratory experiments in an off-line learning stage and continuously forecasts future values of the time line in an on-line forecasting step. During this second stage, the CED Process compares the forecast to the measured value and classifies it as regular background or event value, which modifies the ANN's continuous learning and influences its forecasts. In addition to this basic setup, external information is fed to the CED Process: A so-called Operator Input (OI) is provided to inform about unusual water quality levels that are unrelated to the presence of contamination, for example due to cooling water discharge from a nearby power plant. This study's primary goal is to evaluate how well the OI fits into the design of the combined forecasting ANN and CED Process and to understand its effects on the online forecasting stage. To test this, data from laboratory experiments conducted previously at the School of Environment, Tsinghua University, have been used to perform simulations highlighting features and drawbacks of this method. Applying the OI has been shown to have a positive influence on the ANN's ability to handle a sudden change in background values, which is unrelated to contamination. However, it might also mask the presence of an event, an issue that underlines the necessity to have several instances of the algorithm run in parallel. Other difficulties
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Neural network application to the neutral meson recognition
International Nuclear Information System (INIS)
Lefevre, F.; Delagrange, H.; Merrouch, R.; Ostendorf, R.; Schutz, Y.; Matulewicz, T.
1991-01-01
The combinatorial background produced by high photon multiplicities expected in TAPS experiments causes problems in precise meson recognition. We use neural networks to reduce this background. First we give a description of this technique, hereafter the first results obtained by applying this method to simulated events and future perspective will be discussed [fr
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On the neural mechanisms subserving consciousness and attention
Directory of Open Access Journals (Sweden)
Catherine eTallon-Baudry
2012-01-01
Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.
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Energy Technology Data Exchange (ETDEWEB)
Polifka, Richard
2011-08-15
An inclusive dijet production in diffractive deep-inelastic scattering is measured. The diffractive selection is based on tagging of the leading proton in the Forward Proton Spectrometer. The statistics of events obtained during the HERA II running period (integrated luminosity of 156.7 pb{sup -1}) enables the measurement of jet final states with leading proton for the first time. The data cover the phase space of x{sub P}<0.1, vertical stroke t vertical stroke {<=}1.0 GeV{sup 2} and 4{<=} Q{sup 2} {<=}110 GeV{sup 2}. The dijet data are compared with the next to leading order predictions of the quantum chromodynamics (QCD). The phase space of diffractive dijets is in this analysis by factor of 3 in x{sub P} larger than in previous measurements. The QCD predictions based on the DGLAP parton evolution describe the measured data well even in a non-DGLAP enriched phase space where one on the jets goes into the region close to the direction of the outgoing proton. The measured single-differential cross sections are compared to several Monte Carlo models with different treatment of diffractive exchange implemented. (orig.)
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Voss, Joel L; Paller, Ken A
2008-11-01
A comprehensive understanding of human memory requires cognitive and neural descriptions of memory processes along with a conception of how memory processing drives behavioral responses and subjective experiences. One serious challenge to this endeavor is that an individual memory process is typically operative within a mix of other contemporaneous memory processes. This challenge is particularly disquieting in the context of implicit memory, which, unlike explicit memory, transpires without the subject necessarily being aware of memory retrieval. Neural correlates of implicit memory and neural correlates of explicit memory are often investigated in different experiments using very different memory tests and procedures. This strategy poses difficulties for elucidating the interactions between the two types of memory process that may result in explicit remembering, and for determining the extent to which certain neural processing events uniquely contribute to only one type of memory. We review recent studies that have succeeded in separately assessing neural correlates of both implicit memory and explicit memory within the same paradigm using event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI), with an emphasis on studies from our laboratory. The strategies we describe provide a methodological framework for achieving valid assessments of memory processing, and the findings support an emerging conceptualization of the distinct neurocognitive events responsible for implicit and explicit memory.
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Online fouling detection in electrical circulation heaters using neural networks
Energy Technology Data Exchange (ETDEWEB)
Lalot, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Valenciennes (France). LME; Lecoeuche, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Lille (France). Laboratoire 13D
2003-06-01
Here is presented a method that is able to detect fouling during the service of a circulation electrical heater. The neural based technique is divided in two major steps: identification and classification. Each step uses a neural network, the connection weights of the first one being the inputs of the second network. Each step is detailed and the main characteristics and abilities of the two neural networks are given. It is shown that the method is able to discriminate fouling from viscosity modification that would lead to the same type of effect on the total heat transfer coefficient. (author)
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Directory of Open Access Journals (Sweden)
M. Piot
2008-05-01
Full Text Available We present model studies with the one-dimensional model MISTRA to investigate the potential role of frost flowers, recycling on snow, and open leads in the depletion of tropospheric ozone in the Arctic spring. In our model, we assumed frost flower aerosols to be the major source of bromine. We show that a major ozone depletion event can be satisfactorily reproduced only if the recycling on snow of deposited bromine into gas phase bromine is assumed. In the model, this cycling is more efficient than the bromine explosion process and maintains sufficiently high levels of bromine to deplete ozone down to few nmol mol−1 within four days. We assessed the influence of different surface combinations (open lead/frost flowers on the chemistry in the model. Results showed noticeable modifications affecting the composition of aerosols and the deposition velocities. A model run with a series of coupled frost flower fields and open leads, separated by large areas of snow, showed results comparable with field observations. In addition, we studied the effects of modified temperature of either the frost flower field or the ambient airmass. A warmer frost flower field increases the relative humidity and the aerosol deposition rate. The deposition/re-emission process gains in importance, inducing more reactive bromine in the gas phase, and a stronger ozone depletion. A decrease of 1K in airmass temperature shows in our model that the aerosol uptake capacities of all gas phase species substantially increases, leading to enhanced uptake of acids from the gas phase. Consequently, the so-called bromine explosion accelerated and O3 mixing ratios decreased. In our model representation, variations in wind speed affected the aerosol source function and influenced the amount of bromine in the atmosphere and thus the ozone depletion strength. Recent studies have suggested the important role of the precipitation of calcium carbonate (CaCO3
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Identifying the neural substrates of intrinsic motivation during task performance.
Lee, Woogul; Reeve, Johnmarshall
2017-10-01
Intrinsic motivation is the inherent tendency to seek out novelty and challenge, to explore and investigate, and to stretch and extend one's capacities. When people imagine performing intrinsically motivating tasks, they show heightened anterior insular cortex (AIC) activity. To fully explain the neural system of intrinsic motivation, however, requires assessing neural activity while people actually perform intrinsically motivating tasks (i.e., while answering curiosity-inducing questions or solving competence-enabling anagrams). Using event-related functional magnetic resonance imaging, we found that the neural system of intrinsic motivation involves not only AIC activity, but also striatum activity and, further, AIC-striatum functional interactions. These findings suggest that subjective feelings of intrinsic satisfaction (associated with AIC activations), reward processing (associated with striatum activations), and their interactions underlie the actual experience of intrinsic motivation. These neural findings are consistent with the conceptualization of intrinsic motivation as the pursuit and satisfaction of subjective feelings (interest and enjoyment) as intrinsic rewards.
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Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1
Directory of Open Access Journals (Sweden)
Todor Petkov
2013-12-01
Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.
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Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG.
Duru, Adil Deniz; Assem, Moataz
2018-02-01
Neural efficiency is proposed as one of the neural mechanisms underlying elite athletic performances. Previous sports studies examined neural efficiency using tasks that involve motor functions. In this study we investigate the extent of neural efficiency beyond motor tasks by using a mental subtraction task. A group of elite karate athletes are compared to a matched group of non-athletes. Electroencephalogram is used to measure cognitive dynamics during resting and increased mental workload periods. Mainly posterior alpha band power of the karate players was found to be higher than control subjects under both tasks. Moreover, event related synchronization/desynchronization has been computed to investigate the neural efficiency hypothesis among subjects. Finally, this study is the first study to examine neural efficiency related to a cognitive task, not a motor task, in elite karate players using ERD/ERS analysis. The results suggest that the effect of neural efficiency in the brain is global rather than local and thus might be contributing to the elite athletic performances. Also the results are in line with the neural efficiency hypothesis tested for motor performance studies.
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Improving Neural Recording Technology at the Nanoscale
Ferguson, John Eric
Neural recording electrodes are widely used to study normal brain function (e.g., learning, memory, and sensation) and abnormal brain function (e.g., epilepsy, addiction, and depression) and to interface with the nervous system for neuroprosthetics. With a deep understanding of the electrode interface at the nanoscale and the use of novel nanofabrication processes, neural recording electrodes can be designed that surpass previous limits and enable new applications. In this thesis, I will discuss three projects. In the first project, we created an ultralow-impedance electrode coating by controlling the nanoscale texture of electrode surfaces. In the second project, we developed a novel nanowire electrode for long-term intracellular recordings. In the third project, we created a means of wirelessly communicating with ultra-miniature, implantable neural recording devices. The techniques developed for these projects offer significant improvements in the quality of neural recordings. They can also open the door to new types of experiments and medical devices, which can lead to a better understanding of the brain and can enable novel and improved tools for clinical applications.
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Dormer, James D.; Halicek, Martin; Ma, Ling; Reilly, Carolyn M.; Schreibmann, Eduard; Fei, Baowei
2018-02-01
Cardiovascular disease is a leading cause of death in the United States. The identification of cardiac diseases on conventional three-dimensional (3D) CT can have many clinical applications. An automated method that can distinguish between healthy and diseased hearts could improve diagnostic speed and accuracy when the only modality available is conventional 3D CT. In this work, we proposed and implemented convolutional neural networks (CNNs) to identify diseased hears on CT images. Six patients with healthy hearts and six with previous cardiovascular disease events received chest CT. After the left atrium for each heart was segmented, 2D and 3D patches were created. A subset of the patches were then used to train separate convolutional neural networks using leave-one-out cross-validation of patient pairs. The results of the two neural networks were compared, with 3D patches producing the higher testing accuracy. The full list of 3D patches from the left atrium was then classified using the optimal 3D CNN model, and the receiver operating curves (ROCs) were produced. The final average area under the curve (AUC) from the ROC curves was 0.840 +/- 0.065 and the average accuracy was 78.9% +/- 5.9%. This demonstrates that the CNN-based method is capable of distinguishing healthy hearts from those with previous cardiovascular disease.
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Deep Neural Network Detects Quantum Phase Transition
Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki
2018-03-01
We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.
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msh/Msx gene family in neural development.
Ramos, Casto; Robert, Benoît
2005-11-01
The involvement of Msx homeobox genes in skull and tooth formation has received a great deal of attention. Recent studies also indicate a role for the msh/Msx gene family in development of the nervous system. In this article, we discuss the functions of these transcription factors in neural-tissue organogenesis. We will deal mainly with the interactions of the Drosophila muscle segment homeobox (msh) gene with other homeobox genes and the repressive cascade that leads to neuroectoderm patterning; the role of Msx genes in neural-crest induction, focusing especially on the differences between lower and higher vertebrates; their implication in patterning of the vertebrate neural tube, particularly in diencephalon midline formation. Finally, we will examine the distinct activities of Msx1, Msx2 and Msx3 genes during neurogenesis, taking into account their relationships with signalling molecules such as BMP.
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Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator
International Nuclear Information System (INIS)
Zhou Gang; Yang Li
2009-01-01
A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)
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Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Strauss, J.; Waltenberger, W.; Wulz, C.-E.; Dvornikov, O.; Makarenko, V.; Mossolov, V.; Suarez Gonzalez, J.; Zykunov, V.; Shumeiko, N.; Alderweireldt, S.; De Wolf, E. A.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Salva, S.; Schöfbeck, R.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Bakhshiansohi, H.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Jafari, A.; Komm, M.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; De Oliveira Martins, C.; De Souza, S. Fonseca; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; De Araujo, F. Torres Da Silva; Pereira, A. Vilela; Ahuja, S.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Cheng, T.; Jiang, C. H.; Leggat, D.; Liu, Z.; Romeo, F.; Ruan, M.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Zhao, J.; Ban, Y.; Chen, G.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; González Hernández, C. F.; Ruiz Alvarez, J. D.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Sculac, T.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Ferencek, D.; Kadija, K.; Mesic, B.; Susa, T.; Ather, M. W.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Finger, M.; Finger, M.; Carrera Jarrin, E.; Elgammal, S.; Ellithi Kamel, A.; Mohamed, A.; Kadastik, M.; Perrini, L.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Järvinen, T.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Ghosh, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Kucher, I.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; de Cassagnac, R. Granier; Jo, M.; Lisniak, S.; Miné, P.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sirois, Y.; Leiton, A. G. Stahl; Strebler, T.; Yilmaz, Y.; Zabi, A.; Zghiche, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Bihan, A.-C. Le; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Kiesel, M. 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J.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Almond, J.; Kim, J.; Lee, H.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. h.; Yang, U. K.; Yoo, H. D.; Yu, G. B.; Choi, M.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Goh, J.; Hwang, C.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Ali, M. A. B. Md; Mohamad Idris, F.; Abdullah, W. A. T. Wan; Yusli, M. N.; Zolkapli, Z.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Magaña Villalba, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.; Carpinteyro, S.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khan, W. A.; Saddique, A.; Shah, M. 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T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Rossini, M.; Schönenberger, M.; Starodumov, A.; Tavolaro, V. R.; Theofilatos, K.; Wallny, R.; Aarrestad, T. K.; Amsler, C.; Caminada, L.; Canelli, M. F.; De Cosa, A.; Galloni, C.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Ngadiuba, J.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Seitz, C.; Yang, Y.; Zucchetta, A.; Candelise, V.; Doan, T. H.; Jain, Sh.; Khurana, R.; Konyushikhin, M.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chang, Y. H.; Chao, Y.; Chen, K. F.; Chen, P. H.; Fiori, F.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Miñano Moya, M.; Paganis, E.; Psallidas, A.; Tsai, J. f.; Asavapibhop, B.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Dumanoglu, I.; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I.; Kangal, E. E.; Kara, O.; Topaksu, A. Kayis; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Cerci, D. Sunar; Topakli, H.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.; Bilin, B.; Bilmis, S.; Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Kaya, M.; Kaya, O.; Yetkin, E. A.; Yetkin, T.; Cakir, A.; Cankocak, K.; Sen, S.; Grynyov, B.; Levchuk, L.; Sorokin, P.; Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; Seif El Nasr-storey, S.; Smith, D.; Smith, V. J.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Calligaris, L.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Bundock, A.; Burton, D.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Di Maria, R.; Dunne, P.; Elwood, A.; Futyan, D.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Lane, R.; Laner, C.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Malik, S.; Mastrolorenzo, L.; Nash, J.; Nikitenko, A.; Pela, J.; Penning, B.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Summers, S.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Wright, J.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Bartek, R.; Dominguez, A.; Buccilli, A.; Cooper, S. I.; Henderson, C.; Rumerio, P.; West, C.; Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.; Benelli, G.; Cutts, D.; Garabedian, A.; Hakala, J.; Heintz, U.; Hogan, J. M.; Jesus, O.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Spencer, E.; Syarif, R.; Breedon, R.; Burns, D.; De La Barca Sanchez, M. Calderon; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Smith, J.; Squires, M.; Stolp, D.; Tos, K.; Tripathi, M.; Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Weber, M.; Bouvier, E.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Ghiasi Shirazi, S. M. A.; Hanson, G.; Heilman, J.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Olmedo Negrete, M.; Paneva, M. I.; Shrinivas, A.; Si, W.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Holzner, A.; Klein, D.; Krutelyov, V.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Welke, C.; Wood, J.; Würthwein, F.; Yagil, A.; Della Porta, G. Zevi; Amin, N.; Bhandari, R.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Franco Sevilla, M.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Heller, R.; Incandela, J.; Mullin, S. D.; Ovcharova, A.; Qu, H.; Richman, J.; Stuart, D.; Suarez, I.; Yoo, J.; Anderson, D.; Bendavid, J.; Bornheim, A.; Bunn, J.; Duarte, J.; Lawhorn, J. M.; Mott, A.; Newman, H. B.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhu, R. Y.; Andrews, M. B.; Ferguson, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Weinberg, M.; Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Leontsinis, S.; Mulholland, T.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chaves, J.; Chu, J.; Dittmer, S.; Mcdermott, K.; Mirman, N.; Kaufman, G. Nicolas; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Tan, S. M.; Tao, Z.; Thom, J.; Tucker, J.; Wittich, P.; Zientek, M.; Winn, D.; Abdullin, S.; Albrow, M.; Apollinari, G.; Apresyan, A.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Cremonesi, M.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hare, D.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, M.; Liu, T.; De Sá, R. Lopes; Lykken, J.; Maeshima, K.; Magini, N.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Ristori, L.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strait, J.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Wu, Y.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Field, R. D.; Furic, I. K.; Konigsberg, J.; Korytov, A.; Low, J. F.; Ma, P.; Matchev, K.; Mei, H.; Mitselmakher, G.; Rank, D.; Shchutska, L.; Sperka, D.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, T.; Askew, A.; Bein, S.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Kolberg, T.; Perry, T.; Prosper, H.; Santra, A.; Yohay, R.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Chen, X.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Jung, K.; Sandoval Gonzalez, I. D.; Varelas, N.; Wang, H.; Wu, Z.; Zakaria, M.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; You, C.; Al-bataineh, A.; Baringer, P.; Bean, A.; Boren, S.; Bowen, J.; Castle, J.; Forthomme, L.; Kenny, R. P.; Khalil, S.; Kropivnitskaya, A.; Majumder, D.; Mcbrayer, W.; Murray, M.; Sanders, S.; Stringer, R.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Jeng, G. Y.; Kellogg, R. G.; Kunkle, J.; Mignerey, A. C.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Abercrombie, D.; Allen, B.; Apyan, A.; Azzolini, V.; Barbieri, R.; Baty, A.; Bi, R.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; D'Alfonso, M.; Demiragli, Z.; Gomez Ceballos, G.; Goncharov, M.; Hsu, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Krajczar, K.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Tatar, K.; Velicanu, D.; Wang, J.; Wang, T. W.; Wyslouch, B.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Claes, D. R.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Kravchenko, I.; Malta Rodrigues, A.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Alyari, M.; Dolen, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Nguyen, D.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; De Lima, R. Teixeira; Trocino, D.; Wang, R.-J.; Wood, D.; Bhattacharya, S.; Charaf, O.; Hahn, K. A.; Kumar, A.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Hurtado Anampa, K.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Rupprecht, N.; Smith, G.; Taroni, S.; Wayne, M.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Ji, W.; Liu, B.; Luo, W.; Puigh, D.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Lange, D.; Luo, J.; Marlow, D.; Medvedeva, T.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Svyatkovskiy, A.; Tully, C.; Malik, S.; Barker, A.; Barnes, V. E.; Folgueras, S.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Khatiwada, A.; Miller, D. H.; Neumeister, N.; Schulte, J. F.; Shi, X.; Sun, J.; Wang, F.; Xie, W.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Ciesielski, R.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Juska, E.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Sturdy, J.; Zaleski, S.; Belknap, D. A.; Buchanan, J.; Caillol, C.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Pierro, G. A.; Polese, G.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.
2018-03-01
Events with no charged particles produced between the two leading jets are studied in proton-proton collisions at √{s}=7 {TeV}. The jets were required to have transverse momentum pT ^{ {jet}}>40 {GeV} and pseudorapidity 1.50.2 {GeV} in the interval -1<η < 1 between the jets are observed in excess of calculations that assume no color-singlet exchange. The fraction of events with such a rapidity gap, amounting to 0.5-1% of the selected dijet sample, is measured as a function of the pT of the second-leading jet and of the rapidity separation between the jets. The data are compared to previous measurements at the Tevatron, and to perturbative quantum chromodynamics calculations based on the Balitsky-Fadin-Kuraev-Lipatov evolution equations, including different models of the non-perturbative gap survival probability.
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Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.
Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol
2018-01-01
Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.
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ENGAGING STUDENTS THROUGH EVENT MARKETING: AN EXAMPLE OF UNIVERSITY ENTREPRENEURSHIP EVENT
Directory of Open Access Journals (Sweden)
Alev KOÇAK ALAN
2017-12-01
Full Text Available Despite the growing importance of event marketing, this study investigated the impact of entrepreneurship event on university students which was hosting by one of the leading university in Turkey. Three different assets of event image (event inventiveness, event appropriateness, event adequacy were proposed to influence students’ satisfaction and revisit intentions. Research conducted to 468 students which participate in the entrepreneurship event for two days. For the analyses structural equation modeling technique was used. It was found that (i the dimensions of event image (inventiveness, appropriateness, and adequacy have an impact on students’ satisfaction and (ii students’ satisfaction was a main driver of their revisit intention. Results, future researches and managerial implications were addressed.
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The neural network z-vertex trigger for the Belle II detector
Energy Technology Data Exchange (ETDEWEB)
Skambraks, Sebastian; Neuhaus, Sara [Technische Universitaet Muenchen (Germany); Chen, Yang; Kiesling, Christian [Max-Planck-Institut fuer Physik, Muenchen (Germany); Collaboration: Belle II-Collaboration
2016-07-01
We present a neural network based first level track trigger for the upcoming Belle II detector at the high luminosity SuperKEKB flavor factory. Using hit and drift time information from the Central Drift Chamber (CDC), neural networks estimate the z-coordinates of single track vertex positions. Especially beam induced background, with vertices outside of the interaction region, can clearly be rejected. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity. In the CDC trigger pipeline, the preceding 2D pattern recognition enables a unique per track input representation and a sectorization of the track parameter phase space. The precise z-vertices are then estimated by an ensemble of sector-specific local expert neural networks. After an introduction to the neural trigger system, the benefits of an improved 3D pattern recognition are discussed.
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Advanced event reweighting using multivariate analysis
International Nuclear Information System (INIS)
Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J
2012-01-01
Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.
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Optimizing Design Efficiency of Free Recall Events for fMRI
Öztekin, Ilke; Long, Nicole M.; Badre, David
2010-01-01
Free recall is a fundamental paradigm for studying memory retrieval in the context of minimal cue support. Accordingly, free recall has been extensively studied using behavioral methods. However, the neural mechanisms that support free recall have not been fully investigated due to technical challenges associated with probing individual recall events with neuroimaging methods. Of particular concern is the extent to which the uncontrolled latencies associated with recall events can confer suff...
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Model for neural signaling leap statistics
International Nuclear Information System (INIS)
Chevrollier, Martine; Oria, Marcos
2011-01-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.
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Model for neural signaling leap statistics
Chevrollier, Martine; Oriá, Marcos
2011-03-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.
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Deep Recurrent Neural Networks for seizure detection and early seizure detection systems
Energy Technology Data Exchange (ETDEWEB)
Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-06-05
Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.
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Consolidation of Complex Events via Reinstatement in Posterior Cingulate Cortex.
Bird, Chris M; Keidel, James L; Ing, Leslie P; Horner, Aidan J; Burgess, Neil
2015-10-28
It is well-established that active rehearsal increases the efficacy of memory consolidation. It is also known that complex events are interpreted with reference to prior knowledge. However, comparatively little attention has been given to the neural underpinnings of these effects. In healthy adults humans, we investigated the impact of effortful, active rehearsal on memory for events by showing people several short video clips and then asking them to recall these clips, either aloud (Experiment 1) or silently while in an MRI scanner (Experiment 2). In both experiments, actively rehearsed clips were remembered in far greater detail than unrehearsed clips when tested a week later. In Experiment 1, highly similar descriptions of events were produced across retrieval trials, suggesting a degree of semanticization of the memories had taken place. In Experiment 2, spatial patterns of BOLD signal in medial temporal and posterior midline regions were correlated when encoding and rehearsing the same video. Moreover, the strength of this correlation in the posterior cingulate predicted the amount of information subsequently recalled. This is likely to reflect a strengthening of the representation of the video's content. We argue that these representations combine both new episodic information and stored semantic knowledge (or "schemas"). We therefore suggest that posterior midline structures aid consolidation by reinstating and strengthening the associations between episodic details and more generic schematic information. This leads to the creation of coherent memory representations of lifelike, complex events that are resistant to forgetting, but somewhat inflexible and semantic-like in nature. Copyright © 2015 Bird, Keidel et al.
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Neural correlates of rhythmic expectancy
Directory of Open Access Journals (Sweden)
Theodore P. Zanto
2006-01-01
Full Text Available Temporal expectancy is thought to play a fundamental role in the perception of rhythm. This review summarizes recent studies that investigated rhythmic expectancy by recording neuroelectric activity with high temporal resolution during the presentation of rhythmic patterns. Prior event-related brain potential (ERP studies have uncovered auditory evoked responses that reflect detection of onsets, offsets, sustains,and abrupt changes in acoustic properties such as frequency, intensity, and spectrum, in addition to indexing higher-order processes such as auditory sensory memory and the violation of expectancy. In our studies of rhythmic expectancy, we measured emitted responses - a type of ERP that occurs when an expected event is omitted from a regular series of stimulus events - in simple rhythms with temporal structures typical of music. Our observations suggest that middle-latency gamma band (20-60 Hz activity (GBA plays an essential role in auditory rhythm processing. Evoked (phase-locked GBA occurs in the presence of physically presented auditory events and reflects the degree of accent. Induced (non-phase-locked GBA reflects temporally precise expectancies for strongly and weakly accented events in sound patterns. Thus far, these findings support theories of rhythm perception that posit temporal expectancies generated by active neural processes.
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Parametric models to relate spike train and LFP dynamics with neural information processing.
Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan
2012-01-01
Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial
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Neural Darwinism and consciousness.
Seth, Anil K; Baars, Bernard J
2005-03-01
Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.
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Shaping the learning curve: epigenetic dynamics in neural plasticity
Directory of Open Access Journals (Sweden)
Zohar Ziv Bronfman
2014-07-01
Full Text Available A key characteristic of learning and neural plasticity is state-dependent acquisition dynamics reflected by the non-linear learning curve that links increase in learning with practice. Here we propose that the manner by which epigenetic states of individual cells change during learning contributes to the shape of the neural and behavioral learning curve. We base our suggestion on recent studies showing that epigenetic mechanisms such as DNA methylation, histone acetylation and RNA-mediated gene regulation are intimately involved in the establishment and maintenance of long-term neural plasticity, reflecting specific learning-histories and influencing future learning. Our model, which is the first to suggest a dynamic molecular account of the shape of the learning curve, leads to several testable predictions regarding the link between epigenetic dynamics at the promoter, gene-network and neural-network levels. This perspective opens up new avenues for therapeutic interventions in neurological pathologies.
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Amphioxus and lamprey AP-2 genes: implications for neural crest evolution and migration patterns
Meulemans, Daniel; Bronner-Fraser, Marianne
2002-01-01
The neural crest is a uniquely vertebrate cell type present in the most basal vertebrates, but not in cephalochordates. We have studied differences in regulation of the neural crest marker AP-2 across two evolutionary transitions: invertebrate to vertebrate, and agnathan to gnathostome. Isolation and comparison of amphioxus, lamprey and axolotl AP-2 reveals its extensive expansion in the vertebrate dorsal neural tube and pharyngeal arches, implying co-option of AP-2 genes by neural crest cells early in vertebrate evolution. Expression in non-neural ectoderm is a conserved feature in amphioxus and vertebrates, suggesting an ancient role for AP-2 genes in this tissue. There is also common expression in subsets of ventrolateral neurons in the anterior neural tube, consistent with a primitive role in brain development. Comparison of AP-2 expression in axolotl and lamprey suggests an elaboration of cranial neural crest patterning in gnathostomes. However, migration of AP-2-expressing neural crest cells medial to the pharyngeal arch mesoderm appears to be a primitive feature retained in all vertebrates. Because AP-2 has essential roles in cranial neural crest differentiation and proliferation, the co-option of AP-2 by neural crest cells in the vertebrate lineage was a potentially crucial event in vertebrate evolution.
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Neural Substrates for Processing Task-Irrelevant Sad Images in Adolescents
Wang, Lihong; Huettel, Scott; De Bellis, Michael D.
2008-01-01
Neural systems related to cognitive and emotional processing were examined in adolescents using event-related functional magnetic resonance imaging (fMRI). Ten healthy adolescents performed an emotional oddball task. Subjects detected infrequent circles (targets) within a continual stream of phase-scrambled images (standards). Sad and neutral…
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Directory of Open Access Journals (Sweden)
Matteo Picchiani
2015-03-01
Full Text Available This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010 and Grimsvötn (2011 volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event.
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Using a neural network approach for muon reconstruction and triggering
Etzion, E; Abramowicz, H; Benhammou, Ya; Horn, D; Levinson, L; Livneh, R
2004-01-01
The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.
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International Nuclear Information System (INIS)
1979-01-01
On October 17 during the period of approximately 8:15 to 8:40 p.m., a criticality event occurred in the base of IB column, H-100. The inventory of medium short-lived fission products used to determine the number of fissions indicates that the criticality occurred in column H-100 aqueous phase and the sampling of the column wall with counting of the filings clearly indicates that the event occurred in the column base. The events leading up to the accident are described. The event produced no personnel injury, on-or off-site contamination, nor damage to equipment or property
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Takiyama, Ken
2017-12-01
How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.
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Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.
Dai, Chenyun; Zheng, Yang; Hu, Xiaogang
2018-01-01
Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.
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Model for neural signaling leap statistics
Energy Technology Data Exchange (ETDEWEB)
Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)
2011-03-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.
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Sosic-Vasic, Zrinka; Ulrich, Martin; Ruchsow, Martin; Vasic, Nenad; Grön, Georg
2012-01-01
The present study investigated the association between traits of the Five Factor Model of Personality (Neuroticism, Extraversion, Openness for Experiences, Agreeableness, and Conscientiousness) and neural correlates of error monitoring obtained from a combined Eriksen-Flanker-Go/NoGo task during event-related functional magnetic resonance imaging in 27 healthy subjects. Individual expressions of personality traits were measured using the NEO-PI-R questionnaire. Conscientiousness correlated positively with error signaling in the left inferior frontal gyrus and adjacent anterior insula (IFG/aI). A second strong positive correlation was observed in the anterior cingulate gyrus (ACC). Neuroticism was negatively correlated with error signaling in the inferior frontal cortex possibly reflecting the negative inter-correlation between both scales observed on the behavioral level. Under present statistical thresholds no significant results were obtained for remaining scales. Aligning the personality trait of Conscientiousness with task accomplishment striving behavior the correlation in the left IFG/aI possibly reflects an inter-individually different involvement whenever task-set related memory representations are violated by the occurrence of errors. The strong correlations in the ACC may indicate that more conscientious subjects were stronger affected by these violations of a given task-set expressed by individually different, negatively valenced signals conveyed by the ACC upon occurrence of an error. Present results illustrate that for predicting individual responses to errors underlying personality traits should be taken into account and also lend external validity to the personality trait approach suggesting that personality constructs do reflect more than mere descriptive taxonomies.
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Directory of Open Access Journals (Sweden)
Zrinka Sosic-Vasic
Full Text Available The present study investigated the association between traits of the Five Factor Model of Personality (Neuroticism, Extraversion, Openness for Experiences, Agreeableness, and Conscientiousness and neural correlates of error monitoring obtained from a combined Eriksen-Flanker-Go/NoGo task during event-related functional magnetic resonance imaging in 27 healthy subjects. Individual expressions of personality traits were measured using the NEO-PI-R questionnaire. Conscientiousness correlated positively with error signaling in the left inferior frontal gyrus and adjacent anterior insula (IFG/aI. A second strong positive correlation was observed in the anterior cingulate gyrus (ACC. Neuroticism was negatively correlated with error signaling in the inferior frontal cortex possibly reflecting the negative inter-correlation between both scales observed on the behavioral level. Under present statistical thresholds no significant results were obtained for remaining scales. Aligning the personality trait of Conscientiousness with task accomplishment striving behavior the correlation in the left IFG/aI possibly reflects an inter-individually different involvement whenever task-set related memory representations are violated by the occurrence of errors. The strong correlations in the ACC may indicate that more conscientious subjects were stronger affected by these violations of a given task-set expressed by individually different, negatively valenced signals conveyed by the ACC upon occurrence of an error. Present results illustrate that for predicting individual responses to errors underlying personality traits should be taken into account and also lend external validity to the personality trait approach suggesting that personality constructs do reflect more than mere descriptive taxonomies.
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Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.
2012-01-01
The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480
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Absence of Rybp Compromises Neural Differentiation of Embryonic Stem Cells
Directory of Open Access Journals (Sweden)
Gergo Kovacs
2016-01-01
Full Text Available Rybp (Ring1 and Yy1 Binding Protein is a transcriptional regulator and member of the noncanonical polycomb repressive complex 1 with essential role in early embryonic development. We have previously described that alteration of Rybp dosage in mouse models induced striking neural tube defects (NTDs, exencephaly, and disorganized neurocortex. In this study we further investigated the role of Rybp in neural differentiation by utilising wild type (rybp+/+ and rybp null mutant (rybp-/- embryonic stem cells (ESCs and tried to uncover underlying molecular events that are responsible for the observed phenotypic changes. We found that rybp null mutant ESCs formed less matured neurons, astrocytes, and oligodendrocytes from existing progenitors than wild type cells. Furthermore, lack of rybp coincided with altered gene expression of key neural markers including Pax6 and Plagl1 pinpointing a possible transcriptional circuit among these genes.
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International Nuclear Information System (INIS)
Uhrig, R.E.
1990-01-01
Application of neural networks to monitoring and decision making in the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: (1) diagnosing specific abnormal conditions or problems in nuclear power plants, (2) detection of the change of mode of operation of the plant, (3) validating signals coming from detectors, (4) review of ''noise'' data from TVA's Sequoyah Nuclear Power Plant, and (5) examination of the NRC's database of ''Letter Event Reports'' for correlation of sequences of events in the reported incidents. Each of these projects and its status are described briefly in this paper. This broad based program has as its objective the definition of the state-of-the-art in using neural networks to enhance the performance of commercial nuclear power plants
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The neural basis of speech sound discrimination from infancy to adulthood
Partanen, Eino
2013-01-01
Rapid processing of speech is facilitated by neural representations of native language phonemes. However, some disorders and developmental conditions, such as developmental dyslexia, can hamper the development of these neural memory traces, leading to language delays and poor academic achievement. While the early identification of such deficits is paramount so that interventions can be started as early as possible, there is currently no systematically used ecologically valid paradigm for the ...
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Directory of Open Access Journals (Sweden)
Mattia Rigotti
2010-10-01
Full Text Available Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics, the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding. A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.
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Neural Online Filtering Based on Preprocessed Calorimeter Data
Torres, R C; The ATLAS collaboration; Simas Filho, E F; De Seixas, J M
2009-01-01
Among LHC detectors, ATLAS aims at coping with such high event rate by designing a three-level online triggering system. The first level trigger output will be ~75 kHz. This level will mark the regions where relevant events were found. The second level will validate LVL1 decision by looking only at the approved data using full granularity. At the level two output, the event rate will be reduced to ~2 kHz. Finally, the third level will look at full event information and a rate of ~200 Hz events is expected to be approved, and stored in persistent media for further offline analysis. Many interesting events decay into electrons, which have to be identified from the huge background noise (jets). This work proposes a high-efficient LVL2 electron / jet discrimination system based on neural networks fed from preprocessed calorimeter information. The feature extraction part of the proposed system performs a ring structure of data description. A set of concentric rings centered at the highest energy cell is generated ...
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Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow.
Hadley, Aaron J; Krival, Kate R; Ridgel, Angela L; Hahn, Elizabeth C; Tyler, Dustin J
2015-04-01
We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96 % of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93 % of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.
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Sengupta, Abhronil; Shim, Yong; Roy, Kaushik
2016-12-01
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by ∼ 100× in comparison to a corresponding digital/analog CMOS neuron implementation.
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Hashimoto, Yuki; Yotsumoto, Yuko
2018-01-01
The neural basis of time perception has long attracted the interests of researchers. Recently, a conceptual model consisting of neural oscillators was proposed and validated by behavioral experiments that measured the dilated duration in perception of a flickering stimulus (Hashimoto and Yotsumoto, 2015). The model proposed that flickering stimuli cause neural entrainment of oscillators, resulting in dilated time perception. In this study, we examined the oscillator-based model of time perception, by collecting electroencephalography (EEG) data during an interval-timing task. Initially, subjects observed a stimulus, either flickering at 10-Hz or constantly illuminated. The subjects then reproduced the duration of the stimulus by pressing a button. As reported in previous studies, the subjects reproduced 1.22 times longer durations for flickering stimuli than for continuously illuminated stimuli. The event-related potential (ERP) during the observation of a flicker oscillated at 10 Hz, reflecting the 10-Hz neural activity phase-locked to the flicker. Importantly, the longer reproduced duration was associated with a larger amplitude of the 10-Hz ERP component during the inter-stimulus interval, as well as during the presentation of the flicker. The correlation between the reproduced duration and the 10-Hz oscillation during the inter-stimulus interval suggested that the flicker-induced neural entrainment affected time dilation. While the 10-Hz flickering stimuli induced phase-locked entrainments at 10 Hz, we also observed event-related desynchronizations of spontaneous neural oscillations in the alpha-frequency range. These could be attributed to the activation of excitatory neurons while observing the flicker stimuli. In addition, neural activity at approximately the alpha frequency increased during the reproduction phase, indicating that flicker-induced neural entrainment persisted even after the offset of the flicker. In summary, our results suggest that the
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Neutral networks for event filtering at D0
International Nuclear Information System (INIS)
Cutts, D.; Hoftun, J.S.; Sornborger, A.; Johnson, R.C.; Zeller, R.T.
1989-01-01
Neutral networks may provide important tools for pattern recognition in high energy physics. We discuss an initial exploration of these techniques, presenting the result of network simulations of several filter algorithms. The D0 data acquisition system, a MicroVAX farm, will perform critical event selection; we describe a possible implementation of neural network algorithms in this system. (orig.)
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Neural ECM in addiction, schizophrenia, and mood disorder
Lubbers, B.R.; Smit, A.B.; Spijker, S.; van den Oever, M.C.
2014-01-01
The extracellular matrix (ECM) has a prominent role in brain development, maturation of neural circuits, and adult neuroplasticity. This multifactorial role of the ECM suggests that processes that affect composition or turnover of ECM in the brain could lead to altered brain function, possibly
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Wang, Yimeng; Bargh, John A
2016-01-01
Consistent with neural reuse theory, empirical tests of the related "scaffolding" principle of abstract concept development show that higher-level concepts "reuse" and are built upon fundamental motives such as survival, safety, and consumption. This produces mutual influence between the two levels, with far-ranging impacts from consumer behavior to political attitudes.
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Neural networks and their potential application to nuclear power plants
International Nuclear Information System (INIS)
Uhrig, R.E.
1991-01-01
A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters
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Neural correlates of processing "self-conscious" vs. "basic" emotions.
Gilead, Michael; Katzir, Maayan; Eyal, Tal; Liberman, Nira
2016-01-29
Self-conscious emotions are prevalent in our daily lives and play an important role in both normal and pathological behavior. Despite their immense significance, the neural substrates that are involved in the processing of such emotions are surprisingly under-studied. In light of this, we conducted an fMRI study in which participants thought of various personal events which elicited feelings of negative and positive self-conscious (i.e., guilt, pride) or basic (i.e., anger, joy) emotions. We performed a conjunction analysis to investigate the neural correlates associated with processing events that are related to self-conscious vs. basic emotions, irrespective of valence. The results show that processing self-conscious emotions resulted in activation within frontal areas associated with self-processing and self-control, namely, the mPFC extending to the dACC, and within the lateral-dorsal prefrontal cortex. Processing basic emotions resulted in activation throughout relatively phylogenetically-ancient regions of the cortex, namely in visual and tactile processing areas and in the insular cortex. Furthermore, self-conscious emotions differentially activated the mPFC such that the negative self-conscious emotion (guilt) was associated with a more dorsal activation, and the positive self-conscious emotion (pride) was associated with a more ventral activation. We discuss how these results shed light on the nature of mental representations and neural systems involved in self-reflective and affective processing. Copyright © 2015 Elsevier Ltd. All rights reserved.
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The neural basis of responsibility attribution in decision-making.
Li, Peng; Shen, Yue; Sui, Xue; Chen, Changming; Feng, Tingyong; Li, Hong; Holroyd, Clay
2013-01-01
Social responsibility links personal behavior with societal expectations and plays a key role in affecting an agent's emotional state following a decision. However, the neural basis of responsibility attribution remains unclear. In two previous event-related brain potential (ERP) studies we found that personal responsibility modulated outcome evaluation in gambling tasks. Here we conducted a functional magnetic resonance imaging (fMRI) study to identify particular brain regions that mediate responsibility attribution. In a context involving team cooperation, participants completed a task with their teammates and on each trial received feedback about team success and individual success sequentially. We found that brain activity differed between conditions involving team success vs. team failure. Further, different brain regions were associated with reinforcement of behavior by social praise vs. monetary reward. Specifically, right temporoparietal junction (RTPJ) was associated with social pride whereas dorsal striatum and dorsal anterior cingulate cortex (ACC) were related to reinforcement of behaviors leading to personal gain. The present study provides evidence that the RTPJ is an important region for determining whether self-generated behaviors are deserving of praise in a social context.
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Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Jaehong Yoon
2018-01-01
Full Text Available Feature of event-related potential (ERP has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.
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Neural networks prove effective at NO{sub x} reduction
Energy Technology Data Exchange (ETDEWEB)
Radl, B.J. [Pegasus Technologies, Mentor, OH (United States)
2000-05-01
The virtues of the Pegasus NeuSIGHT combustion optimisation software are extolled. It has been installed at more than 25 power plants and operates mostly in closed loop control. The software uses the leading neural network technology from Computer associates. The system is said to reduce emissions of nitrogen oxides, increase plant efficiency and has the potential for saving millions of dollars in capital and operating costs. The value of the system is illustrated by case studies from three coal-fired power plants. The meaning of 'neural network' is explained.
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Directory of Open Access Journals (Sweden)
Jason Samuel Sherwin
Full Text Available For a soldier, decisions to use force can happen rapidly and sometimes lead to undesired consequences. In many of these situations, there is a rapid assessment by the shooter that recognizes a threat and responds to it with return fire. But the neural processes underlying these rapid decisions are largely unknown, especially amongst those with extensive weapons experience and expertise. In this paper, we investigate differences in weapons experts and non-experts during an incoming gunfire detection task. Specifically, we analyzed the electroencephalography (EEG of eleven expert marksmen/soldiers and eleven non-experts while they listened to an audio scene consisting of a sequence of incoming and non-incoming gunfire events. Subjects were tasked with identifying each event as quickly as possible and committing their choice via a motor response. Contrary to our hypothesis, experts did not have significantly better behavioral performance or faster response time than novices. Rather, novices indicated trends of better behavioral performance than experts. These group differences were more dramatic in the EEG correlates of incoming gunfire detection. Using machine learning, we found condition-discriminating EEG activity among novices showing greater magnitude and covering longer periods than those found in experts. We also compared group-level source reconstruction on the maximum discriminating neural correlates and found that each group uses different neural structures to perform the task. From condition-discriminating EEG and source localization, we found that experts perceive more categorical overlap between incoming and non-incoming gunfire. Consequently, the experts did not perform as well behaviorally as the novices. We explain these unexpected group differences as a consequence of experience with gunfire not being equivalent to expertise in recognizing incoming gunfire.
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Nonlinear neural network for hemodynamic model state and input estimation using fMRI data
Karam, Ayman M.
2014-11-01
Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.
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Cosmic-ray discrimination capabilities of DELTA E-E silicon nuclear telescopes using neural networks
Ambriola, M; Cafagna, F; Castellano, M; Ciacio, F; Circella, M; De Marzo, C N; Montaruli, T
2000-01-01
An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the 'mixture of experts' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a 'classical' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within 1% - with that of the classical one, with efficiency of the order of 98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.
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Neural decoding of collective wisdom with multi-brain computing.
Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry
2012-01-02
Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally
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Application of artificial neural networks in particle physics
International Nuclear Information System (INIS)
Kolanoski, H.
1995-04-01
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)
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Higher Moments of Underlying Event Distributions
Xu, Zhen
2017-01-01
We perform an Underlying Event analysis for real data sets from pp collisions at center of mass energy $ \\sqrt{s}=5 $ and 13 TeV and pPb collisions at $ \\sqrt{s}=7 $ TeV at the LHC, together with the Monte Carlo data sets generated with Pythia8 and EPOS in the same conditions. The analysis is focused on the transverse region which is more sensitive to the Underlying Event, and performed as a function of the leading track transverse - momentum $p_t$ in each event. In our work, not only the average underlying event activity but also its fluctuation, namely its root mean square (RMS), Skewness and Kurtosis, are analyzed. We find that the particle density, energy density and their fluctuation magnitude (RMS) are suppressed at leading $p_t\\approx$ 5 GeV/c for all these cases, with EPOS having evident deviation of 10\\%-25\\%. The higher moments skewness and kurtosis decrease rapidly in low leading $p_t$ region, and follow an interesting Gaussian-like peak centered at leading $p_t\\approx$ 15 GeV/c.
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Neural Signature of Value-Based Sensorimotor Prioritization in Humans.
Blangero, Annabelle; Kelly, Simon P
2017-11-01
In situations in which impending sensory events demand fast action choices, we must be ready to prioritize higher-value courses of action to avoid missed opportunities. When such a situation first presents itself, stimulus-action contingencies and their relative value must be encoded to establish a value-biased state of preparation for an impending sensorimotor decision. Here, we sought to identify neurophysiological signatures of such processes in the human brain (both female and male). We devised a task requiring fast action choices based on the discrimination of a simple visual cue in which the differently valued sensory alternatives were presented 750-800 ms before as peripheral "targets" that specified the stimulus-action mapping for the upcoming decision. In response to the targets, we identified a discrete, transient, spatially selective signal in the event-related potential (ERP), which scaled with relative value and strongly predicted the degree of behavioral bias in the upcoming decision both across and within subjects. This signal is not compatible with any hitherto known ERP signature of spatial selection and also bears novel distinctions with respect to characterizations of value-sensitive, spatially selective activity found in sensorimotor areas of nonhuman primates. Specifically, a series of follow-up experiments revealed that the signal was reliably invoked regardless of response laterality, response modality, sensory feature, and reward valence. It was absent, however, when the response deadline was relaxed and the strategic need for biasing removed. Therefore, more than passively representing value or salience, the signal appears to play a versatile and active role in adaptive sensorimotor prioritization. SIGNIFICANCE STATEMENT In many situations such as fast-moving sports, we must be ready to act fast in response to sensory events and, in our preparation, prioritize courses of action that lead to greater rewards. Although behavioral effects of
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Application of neural network in τ→ρυτ polarization analysis
International Nuclear Information System (INIS)
Zhang Ziping; Wang Yifang; Innocente, V.
1994-01-01
An artificial neutral network was built to select events in the τ→ρυ τ polarization analysis at LEP/L3, much better selection efficiency has been achieved. Detailed studies show that no systematic errors or bias have been introduced by the application of neural network. A polarization of P τ = -0.129 +- 0.050 +- 0.050 for this channel was obtained by using a sample of 8977 τ + τ - pairs collected near the peak of Z 0 resonance. The neural network training method and some details are described
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Using neural networks to enhance the Higgs boson signal at hadron colliders
International Nuclear Information System (INIS)
Field, R.D.; Kanev, Y.; Tayebnejad, M.; Griffin, P.A.
1995-01-01
Neural networks are used to help distinguish the ZZ → ell + ell - -jet-jet signal produced by the decay of a 400 GeV Higgs boson at a proton-proton collider energy of 15 TeV from the ''ordinary'' QCD Z + jets background. The ideal case where only one event at a time enters the detector (no pile-up) and the case of multiple interactions per beam crossing (pile-up) are examined. In both cases, when used in conjunction with the standard cuts, neural networks provide an additional signal to background enhancement
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Energy Technology Data Exchange (ETDEWEB)
Hallerberg, Sarah [Chemnitz University of Technology (Germany); Ruzmaikin, Alexander; Feynman, Joan [Jet Propulsion Laboratory, California Institute of Technology (United States)
2011-07-01
Shock waves in the solar wind associated with solar coronal mass ejections produce fluxes of high-energy protons and ions with energies larger than 10 MeV. These fluxes present a danger to humans and electronic equipment in space, and also endanger passengers of over-pole air flights. The approaches that have been exploited for the prediction of high-energy particle events so far consist in training artificial neural networks on catalogues of events. Our approach towards this task is based on the identification of precursory structures in the fluxes of particles. In contrast to artificial neural networks that function as a ''black box'' transforming data into predictions, this classification approach can additionally provide information on relevant precursory events and thus might help to improve the understanding of underlying mechanisms of particle acceleration.
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Neural network determination of parton distributions: the nonsinglet case
International Nuclear Information System (INIS)
Del Debbio, Luigi; Forte, Stefano; Latorre, Jose I.; Piccione, Andrea; Rojo, Joan
2007-01-01
We provide a determination of the isotriplet quark distribution from available deep-inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next-to-next-to-leading order, and compare them with those obtained using other approaches
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Vibration analysis in nuclear power plant using neural networks
International Nuclear Information System (INIS)
Loskiewicz-Buczak, A.; Alguindigue, I.E.
1993-01-01
Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper documents the authors' work on the design of a vibration monitoring methodology enhanced by neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to handle data which may be distorted or noisy. This paper describes three neural networks-based methods for the automation of some of the activities related to motion and vibration monitoring in engineering systems
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Tests of track segment and vertex finding with neural networks
International Nuclear Information System (INIS)
Denby, B.; Lessner, E.; Lindsey, C.S.
1990-04-01
Feed forward neural networks have been trained, using back-propagation, to find the slopes of simulated track segments in a straw chamber and to find the vertex of tracks from both simulated and real events in a more conventional drift chamber geometry. Network architectures, training, and performance are presented. 12 refs., 7 figs
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Autocorrel I: A Neural Network Based Network Event Correlation Approach
National Research Council Canada - National Science Library
Japkowicz, Nathalie; Smith, Reuben
2005-01-01
.... We use the autoassociator to build prototype software to cluster network alerts generated by a Snort intrusion detection system, and discuss how the results are significant, and how they can be applied to other types of network events.
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Kim, Suhwan; Jung, Unsang; Baek, Juyoung; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon
2013-01-01
Recently, mouse neuroblastoma cells have been considered as an attractive model for the study of human neurological and prion diseases, and they have been intensively used as a model system in different areas. For example, the differentiation of neuro2a (N2A) cells, receptor-mediated ion current, and glutamate-induced physiological responses have been actively investigated with these cells. These mouse neuroblastoma N2A cells are of interest because they grow faster than other cells of neural origin and have a number of other advantages. The calcium oscillations and neural spikes of mouse neuroblastoma N2A cells in epileptic conditions are evaluated. Based on our observations of neural spikes in these cells with our proposed imaging modality, we reported that they can be an important model in epileptic activity studies. We concluded that mouse neuroblastoma N2A cells produce epileptic spikes in vitro in the same way as those produced by neurons or astrocytes. This evidence suggests that increased levels of neurotransmitter release due to the enhancement of free calcium from 4-aminopyridine causes the mouse neuroblastoma N2A cells to produce epileptic spikes and calcium oscillations.
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Vibration monitoring with artificial neural networks
International Nuclear Information System (INIS)
Alguindigue, I.
1991-01-01
Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural network to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected from operating machinery. Two neural networks algorithms were used in our project: the Recirculation algorithm for data compression and the Backpropagation algorithm to perform the actual classification of the patterns. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results to date are very encouraging
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Different Neural Correlates of Emotion-Label Words and Emotion-Laden Words: An ERP Study
Zhang, Juan; Wu, Chenggang; Meng, Yaxuan; Yuan, Zhen
2017-01-01
It is well-documented that both emotion-label words (e.g., sadness, happiness) and emotion-laden words (e.g., death, wedding) can induce emotion activation. However, the neural correlates of emotion-label words and emotion-laden words recognition have not been examined. The present study aimed to compare the underlying neural responses when processing the two kinds of words by employing event-related potential (ERP) measurements. Fifteen Chinese native speakers were asked to perform a lexical...
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Probabilistic neural network algorithm for using radon emanations as an earthquake precursor
International Nuclear Information System (INIS)
Gupta, Dhawal; Shahani, D.T.
2014-01-01
The investigation throughout the world in past two decades provides evidence which indicate that significance variation of radon and other soil gases occur in association with major geophysical events such as earthquake. The traditional statistical algorithm includes regression to remove the effect of the meteorological parameters from the raw radon and anomalies are calculated either taking the periodicity in seasonal variations or periodicity computed using Fast Fourier Transform. In case of neural networks the regression step is avoided. A neural network model can be found which can learn the behavior of radon with respect to meteorological parameter in order that changing emission patterns may be adapted to by the model on its own. The output of this neural model is the estimated radon values. This estimated radon value is used to decide whether anomalous behavior of radon has occurred and a valid precursor may be identified. The neural network model developed using Radial Basis function network gave a prediction rate of 87.7%. The same was accompanied by huge false alarms. The present paper deals with improved neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period. This neural network model reduces the false alarms to zero and gave same prediction rate as RBF networks. (author)
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Safe leads and lead changes in competitive team sports
Clauset, A.; Kogan, M.; Redner, S.
2015-06-01
We investigate the time evolution of lead changes within individual games of competitive team sports. Exploiting ideas from the theory of random walks, the number of lead changes within a single game follows a Gaussian distribution. We show that the probability that the last lead change and the time of the largest lead size are governed by the same arcsine law, a bimodal distribution that diverges at the start and at the end of the game. We also determine the probability that a given lead is "safe" as a function of its size L and game time t . Our predictions generally agree with comprehensive data on more than 1.25 million scoring events in roughly 40 000 games across four professional or semiprofessional team sports, and are more accurate than popular heuristics currently used in sports analytics.
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ATLAS event at 13 TeV - Highest mass dijets resonance event in 2015 data
ATLAS Collaboration
2015-01-01
The highest-mass, central dijet event passing the dijet resonance selection collected in 2015 (Event 1273922482, Run 280673) : the two central high-pT jets have an invariant mass of 6.9 TeV, the two leading jets have a pT of 3.2 TeV. The missing transverse momentum in this event is 46 GeV.
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Sequential and parallel image restoration: neural network implementations.
Figueiredo, M T; Leitao, J N
1994-01-01
Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.
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The specialization of function: cognitive and neural perspectives.
Mahon, Bradford Z; Cantlon, Jessica F
2011-05-01
A unifying theme that cuts across all research areas and techniques in the cognitive and brain sciences is whether there is specialization of function at levels of processing that are "abstracted away" from sensory inputs and motor outputs. Any theory that articulates claims about specialization of function in the mind/brain confronts the following types of interrelated questions, each of which carries with it certain theoretical commitments. What methods are appropriate for decomposing complex cognitive and neural processes into their constituent parts? How do cognitive processes map onto neural processes, and at what resolution are they related? What types of conclusions can be drawn about the structure of mind from dissociations observed at the neural level, and vice versa? The contributions that form this Special Issue of Cognitive Neuropsychology represent recent reflections on these and other issues from leading researchers in different areas of the cognitive and brain sciences.
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Evolvable synthetic neural system
Curtis, Steven A. (Inventor)
2009-01-01
An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.
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Design of Robust Neural Network Classifiers
DEFF Research Database (Denmark)
Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads
1998-01-01
This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...
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Using neural networks with jet shapes to identify b jets in e+e- interactions
International Nuclear Information System (INIS)
Bellantoni, L.; Conway, J.S.; Jacobsen, J.E.; Pan, Y.B.; Wu Saulan
1991-01-01
A feed-forward neural network trained using backpropagation was used to discriminate between b and light quark jets in e + e - → Z 0 → qanti q events. The information presented to the network consisted of 25 jet shape variables. The network successfully identified b jets in two- and three-jet events modeled using a detector simulation. The jet identification efficiency for two-jet events was 61% and the probability to call a light quark jet a b jet equal to 20%. (orig.)
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Directory of Open Access Journals (Sweden)
Andrej Robida
2004-09-01
Full Text Available Background. The Objective of the article is a two year statistics on sentinel events in hospitals. Results of a survey on sentinel events and the attitude of hospital leaders and staff are also included. Some recommendations regarding patient safety and the handling of sentinel events are given.Methods. In March 2002 the Ministry of Health introduce a voluntary reporting system on sentinel events in Slovenian hospitals. Sentinel events were analyzed according to the place the event, its content, and root causes. To show results of the first year, a conference for hospital directors and medical directors was organized. A survey was conducted among the participants with the purpose of gathering information about their view on sentinel events. One hundred questionnaires were distributed.Results. Sentinel events. There were 14 reports of sentinel events in the first year and 7 in the second. In 4 cases reports were received only after written reminders were sent to the responsible persons, in one case no reports were obtained. There were 14 deaths, 5 of these were in-hospital suicides, 6 were due to an adverse event, 3 were unexplained. Events not leading to death were a suicide attempt, a wrong side surgery, a paraplegia after spinal anaesthesia, a fall with a femoral neck fracture, a damage of the spleen in the event of pleural space drainage, inadvertent embolization with absolute alcohol into a femoral artery and a physical attack on a physician by a patient. Analysis of root causes of sentinel events showed that in most cases processes were inadequate.Survey. One quarter of those surveyed did not know about the sentinel events reporting system. 16% were having actual problems when reporting events and 47% beleived that there was an attempt to blame individuals. Obstacles in reporting events openly were fear of consequences, moral shame, fear of public disclosure of names of participants in the event and exposure in mass media. The majority of
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Aad, Georges; Abdallah, Jalal; Abdelalim, Ahmed Ali; Abdesselam, Abdelouahab; Abdinov, Ovsat; Abi, Babak; Abolins, Maris; Abramowicz, Halina; Abreu, Henso; Acerbi, Emilio; Acharya, Bobby Samir; Ackers, Mario; Adams, David; Addy, Tetteh; Adelman, Jahred; Aderholz, Michael; Adomeit, Stefanie; Adragna, Paolo; Adye, Tim; Aefsky, Scott; Aguilar-Saavedra, Juan Antonio; Aharrouche, Mohamed; Ahlen, Steven; Ahles, Florian; Ahmad, Ashfaq; Ahsan, Mahsana; Aielli, Giulio; Akdogan, Taylan; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alam, Mohammad; Alam, Muhammad Aftab; Albrand, Solveig; Aleksa, Martin; Aleksandrov, Igor; Aleppo, Mario; Alessandria, Franco; Alexa, Calin; Alexander, Gideon; Alexandre, Gauthier; Alexopoulos, Theodoros; Alhroob, Muhammad; Aliev, Malik; Alimonti, Gianluca; Alison, John; Aliyev, Magsud; Allport, Phillip; Allwood-Spiers, Sarah; Almond, John; Aloisio, Alberto; Alon, Raz; Alonso, Alejandro; Alonso, Jose; Alviggi, Mariagrazia; Amako, Katsuya; Amaral, Pedro; Amelung, Christoph; Ammosov, Vladimir; Amorim, Antonio; Amorós, Gabriel; Amram, Nir; Anastopoulos, Christos; Andeen, Timothy; Anders, Christoph Falk; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Andrieux, Marie-Laure; Anduaga, Xabier; Angerami, Aaron; Anghinolfi, Francis; Anjos, Nuno; Annovi, Alberto; Antonaki, Ariadni; Antonelli, Mario; Antonelli, Stefano; Antos, Jaroslav; Anulli, Fabio; Aoun, Sahar; Aperio Bella, Ludovica; Apolle, Rudi; Arabidze, Giorgi; Aracena, Ignacio; Arai, Yasuo; Arce, Ayana; Archambault, John-Paul; Arfaoui, Samir; Arguin, Jean-Francois; Arik, Engin; Arik, Metin; Armbruster, Aaron James; Arms, Kregg; Armstrong, Stephen Randolph; Arnaez, Olivier; Arnault, Christian; Artamonov, Andrei; Artoni, Giacomo; Arutinov, David; Asai, Shoji; Silva, José; Asfandiyarov, Ruslan; Ask, Stefan; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astbury, Alan; Astvatsatourov, Anatoli; Atoian, Grigor; Aubert, Bernard; Auerbach, Benjamin; Auge, Etienne; Augsten, Kamil; Aurousseau, Mathieu; Austin, Nicholas; Avramidou, Rachel Maria; Axen, David; Ay, Cano; Azuelos, Georges; Azuma, Yuya; Baak, Max; Baccaglioni, Giuseppe; Bacci, Cesare; Bach, Andre; Bachacou, Henri; Bachas, Konstantinos; Bachy, Gerard; Backes, Moritz; Badescu, Elisabeta; Bagnaia, Paolo; Bahinipati, Seema; Bai, Yu; Bailey, David; Bain, Travis; Baines, John; Baker, Oliver Keith; Baker, Sarah; Baltasar Dos Santos Pedrosa, Fernando; Banas, Elzbieta; Banerjee, Piyali; Banerjee, Swagato; Banfi, Danilo; Bangert, Andrea Michelle; Bansal, Vikas; Bansil, Hardeep Singh; Barak, Liron; Baranov, Sergei; Barashkou, Andrei; Barbaro Galtieri, Angela; Barber, Tom; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Bardin, Dmitri; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnett, Bruce; Barnett, Michael; Baroncelli, Antonio; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Barrillon, Pierre; Bartoldus, Rainer; Barton, Adam Edward; Bartsch, Detlef; Bates, Richard; Batkova, Lucia; Batley, Richard; Battaglia, Andreas; Battistin, Michele; Battistoni, Giuseppe; Bauer, Florian; Bawa, Harinder Singh; Beare, Brian; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Beckingham, Matthew; Becks, Karl-Heinz; Beddall, Andrew; Beddall, Ayda; Bednyakov, Vadim; Bee, Christopher; Begel, Michael; Behar Harpaz, Silvia; Behera, Prafulla; Beimforde, Michael; Belanger-Champagne, Camille; Bell, Paul; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellina, Francesco; Bellomo, Giovanni; Bellomo, Massimiliano; Belloni, Alberto; Belotskiy, Konstantin; Beltramello, Olga; Ben Ami, Sagi; Benary, Odette; Benchekroun, Driss; Benchouk, Chafik; Bendel, Markus; Benedict, Brian Hugues; Benekos, Nektarios; Benhammou, Yan; Benjamin, Douglas; Benoit, Mathieu; Bensinger, James; Benslama, Kamal; Bentvelsen, Stan; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Berglund, Elina; Beringer, Jürg; Bernardet, Karim; Bernat, Pauline; Bernhard, Ralf; Bernius, Catrin; Berry, Tracey; Bertin, Antonio; Bertinelli, Francesco; Bertolucci, Federico; Besana, Maria Ilaria; Besson, Nathalie; Bethke, Siegfried; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianco, Michele; Biebel, Otmar; Biesiada, Jed; Biglietti, Michela; Bilokon, Halina; Bindi, Marcello; Bingul, Ahmet; Bini, Cesare; Biscarat, Catherine; Bitenc, Urban; Black, Kevin; Blair, Robert; Blanchard, Jean-Baptiste; Blanchot, Georges; Blocker, Craig; Blocki, Jacek; Blondel, Alain; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocci, Andrea; Bock, Rudolf; Boddy, Christopher Richard; Boehler, Michael; Boek, Jennifer; Boelaert, Nele; Böser, Sebastian; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bogouch, Andrei; Bohm, Christian; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boonekamp, Maarten; Boorman, Gary; Booth, Chris; Booth, Peter; Booth, Richard; Bordoni, Stefania; Borer, Claudia; Borisov, Anatoly; Borissov, Guennadi; Borjanovic, Iris; Borroni, Sara; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boterenbrood, Hendrik; Botterill, David; Bouchami, Jihene; Boudreau, Joseph; Bouhova-Thacker, Evelina Vassileva; Boulahouache, Chaouki; Bourdarios, Claire; Bousson, Nicolas; Boveia, Antonio; Boyd, James; Boyko, Igor; Bozhko, Nikolay; Bozovic-Jelisavcic, Ivanka; Bracinik, Juraj; Braem, André; Brambilla, Elena; Branchini, Paolo; Brandenburg, George; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brelier, Bertrand; Bremer, Johan; Brenner, Richard; Bressler, Shikma; Breton, Dominique; Brett, Nicolas; Bright-Thomas, Paul; Britton, Dave; Brochu, Frederic; Brock, Ian; Brock, Raymond; Brodbeck, Timothy; Brodet, Eyal; Broggi, Francesco; Bromberg, Carl; Brooijmans, Gustaaf; Brooks, William; Brown, Gareth; Brubaker, Erik; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Brunet, Sylvie; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Buanes, Trygve; Bucci, Francesca; Buchanan, James; Buchanan, Norman; Buchholz, Peter; Buckingham, Ryan; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Budick, Burton; Büscher, Volker; Bugge, Lars; Buira-Clark, Daniel; Buis, Ernst-Jan; Bulekov, Oleg; Bunse, Moritz; Buran, Torleiv; Burckhart, Helfried; Burdin, Sergey; Burgess, Thomas; Burke, Stephen; Busato, Emmanuel; Bussey, Peter; Buszello, Claus-Peter; Butin, François; Butler, Bart; Butler, John; Buttar, Craig; Butterworth, Jonathan; Buttinger, William; Byatt, Tom; Cabrera Urbán, Susana; Caccia, Massimo; Caforio, Davide; Cakir, Orhan; Calafiura, Paolo; Calderini, Giovanni; Calfayan, Philippe; Calkins, Robert; Caloba, Luiz; Caloi, Rita; Calvet, David; Calvet, Samuel; Camard, Arnaud; Camarri, Paolo; Cambiaghi, Mario; Cameron, David; Cammin, Jochen; Campana, Simone; Campanelli, Mario; Canale, Vincenzo; Canelli, Florencia; Canepa, Anadi; Cantero, Josu; Capasso, Luciano; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capriotti, Daniele; Capua, Marcella; Caputo, Regina; Caramarcu, Costin; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Bryan; Caron, Sascha; Carpentieri, Carmen; Carrillo Montoya, German D; Carron Montero, Sebastian; Carter, Antony; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Cascella, Michele; Caso, Carlo; Castaneda Hernandez, Alfredo Martin; Castaneda-Miranda, Elizabeth; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Cataldi, Gabriella; Cataneo, Fernando; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Cattani, Giordano; Caughron, Seth; Cavallari, Alvise; Cavalleri, Pietro; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Cazzato, Antonio; Ceradini, Filippo; Cerna, Cedric; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cetin, Serkant Ali; Cevenini, Francesco; Chafaq, Aziz; Chakraborty, Dhiman; Chan, Kevin; Chapleau, Bertrand; Chapman, John Derek; Chapman, John Wehrley; Chareyre, Eve; Charlton, Dave; Chavda, Vikash; Cheatham, Susan; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chen, Hucheng; Chen, Li; Chen, Shenjian; Chen, Tingyang; Chen, Xin; Chen, Yujiao; Cheng, Shaochen; Cheplakov, Alexander; Chepurnov, Vladimir; Cherkaoui El Moursli, Rajaa; Tcherniatine, Valeri; Cheu, Elliott; Cheung, Sing-Leung; Chevalier, Laurent; Chevallier, Florent; Chiefari, Giovanni; Chikovani, Leila; Childers, John Taylor; Chilingarov, Alexandre; Chiodini, Gabriele; Chizhov, Mihail; Choudalakis, Georgios; Chouridou, Sofia; Christidi, Illectra-Athanasia; Christov, Asen; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Ciapetti, Guido; Ciftci, Abbas Kenan; Ciftci, Rena; Cinca, Diane; Cindro, Vladimir; Ciobotaru, Matei Dan; Ciocca, Claudia; Ciocio, Alessandra; Cirilli, Manuela; Ciubancan, Mihai; Clark, Allan G; Clark, Philip James; Cleland, Bill; Clemens, Jean-Claude; Clement, Benoit; Clement, Christophe; Clifft, Roger; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coe, Paul; Cogan, Joshua Godfrey; Coggeshall, James; Cogneras, Eric; Cojocaru, Claudiu; Colas, Jacques; Cole, Brian; Colijn, Auke-Pieter; Collard, Caroline; Collins, Neil; Collins-Tooth, Christopher; Collot, Johann; Colon, German; Coluccia, Rita; Comune, Gianluca; Conde Muiño, Patricia; Coniavitis, Elias; Conidi, Maria Chiara; Consonni, Michele; Constantinescu, Serban; Conta, Claudio; Conventi, Francesco; Cook, James; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cooper-Smith, Neil; Copic, Katherine; Cornelissen, Thijs; Corradi, Massimo; Correard, Sebastien; Corriveau, Francois; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Costin, Tudor; Côté, David; Coura Torres, Rodrigo; Courneyea, Lorraine; Cowan, Glen; Cowden, Christopher; Cox, Brian; Cranmer, Kyle; Cristinziani, Markus; Crosetti, Giovanni; Crupi, Roberto; Crépé-Renaudin, Sabine; Cuenca Almenar, Cristóbal; Cuhadar Donszelmann, Tulay; Cuneo, Stefano; Curatolo, Maria; Curtis, Chris; Cwetanski, Peter; Czirr, Hendrik; Czyczula, Zofia; D'Auria, Saverio; D'Onofrio, Monica; D'Orazio, Alessia; Da Rocha Gesualdi Mello, Aline; Da Silva, Paulo Vitor; Da Via, Cinzia; Dabrowski, Wladyslaw; Dahlhoff, Andrea; Dai, Tiesheng; Dallapiccola, Carlo; Dallison, Steve; Dam, Mogens; Dameri, Mauro; Damiani, Daniel; Danielsson, Hans Olof; Dankers, Reinier; Dannheim, Dominik; Dao, Valerio; Darbo, Giovanni; Darlea, Georgiana Lavinia; Daum, Cornelis; Dauvergne, Jean-Pierre; Davey, Will; Davidek, Tomas; Davidson, Nadia; Davidson, Ruth; Davies, Merlin; Davison, Adam; Dawe, Edmund; Dawson, Ian; Dawson, John; Daya, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; de Graat, Julien; De Groot, Nicolo; de Jong, Paul; De La Cruz-Burelo, Eduard; De La Taille, Christophe; De Lotto, Barbara; De Mora, Lee; De Nooij, Lucie; De Oliveira Branco, Miguel; De Pedis, Daniele; de Saintignon, Paul; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dean, Simon; Debbe, Ramiro; Dedes, George; Dedovich, Dmitri; Degenhardt, James; Dehchar, Mohamed; Deile, Mario; Del Papa, Carlo; Del Peso, Jose; Del Prete, Tarcisio; Dell'Acqua, Andrea; Dell'Asta, Lidia; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delpierre, Pierre; Delruelle, Nicolas; Delsart, Pierre-Antoine; Deluca, Carolina; Demers, Sarah; Demichev, Mikhail; Demirkoz, Bilge; Deng, Jianrong; Denisov, Sergey; Dennis, Chris; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Devetak, Erik; Deviveiros, Pier-Olivier; Dewhurst, Alastair; DeWilde, Burton; Dhaliwal, Saminder; Dhullipudi, Ramasudhakar; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Luise, Silvestro; Di Mattia, Alessandro; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Diaz, Marco Aurelio; Diblen, Faruk; Diehl, Edward; Dietl, Hans; Dietrich, Janet; Dietzsch, Thorsten; Diglio, Sara; Dindar Yagci, Kamile; Dingfelder, Jochen; Dionisi, Carlo; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djilkibaev, Rashid; Djobava, Tamar; Barros do Vale, Maria Aline; Do Valle Wemans, André; Doan, Thi Kieu Oanh; Dobbs, Matt; Dobinson, Robert; Dobos, Daniel; Dobson, Ellie; Dobson, Marc; Dodd, Jeremy; Dogan, Ozgen Berkol; Doglioni, Caterina; Doherty, Tom; Doi, Yoshikuni; Dolejsi, Jiri; Dolenc, Irena; Dolezal, Zdenek; Dolgoshein, Boris; Dohmae, Takeshi; Donadelli, Marisilvia; Donega, Mauro; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dos Anjos, Andre; Dosil, Mireia; Dotti, Andrea; Dova, Maria-Teresa; Dowell, John; Doxiadis, Alexander; Doyle, Tony; Drasal, Zbynek; Drees, Jürgen; Dressnandt, Nandor; Drevermann, Hans; Driouichi, Chafik; Dris, Manolis; Drohan, Janice; Dubbert, Jörg; Dubbs, Tim; Dube, Sourabh; Duchovni, Ehud; Duckeck, Guenter; Dudarev, Alexey; Dudziak, Fanny; Dührssen, Michael; Duerdoth, Ian; Duflot, Laurent; Dufour, Marc-Andre; Dunford, Monica; Duran Yildiz, Hatice; Duxfield, Robert; Dwuznik, Michal; Dydak, Friedrich; Dzahini, Daniel; Düren, Michael; Ebke, Johannes; Eckert, Simon; Eckweiler, Sebastian; Edmonds, Keith; Edwards, Clive; Efthymiopoulos, Ilias; Ehrenfeld, Wolfgang; Ehrich, Thies; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Eisenhandler, Eric; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Ellis, Katherine; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Ely, Robert; Emeliyanov, Dmitry; Engelmann, Roderich; Engl, Albert; Epp, Brigitte; Eppig, Andrew; Erdmann, Johannes; Ereditato, Antonio; Eriksson, Daniel; Ernst, Jesse; Ernst, Michael; Ernwein, Jean; Errede, Deborah; Errede, Steven; Ertel, Eugen; Escalier, Marc; Escobar, Carlos; Espinal Curull, Xavier; Esposito, Bellisario; Etienne, Francois; Etienvre, Anne-Isabelle; Etzion, Erez; Evangelakou, Despoina; Evans, Hal; Fabbri, Laura; Fabre, Caroline; Facius, Katrine; Fakhrutdinov, Rinat; Falciano, Speranza; Falou, Alain; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farley, Jason; Farooque, Trisha; Farrington, Sinead; Farthouat, Philippe; Fasching, Damon; Fassnacht, Patrick; Fassouliotis, Dimitrios; Fatholahzadeh, Baharak; Favareto, Andrea; Fayard, Louis; Fazio, Salvatore; Febbraro, Renato; Federic, Pavol; Fedin, Oleg; Fedorko, Ivan; Fedorko, Woiciech; Fehling-Kaschek, Mirjam; Feligioni, Lorenzo; Fellmann, Denis; Felzmann, Ulrich; Feng, Cunfeng; Feng, Eric; Fenyuk, Alexander; Ferencei, Jozef; Ferguson, Douglas; Ferland, Jonathan; Fernandes, Bruno; Fernando, Waruna; Ferrag, Samir; Ferrando, James; Ferrara, Valentina; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferrer, Antonio; Ferrer, Maria Lorenza; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filippas, Anastasios; Filthaut, Frank; Fincke-Keeler, Margret; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Gordon; Fischer, Peter; Fisher, Matthew; Fisher, Steve; Flammer, Joachim; Flechl, Martin; Fleck, Ivor; Fleckner, Johanna; Fleischmann, Philipp; Fleischmann, Sebastian; Flick, Tobias; Flores Castillo, Luis; Flowerdew, Michael; Föhlisch, Florian; Fokitis, Manolis; Fonseca Martin, Teresa; Forbush, David Alan; Formica, Andrea; Forti, Alessandra; Fortin, Dominique; Foster, Joe; Fournier, Daniel; Foussat, Arnaud; Fowler, Andrew; Fowler, Ken; Fox, Harald; Francavilla, Paolo; Franchino, Silvia; Francis, David; Frank, Tal; Franklin, Melissa; Franz, Sebastien; Fraternali, Marco; Fratina, Sasa; French, Sky; Froeschl, Robert; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gadfort, Thomas; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Gallas, Elizabeth; Gallas, Manuel; Gallo, Valentina Santina; Gallop, Bruce; Gallus, Petr; Galyaev, Eugene; Gan, KK; Gao, Yongsheng; Gapienko, Vladimir; Gaponenko, Andrei; Garberson, Ford; Garcia-Sciveres, Maurice; García, Carmen; García Navarro, José Enrique; Gardner, Robert; Garelli, Nicoletta; Garitaonandia, Hegoi; Garonne, Vincent; Garvey, John; Gatti, Claudio; Gaudio, Gabriella; Gaumer, Olivier; Gaur, Bakul; Gauthier, Lea; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gayde, Jean-Christophe; Gazis, Evangelos; Ge, Peng; Gee, Norman; Geich-Gimbel, Christoph; Gellerstedt, Karl; Gemme, Claudia; Genest, Marie-Hélène; Gentile, Simonetta; Georgatos, Fotios; George, Simon; Gerlach, Peter; Gershon, Avi; Geweniger, Christoph; Ghazlane, Hamid; Ghez, Philippe; Ghodbane, Nabil; Giacobbe, Benedetto; Giagu, Stefano; Giakoumopoulou, Victoria; Giangiobbe, Vincent; Gianotti, Fabiola; Gibbard, Bruce; Gibson, Adam; Gibson, Stephen; Gieraltowski, Gerry; Gilbert, Laura; Gilchriese, Murdock; Gildemeister, Otto; Gilewsky, Valentin; Gillberg, Dag; Gillman, Tony; Gingrich, Douglas; Ginzburg, Jonatan; Giokaris, Nikos; Giordano, Raffaele; Giorgi, Francesco Michelangelo; Giovannini, Paola; Giraud, Pierre-Francois; Giugni, Danilo; Giusti, Paolo; Gjelsten, Børge Kile; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glazov, Alexandre; Glitza, Karl-Walter; Glonti, George; Godfrey, Jennifer; Godlewski, Jan; Goebel, Martin; Göpfert, Thomas; Goeringer, Christian; Gössling, Claus; Göttfert, Tobias; Goldfarb, Steven; Goldin, Daniel; Golling, Tobias; Gollub, Nils Peter; Golovnia, Serguei; Gomes, Agostinho; Gomez Fajardo, Luz Stella; Gonçalo, Ricardo; Gonella, Laura; Gong, Chenwei; Gonidec, Allain; Gonzalez, Saul; González de la Hoz, Santiago; Gonzalez Silva, Laura; Gonzalez-Sevilla, Sergio; Goodson, Jeremiah Jet; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorfine, Grant; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Gorokhov, Serguei; Gorski, Boguslaw Tomasz; Goryachev, Vladimir; Gosdzik, Bjoern; Gosselink, Martijn; Gostkin, Mikhail Ivanovitch; Gouanère, Michel; Gough Eschrich, Ivo; Gouighri, Mohamed; Goujdami, Driss; Goulette, Marc Phillippe; Goussiou, Anna; Goy, Corinne; Grabowska-Bold, Iwona; Grabski, Varlen; Grafström, Per; Grah, Christian; Grahn, Karl-Johan; Grancagnolo, Francesco; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Grau, Nathan; Gray, Heather; Gray, Julia Ann; Graziani, Enrico; Grebenyuk, Oleg; Greenfield, Debbie; Greenshaw, Timothy; Greenwood, Zeno Dixon; Gregor, Ingrid-Maria; Grenier, Philippe; Griesmayer, Erich; Griffiths, Justin; Grigalashvili, Nugzar; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grishkevich, Yaroslav; Grivaz, Jean-Francois; Grognuz, Joel; Groh, Manfred; Gross, Eilam; Grosse-Knetter, Joern; Groth-Jensen, Jacob; Gruwe, Magali; Grybel, Kai; Guarino, Victor; Guicheney, Christophe; Guida, Angelo; Guillemin, Thibault; Guindon, Stefan; Guler, Hulya; Gunther, Jaroslav; Guo, Bin; Guo, Jun; Gupta, Ambreesh; Gusakov, Yury; Gushchin, Vladimir; Gutierrez, Andrea; Gutierrez, Phillip; Guttman, Nir; Gutzwiller, Olivier; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haas, Stefan; Haber, Carl; Hackenburg, Robert; Hadavand, Haleh Khani; Hadley, David; Haefner, Petra; Hahn, Ferdinand; Haider, Stefan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haller, Johannes; Hamacher, Klaus; Hamilton, Andrew; Hamilton, Samuel; Han, Hongguang; Han, Liang; Hanagaki, Kazunori; Hance, Michael; Handel, Carsten; Hanke, Paul; Hansen, Christian Johan; Hansen, John Renner; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Peter Henrik; Hansson, Per; Hara, Kazuhiko; Hare, Gabriel; Harenberg, Torsten; Harper, Devin; Harrington, Robert; Harris, Orin; Harrison, Karl; Hart, John; Hartert, Jochen; Hartjes, Fred; Haruyama, Tomiyoshi; Harvey, Alex; Hasegawa, Satoshi; Hasegawa, Yoji; Hassani, Samira; Hatch, Mark; Hauff, Dieter; Haug, Sigve; Hauschild, Michael; Hauser, Reiner; Havranek, Miroslav; Hawes, Brian; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Donovan; Hayakawa, Takashi; Hayden, Daniel; Hayward, Helen; Haywood, Stephen; Hazen, Eric; He, Mao; Head, Simon; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heinemann, Beate; Heisterkamp, Simon; Helary, Louis; Heldmann, Michael; Heller, Mathieu; Hellman, Sten; Helsens, Clement; Henderson, Robert; Henke, Michael; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Henry-Couannier, Frédéric; Hensel, Carsten; Henß, Tobias; Hernández Jiménez, Yesenia; Herrberg, Ruth; Hershenhorn, Alon David; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hessey, Nigel; Hidvegi, Attila; Higón-Rodriguez, Emilio; Hill, Daniel; Hill, John; Hill, Norman; Hiller, Karl Heinz; Hillert, Sonja; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hirose, Minoru; Hirsch, Florian; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoffman, Julia; Hoffmann, Dirk; Hohlfeld, Marc; Holder, Martin; Holmes, Alan; Holmgren, Sven-Olof; Holy, Tomas; Holzbauer, Jenny; Homer, Jim; Homma, Yasuhiro; Horazdovsky, Tomas; Horn, Claus; Horner, Stephan; Horton, Katherine; Hostachy, Jean-Yves; Hott, Thomas; Hou, Suen; Houlden, Michael; Hoummada, Abdeslam; Howarth, James; Howell, David; Hristova, Ivana; Hrivnac, Julius; Hruska, Ivan; Hryn'ova, Tetiana; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Huang, Guang Shun; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Hughes-Jones, Richard; Huhtinen, Mika; Hurst, Peter; Hurwitz, Martina; Husemann, Ulrich; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibbotson, Michael; Ibragimov, Iskander; Ichimiya, Ryo; Iconomidou-Fayard, Lydia; Idarraga, John; Idzik, Marek; Iengo, Paolo; Igonkina, Olga; Ikegami, Yoichi; Ikeno, Masahiro; Ilchenko, Yuri; Iliadis, Dimitrios; Imbault, Didier; Imhaeuser, Martin; Imori, Masatoshi; Ince, Tayfun; Inigo-Golfin, Joaquin; Ioannou, Pavlos; Iodice, Mauro; Ionescu, Gelu; Irles Quiles, Adrian; Ishii, Koji; Ishikawa, Akimasa; Ishino, Masaya; Ishmukhametov, Renat; Isobe, Tadaaki; Issever, Cigdem; Istin, Serhat; Itoh, Yuki; Ivashin, Anton; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jackson, Brett; Jackson, John; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakubek, Jan; Jana, Dilip; Jankowski, Ernest; Jansen, Eric; Jantsch, Andreas; Janus, Michel; Jarlskog, Göran; Jeanty, Laura; Jelen, Kazimierz; Jen-La Plante, Imai; Jenni, Peter; Jeremie, Andrea; Jež, Pavel; Jézéquel, Stéphane; Ji, Haoshuang; Ji, Weina; Jiang, Yi; Jimenez Belenguer, Marcos; Jin, Ge; Jin, Shan; Jinnouchi, Osamu; Joergensen, Morten Dam; Joffe, David; Johansen, Lars; Johansen, Marianne; Johansson, Erik; Johansson, Per; Johnert, Sebastian; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tegid; Jones, Tim; Jonsson, Ove; Joo, Kwang; Joram, Christian; Jorge, Pedro; Joseph, John; Ju, Xiangyang; Juranek, Vojtech; Jussel, Patrick; Kabachenko, Vasily; Kabana, Sonja; Kaci, Mohammed; Kaczmarska, Anna; Kadlecik, Peter; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kaiser, Steffen; Kajomovitz, Enrique; Kalinin, Sergey; Kalinovskaya, Lidia; Kama, Sami; Kanaya, Naoko; Kaneda, Michiru; Kanno, Takayuki; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kaplon, Jan; Kar, Deepak; Karagoz, Muge; Karnevskiy, Mikhail; Karr, Kristo; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kasmi, Azzedine; Kass, Richard; Kastanas, Alex; Kataoka, Mayuko; Kataoka, Yousuke; Katsoufis, Elias; Katzy, Judith; Kaushik, Venkatesh; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kayl, Manuel; Kazanin, Vassili; Kazarinov, Makhail; Kazi, Sandor Istvan; Keates, James Robert; Keeler, Richard; Kehoe, Robert; Keil, Markus; Kekelidze, George; Kelly, Marc; Kennedy, John; Kenney, Christopher John; Kenyon, Mike; Kepka, Oldrich; Kerschen, Nicolas; Kerševan, Borut Paul; Kersten, Susanne; Kessoku, Kohei; Ketterer, Christian; Khakzad, Mohsen; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Kharchenko, Dmitri; Khodinov, Alexander; Kholodenko, Anatoli; Khomich, Andrei; Khoo, Teng Jian; Khoriauli, Gia; Khovanskiy, Nikolai; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kilvington, Graham; Kim, Hyeon Jin; Kim, Min Suk; Kim, Peter; Kim, Shinhong; Kimura, Naoki; Kind, Oliver; King, Barry; King, Matthew; King, Robert Steven Beaufoy; Kirk, Julie; Kirsch, Guillaume; Kirsch, Lawrence; Kiryunin, Andrey; Kisielewska, Danuta; Kittelmann, Thomas; Kiver, Andrey; Kiyamura, Hironori; Kladiva, Eduard; Klaiber-Lodewigs, Jonas; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klemetti, Miika; Klier, Amit; Klimentov, Alexei; Klingenberg, Reiner; Klinkby, Esben; Klioutchnikova, Tatiana; Klok, Peter; Klous, Sander; Kluge, Eike-Erik; Kluge, Thomas; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knobloch, Juergen; Knue, Andrea; Ko, Byeong Rok; Kobayashi, Tomio; Kobel, Michael; Koblitz, Birger; Kocian, Martin; Kocnar, Antonin; Kodys, Peter; Köneke, Karsten; König, Adriaan; Koenig, Sebastian; König, Stefan; Köpke, Lutz; Koetsveld, Folkert; Koevesarki, Peter; Koffas, Thomas; Koffeman, Els; Kohn, Fabian; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kokott, Thomas; Kolachev, Guennady; Kolanoski, Hermann; Kolesnikov, Vladimir; Koletsou, Iro; Koll, James; Kollar, Daniel; Kollefrath, Michael; Kolya, Scott; Komar, Aston; Komaragiri, Jyothsna Rani; Kondo, Takahiko; Kono, Takanori; Kononov, Anatoly; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kootz, Andreas; Koperny, Stefan; Kopikov, Sergey; Korcyl, Krzysztof; Kordas, Kostantinos; Koreshev, Victor; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Korotkov, Vladislav; Kortner, Oliver; Kortner, Sandra; Kostyukhin, Vadim; Kotamäki, Miikka Juhani; Kotov, Serguei; Kotov, Vladislav; Kourkoumelis, Christine; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kral, Vlastimil; Kramarenko, Viktor; Kramberger, Gregor; Krasel, Olaf; Krasny, Mieczyslaw Witold; Krasznahorkay, Attila; Kraus, James; Kreisel, Arik; Krejci, Frantisek; Kretzschmar, Jan; Krieger, Nina; Krieger, Peter; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Krumshteyn, Zinovii; Kruth, Andre; Kubota, Takashi; Kuehn, Susanne; Kugel, Andreas; Kuhl, Thorsten; Kuhn, Dietmar; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kummer, Christian; Kuna, Marine; Kundu, Nikhil; Kunkle, Joshua; Kupco, Alexander; Kurashige, Hisaya; Kurata, Masakazu; Kurochkin, Yurii; Kus, Vlastimil; Kuykendall, William; Kuze, Masahiro; Kuzhir, Polina; Kvasnicka, Ondrej; Kwee, Regina; La Rosa, Alessandro; La Rotonda, Laura; Labarga, Luis; Labbe, Julien; Lacasta, Carlos; Lacava, Francesco; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Rémi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Laisne, Emmanuel; Lamanna, Massimo; Lampen, Caleb; Lampl, Walter; Lancon, Eric; Landgraf, Ulrich; Landon, Murrough; Landsman, Hagar; Lane, Jenna; Lange, Clemens; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Lapin, Vladimir; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Larionov, Anatoly; Larner, Aimee; Lasseur, Christian; Lassnig, Mario; Lau, Wing; Laurelli, Paolo; Lavorato, Antonia; Lavrijsen, Wim; Laycock, Paul; Lazarev, Alexandre; Lazzaro, Alfio; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Maner, Christophe; Le Menedeu, Eve; Leahu, Marius; Lebedev, Alexander; Lebel, Céline; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Hurng-Chun; Lee, Jason; Lee, Shih-Chang; Lee JR, Lawrence; Lefebvre, Michel; Legendre, Marie; Leger, Annie; LeGeyt, Benjamin; Legger, Federica; Leggett, Charles; Lehmacher, Marc; Lehmann Miotto, Giovanna; Lehto, Mark; Lei, Xiaowen; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lellouch, Jeremie; Leltchouk, Mikhail; Lendermann, Victor; Leney, Katharine; Lenz, Tatiana; Lenzen, Georg; Lenzi, Bruno; Leonhardt, Kathrin; Leroy, Claude; Lessard, Jean-Raphael; Lesser, Jonas; Lester, Christopher; Leung Fook Cheong, Annabelle; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levitski, Mikhail; Lewandowska, Marta; Leyton, Michael; Li, Bo; Li, Haifeng; Li, Shu; Li, Xuefei; Liang, Zhihua; Liang, Zhijun; Liberti, Barbara; Lichard, Peter; Lichtnecker, Markus; Lie, Ki; Liebig, Wolfgang; Lifshitz, Ronen; Lilley, Joseph; Limosani, Antonio; Limper, Maaike; Lin, Simon; Linde, Frank; Linnemann, James; Lipeles, Elliot; Lipinsky, Lukas; Lipniacka, Anna; Liss, Tony; Lister, Alison; Litke, Alan; Liu, Chuanlei; Liu, Dong; Liu, Hao; Liu, Jianbei; Liu, Minghui; Liu, Shengli; Liu, Yanwen; Livan, Michele; Livermore, Sarah; Lleres, Annick; Lloyd, Stephen; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Lockwitz, Sarah; Loddenkoetter, Thomas; Loebinger, Fred; Loginov, Andrey; Loh, Chang Wei; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Loken, James; Lombardo, Vincenzo Paolo; Long, Robin Eamonn; Lopes, Lourenco; Lopez Mateos, David; Losada, Marta; Loscutoff, Peter; Losterzo, Francesco; Losty, Michael; Lou, Xinchou; Lounis, Abdenour; Loureiro, Karina; Love, Jeremy; Love, Peter; Lowe, Andrew; Lu, Feng; Lu, Jiansen; Lu, Liang; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Ludwig, Andreas; Ludwig, Dörthe; Ludwig, Inga; Ludwig, Jens; Luehring, Frederick; Luijckx, Guy; Lumb, Debra; Luminari, Lamberto; Lund, Esben; Lund-Jensen, Bengt; Lundberg, Björn; Lundberg, Johan; Lundquist, Johan; Lungwitz, Matthias; Lupi, Anna; Lutz, Gerhard; Lynn, David; Lys, Jeremy; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maaßen, Michael; Macana Goia, Jorge Andres; Maccarrone, Giovanni; Macchiolo, Anna; Maček, Boštjan; Machado Miguens, Joana; Macina, Daniela; Mackeprang, Rasmus; Madaras, Ronald; Mader, Wolfgang; Maenner, Reinhard; Maeno, Tadashi; Mättig, Peter; Mättig, Stefan; Magalhaes Martins, Paulo Jorge; Magnoni, Luca; Magradze, Erekle; Magrath, Caroline; Mahalalel, Yair; Mahboubi, Kambiz; Mahout, Gilles; Maiani, Camilla; Maidantchik, Carmen; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Mal, Prolay; Malecki, Pawel; Malecki, Piotr; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mameghani, Raphael; Mamuzic, Judita; Manabe, Atsushi; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Mangeard, Pierre-Simon; Manjavidze, Ioseb; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Manz, Andreas; Mapelli, Alessandro; Mapelli, Livio; March, Luis; Marchand, Jean-Francois; Marchese, Fabrizio; Marchesotti, Marco; Marchiori, Giovanni; Marcisovsky, Michal; Marin, Alexandru; Marino, Christopher; Marroquim, Fernando; Marshall, Robin; Marshall, Zach; Martens, Kalen; Marti-Garcia, Salvador; Martin, Andrew; Martin, Brian; Martin, Brian Thomas; Martin, Franck Francois; Martin, Jean-Pierre; Martin, Philippe; Martin, Tim; Martin dit Latour, Bertrand; Martinez, Mario; Martinez Outschoorn, Verena; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Maß, Martin; Massa, Ignazio; Massaro, Graziano; Massol, Nicolas; Mastroberardino, Anna; Masubuchi, Tatsuya; Mathes, Markus; Matricon, Pierre; Matsumoto, Hiroshi; Matsunaga, Hiroyuki; Matsushita, Takashi; Mattravers, Carly; Maugain, Jean-Marie; Maxfield, Stephen; May, Edward; Mayne, Anna; Mazini, Rachid; Mazur, Michael; Mazzanti, Marcello; Mazzoni, Enrico; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; McGlone, Helen; Mchedlidze, Gvantsa; McLaren, Robert Andrew; Mclaughlan, Tom; McMahon, Steve; McMahon, Tania; McMahon, Tom; McPherson, Robert; Meade, Andrew; Mechnich, Joerg; Mechtel, Markus; Medinnis, Mike; Meera-Lebbai, Razzak; Meguro, Tatsuma; Mehdiyev, Rashid; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meinhardt, Jens; Meirose, Bernhard; Melachrinos, Constantinos; Mellado Garcia, Bruce Rafael; Mendoza Navas, Luis; Meng, Zhaoxia; Mengarelli, Alberto; Menke, Sven; Menot, Claude; Meoni, Evelin; Merkl, Doris; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meuser, Stefan; Meyer, Carsten; Meyer, Jean-Pierre; Meyer, Jochen; Meyer, Joerg; Meyer, Thomas Christian; Meyer, W Thomas; Miao, Jiayuan; Michal, Sebastien; Micu, Liliana; Middleton, Robin; Miele, Paola; Migas, Sylwia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikulec, Bettina; Mikuž, Marko; Miller, David; Miller, Robert; Mills, Bill; Mills, Corrinne; Milov, Alexander; Milstead, David; Milstein, Dmitry; Minaenko, Andrey; Miñano, Mercedes; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mirabelli, Giovanni; Miralles Verge, Lluis; Misiejuk, Andrzej; Mitra, Ankush; Mitrevski, Jovan; Mitrofanov, Gennady; Mitsou, Vasiliki A; Mitsui, Shingo; Miyagawa, Paul; Miyazaki, Kazuki; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mockett, Paul; Moed, Shulamit; Moeller, Victoria; Mönig, Klaus; Möser, Nicolas; Mohapatra, Soumya; Mohn, Bjarte; Mohr, Wolfgang; Mohrdieck-Möck, Susanne; Moisseev, Artemy; Moles-Valls, Regina; Molina-Perez, Jorge; Moneta, Lorenzo; Monk, James; Monnier, Emmanuel; Montesano, Simone; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Moorhead, Gareth; Mora Herrera, Clemencia; Moraes, Arthur; Morais, Antonio; Morange, Nicolas; Morel, Julien; Morello, Gianfranco; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morii, Masahiro; Morin, Jerome; Morita, Youhei; Morley, Anthony Keith; Mornacchi, Giuseppe; Morone, Maria-Christina; Morris, John; Moser, Hans-Guenther; Mosidze, Maia; Moss, Josh; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Mudrinic, Mihajlo; Mueller, Felix; Mueller, James; Mueller, Klemens; Müller, Thomas; Muenstermann, Daniel; Muijs, Sandra; Muir, Alex; Munwes, Yonathan; Murakami, Koichi; Murray, Bill; Mussche, Ido; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nadal, Jordi; Nagai, Koichi; Nagano, Kunihiro; Nagasaka, Yasushi; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakano, Itsuo; Nanava, Gizo; Napier, Austin; Nash, Michael; Nasteva, Irina; Nation, Nigel; Nattermann, Till; Naumann, Thomas; Navarro, Gabriela; Neal, Homer; Nebot, Eduardo; Nechaeva, Polina; Negri, Andrea; Negri, Guido; Nektarijevic, Snezana; Nelson, Andrew; Nelson, Silke; Nelson, Timothy Knight; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Nesterov, Stanislav; Neubauer, Mark; Neusiedl, Andrea; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nickerson, Richard; Nicolaidou, Rosy; Nicolas, Ludovic; Nicquevert, Bertrand; Niedercorn, Francois; Nielsen, Jason; Niinikoski, Tapio; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolaev, Kirill; Nikolic-Audit, Irena; Nikolopoulos, Konstantinos; Nilsen, Henrik; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nishiyama, Tomonori; Nisius, Richard; Nodulman, Lawrence; Nomachi, Masaharu; Nomidis, Ioannis; Nomoto, Hiroshi; Nordberg, Markus; Nordkvist, Bjoern; Norniella Francisco, Olga; Norton, Peter; Novakova, Jana; Nozaki, Mitsuaki; Nožička, Miroslav; Nugent, Ian Michael; Nuncio-Quiroz, Adriana-Elizabeth; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nyman, Tommi; O'Brien, Brendan Joseph; O'Neale, Steve; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Ocariz, Jose; Ochi, Atsuhiko; Oda, Susumu; Odaka, Shigeru; Odier, Jerome; Odino, Gian Andrea; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohshima, Takayoshi; Ohshita, Hidetoshi; Ohska, Tokio Kenneth; Ohsugi, Takashi; Okada, Shogo; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olcese, Marco; Olchevski, Alexander; Oliveira, Miguel Alfonso; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olivito, Dominick; Olszewski, Andrzej; Olszowska, Jolanta; Omachi, Chihiro; Onofre, António; Onyisi, Peter; Oram, Christopher; Ordonez, Gustavo; Oreglia, Mark; Orellana, Frederik; Oren, Yona; Orestano, Domizia; Orlov, Iliya; Oropeza Barrera, Cristina; Orr, Robert; Ortega, Eduardo; Osculati, Bianca; Ospanov, Rustem; Osuna, Carlos; Otero y Garzon, Gustavo; Ottersbach, John; Ouchrif, Mohamed; Ould-Saada, Farid; Ouraou, Ahmimed; Ouyang, Qun; Owen, Mark; Owen, Simon; Oyarzun, Alejandro; Øye, Ola; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Paganis, Efstathios; Paige, Frank; Pajchel, Katarina; Palestini, Sandro; Pallin, Dominique; Palma, Alberto; Palmer, Jody; Pan, Yibin; Panagiotopoulou, Evgenia; Panes, Boris; Panikashvili, Natalia; Panitkin, Sergey; Pantea, Dan; Panuskova, Monika; Paolone, Vittorio; Paoloni, Alessandro; Papadopoulou, Theodora; Paramonov, Alexander; Park, Su-Jung; Park, Woochun; Parker, Andy; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passeri, Antonio; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Patricelli, Sergio; Pauly, Thilo; Pecsy, Martin; Pedraza Morales, Maria Isabel; Peleganchuk, Sergey; Peng, Haiping; Pengo, Ruggero; Penson, Alexander; Penwell, John; Perantoni, Marcelo; Perez, Kerstin; Perez Cavalcanti, Tiago; Perez Codina, Estel; Pérez García-Estañ, María Teresa; Perez Reale, Valeria; Peric, Ivan; Perini, Laura; Pernegger, Heinz; Perrino, Roberto; Perrodo, Pascal; Persembe, Seda; Perus, Antoine; Peshekhonov, Vladimir; Peters, Onne; Petersen, Brian; Petersen, Jorgen; Petersen, Troels; Petit, Elisabeth; Petridis, Andreas; Petridou, Chariclia; Petrolo, Emilio; Petrucci, Fabrizio; Petschull, Dennis; Petteni, Michele; Pezoa, Raquel; Phan, Anna; Phillips, Alan; Phillips, Peter William; Piacquadio, Giacinto; Piccaro, Elisa; Piccinini, Maurizio; Pickford, Andrew; Piegaia, Ricardo; Pilcher, James; Pilkington, Andrew; Pina, João Antonio; Pinamonti, Michele; Pinfold, James; Ping, Jialun; Pinto, Belmiro; Pirotte, Olivier; Pizio, Caterina; Placakyte, Ringaile; Plamondon, Mathieu; Plano, Will; Pleier, Marc-Andre; Pleskach, Anatoly; Poblaguev, Andrei; Poddar, Sahill; Podlyski, Fabrice; Poggioli, Luc; Poghosyan, Tatevik; Pohl, Martin; Polci, Francesco; Polesello, Giacomo; Policicchio, Antonio; Polini, Alessandro; Poll, James; Polychronakos, Venetios; Pomarede, Daniel Marc; Pomeroy, Daniel; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Portell Bueso, Xavier; Porter, Robert; Posch, Christoph; Pospelov, Guennady; Pospisil, Stanislav; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Prabhu, Robindra; Pralavorio, Pascal; Prasad, Srivas; Pravahan, Rishiraj; Prell, Soeren; Pretzl, Klaus Peter; Pribyl, Lukas; Price, Darren; Price, Lawrence; Price, Michael John; Prichard, Paul; Prieur, Damien; Primavera, Margherita; Prokofiev, Kirill; Prokoshin, Fedor; Protopopescu, Serban; Proudfoot, James; Prudent, Xavier; Przysiezniak, Helenka; Psoroulas, Serena; Ptacek, Elizabeth; Purdham, John; Purohit, Milind; Puzo, Patrick; Pylypchenko, Yuriy; Qian, Jianming; Qian, Zuxuan; Qin, Zhonghua; Quadt, Arnulf; Quarrie, David; Quayle, William; Quinonez, Fernando; Raas, Marcel; Radescu, Voica; Radics, Balint; Rador, Tonguc; Ragusa, Francesco; Rahal, Ghita; Rahimi, Amir; Rajagopalan, Srinivasan; Rajek, Silke; Rammensee, Michael; Rammes, Marcus; Ramstedt, Magnus; Randrianarivony, Koloina; Ratoff, Peter; Rauscher, Felix; Rauter, Emanuel; Raymond, Michel; Read, Alexander Lincoln; Rebuzzi, Daniela; Redelbach, Andreas; Redlinger, George; Reece, Ryan; Reeves, Kendall; Reichold, Armin; Reinherz-Aronis, Erez; Reinsch, Andreas; Reisinger, Ingo; Reljic, Dusan; Rembser, Christoph; Ren, Zhongliang; Renaud, Adrien; Renkel, Peter; Rensch, Bertram; Rescigno, Marco; Resconi, Silvia; Resende, Bernardo; Reznicek, Pavel; Rezvani, Reyhaneh; Richards, Alexander; Richter, Robert; Richter-Was, Elzbieta; Ridel, Melissa; Rieke, Stefan; Rijpstra, Manouk; Rijssenbeek, Michael; Rimoldi, Adele; Rinaldi, Lorenzo; Rios, Ryan Randy; Riu, Imma; Rivoltella, Giancesare; Rizatdinova, Flera; Rizvi, Eram; Robertson, Steven; Robichaud-Veronneau, Andree; Robinson, Dave; Robinson, James; Robinson, Mary; Robson, Aidan; Rocha de Lima, Jose Guilherme; Roda, Chiara; Roda Dos Santos, Denis; Rodier, Stephane; Rodriguez, Diego; Rodriguez Garcia, Yohany; Roe, Adam; Roe, Shaun; Røhne, Ole; Rojo, Victoria; Rolli, Simona; Romaniouk, Anatoli; Romanov, Victor; Romeo, Gaston; Romero Maltrana, Diego; Roos, Lydia; Ros, Eduardo; Rosati, Stefano; Rose, Matthew; Rosenbaum, Gabriel; Rosenberg, Eli; Rosendahl, Peter Lundgaard; Rosselet, Laurent; Rossetti, Valerio; Rossi, Elvira; Rossi, Leonardo Paolo; Rossi, Lucio; Rotaru, Marina; Roth, Itamar; Rothberg, Joseph; Rottländer, Iris; Rousseau, David; Royon, Christophe; Rozanov, Alexander; Rozen, Yoram; Ruan, Xifeng; Rubinskiy, Igor; Ruckert, Benjamin; Ruckstuhl, Nicole; Rud, Viacheslav; Rudolph, Gerald; Rühr, Frederik; Ruiz-Martinez, Aranzazu; Rulikowska-Zarebska, Elzbieta; Rumiantsev, Viktor; Rumyantsev, Leonid; Runge, Kay; Runolfsson, Ogmundur; Rurikova, Zuzana; Rusakovich, Nikolai; Rust, Dave; Rutherfoord, John; Ruwiedel, Christoph; Ruzicka, Pavel; Ryabov, Yury; Ryadovikov, Vasily; Ryan, Patrick; Rybar, Martin; Rybkin, Grigori; Ryder, Nick; Rzaeva, Sevda; Saavedra, Aldo; Sadeh, Iftach; Sadrozinski, Hartmut; Sadykov, Renat; Safai Tehrani, Francesco; Sakamoto, Hiroshi; Salamanna, Giuseppe; Salamon, Andrea; Saleem, Muhammad; Salihagic, Denis; Salnikov, Andrei; Salt, José; Salvachua Ferrando, Belén; Salvatore, Daniela; Salvatore, Pasquale Fabrizio; Salzburger, Andreas; Sampsonidis, Dimitrios; Samset, Björn Hallvard; Sandaker, Heidi; Sander, Heinz Georg; Sanders, Michiel; Sandhoff, Marisa; Sandhu, Pawan; Sandoval, Tanya; Sandstroem, Rikard; Sandvoss, Stephan; Sankey, Dave; Sansoni, Andrea; Santamarina Rios, Cibran; Santoni, Claudio; Santonico, Rinaldo; Santos, Helena; Saraiva, João; Sarangi, Tapas; Sarkisyan-Grinbaum, Edward; Sarri, Francesca; Sartisohn, Georg; Sasaki, Osamu; Sasaki, Takashi; Sasao, Noboru; Satsounkevitch, Igor; Sauvage, Gilles; Sauvan, Jean-Baptiste; Savard, Pierre; Savinov, Vladimir; Savva, Panagiota; Sawyer, Lee; Saxon, David; Says, Louis-Pierre; Sbarra, Carla; Sbrizzi, Antonio; Scallon, Olivia; Scannicchio, Diana; Schaarschmidt, Jana; Schacht, Peter; 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Dean; Schamov, Andrey; Scharf, Veit; Schegelsky, Valery; Scheirich, Daniel; Scherzer, Max; Schiavi, Carlo; Schieck, Jochen; Schioppa, Marco; Schlenker, Stefan; Schlereth, James; Schmidt, Evelyn; Schmidt, Michael; Schmieden, Kristof; Schmitt, Christian; Schmitz, Martin; Schöning, André; Schott, Matthias; Schouten, Doug; Schovancova, Jaroslava; Schram, Malachi; Schreiner, Alexander; Schroeder, Christian; Schroer, Nicolai; Schuh, Silvia; Schuler, Georges; Schultes, Joachim; Schultz-Coulon, Hans-Christian; Schulz, Holger; Schumacher, Jan; Schumacher, Markus; Schumm, Bruce; Schune, Philippe; Schwanenberger, Christian; Schwartzman, Ariel; Schwemling, Philippe; Schwienhorst, Reinhard; Schwierz, Rainer; Schwindling, Jerome; Scott, Bill; Searcy, Jacob; Sedykh, Evgeny; Segura, Ester; Seidel, Sally; Seiden, Abraham; Seifert, Frank; Seixas, José; Sekhniaidze, Givi; Seliverstov, Dmitry; Sellden, Bjoern; Sellers, Graham; Seman, Michal; Semprini-Cesari, Nicola; Serfon, Cedric; Serin, Laurent; Seuster, Rolf; 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Smirnova, Oxana; Smith, Ben Campbell; Smith, Douglas; Smith, Kenway; Smizanska, Maria; Smolek, Karel; Snesarev, Andrei; Snow, Steve; Snow, Joel; Snuverink, Jochem; Snyder, Scott; Soares, Mara; Sobie, Randall; Sodomka, Jaromir; Soffer, Abner; Solans, Carlos; Solar, Michael; Solc, Jaroslav; Soldevila, Urmila; Solfaroli Camillocci, Elena; Solodkov, Alexander; Solovyanov, Oleg; Sondericker, John; Soni, Nitesh; Sopko, Vit; Sopko, Bruno; Sorbi, Massimo; Sosebee, Mark; Soukharev, Andrey; Spagnolo, Stefania; Spanò, Francesco; Spighi, Roberto; Spigo, Giancarlo; Spila, Federico; Spiriti, Eleuterio; Spiwoks, Ralf; Spousta, Martin; Spreitzer, Teresa; Spurlock, Barry; St Denis, Richard Dante; Stahl, Thorsten; Stahlman, Jonathan; Stamen, Rainer; Stanecka, Ewa; Stanek, Robert; Stanescu, Cristian; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staude, Arnold; Stavina, Pavel; Stavropoulos, Georgios; Steele, Genevieve; Steinbach, Peter; Steinberg, Peter; Stekl, Ivan; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stevenson, Kyle; Stewart, Graeme; Stockmanns, Tobias; Stockton, Mark; Stoerig, Kathrin; Stoicea, Gabriel; Stonjek, Stefan; Strachota, Pavel; Stradling, Alden; Straessner, Arno; Strandberg, Jonas; Strandberg, Sara; Strandlie, Are; Strang, Michael; Strauss, Emanuel; Strauss, Michael; Strizenec, Pavol; Ströhmer, Raimund; Strom, David; Strong, John; Stroynowski, Ryszard; Strube, Jan; Stugu, Bjarne; Stumer, Iuliu; Stupak, John; Sturm, Philipp; Soh, Dart-yin; Su, Dong; Subramania, Siva; Sugaya, Yorihito; Sugimoto, Takuya; Suhr, Chad; Suita, Koichi; Suk, Michal; Sulin, Vladimir; Sultansoy, Saleh; Sumida, Toshi; Sun, Xiaohu; Sundermann, Jan Erik; Suruliz, Kerim; Sushkov, Serge; Susinno, Giancarlo; Sutton, Mark; Suzuki, Yu; Sviridov, Yuri; Swedish, Stephen; Sykora, Ivan; Sykora, Tomas; Szeless, Balazs; Sánchez, Javier; Ta, Duc; Tackmann, Kerstin; Taffard, Anyes; Tafirout, Reda; Taga, Adrian; Taiblum, Nimrod; Takahashi, Yuta; Takai, Helio; Takashima, Ryuichi; Takeda, Hiroshi; Takeshita, Tohru; Talby, Mossadek; Talyshev, Alexey; Tamsett, Matthew; Tanaka, Junichi; Tanaka, Reisaburo; Tanaka, Satoshi; Tanaka, Shuji; Tanaka, Yoshito; Tani, Kazutoshi; Tannoury, Nancy; Tappern, Geoffrey; Tapprogge, Stefan; Tardif, Dominique; Tarem, Shlomit; Tarrade, Fabien; Tartarelli, Giuseppe Francesco; Tas, Petr; Tasevsky, Marek; Tassi, Enrico; Tatarkhanov, Mous; Taylor, Christopher; Taylor, Frank; Taylor, Gary; Taylor, Geoffrey; Taylor, Wendy; Teixeira Dias Castanheira, Matilde; Teixeira-Dias, Pedro; Temming, Kim Katrin; Ten Kate, Herman; Teng, Ping-Kun; Tennenbaum-Katan, Yaniv-David; Terada, Susumu; Terashi, Koji; Terron, Juan; Terwort, Mark; Testa, Marianna; Teuscher, Richard; Tevlin, Christopher; Thadome, Jocelyn; Therhaag, Jan; Theveneaux-Pelzer, Timothée; Thioye, Moustapha; Thoma, Sascha; Thomas, Juergen; Thompson, Emily; Thompson, Paul; Thompson, Peter; Thompson, Stan; Thomson, Evelyn; Thomson, Mark; Thun, Rudolf; Tic, Tomas; Tikhomirov, Vladimir; Tikhonov, Yury; Timmermans, Charles; Tipton, Paul; Tique Aires Viegas, Florbela De Jes; Tisserant, Sylvain; Tobias, Jürgen; Toczek, Barbara; Todorov, Theodore; Todorova-Nova, Sharka; Toggerson, Brokk; Tojo, Junji; Tokár, Stanislav; Tokunaga, Kaoru; Tokushuku, Katsuo; Tollefson, Kirsten; Tomoto, Makoto; Tompkins, Lauren; Toms, Konstantin; Tonazzo, Alessandra; Tong, Guoliang; Tonoyan, Arshak; Topfel, Cyril; Topilin, Nikolai; Torchiani, Ingo; Torrence, Eric; Torró Pastor, Emma; Toth, Jozsef; Touchard, Francois; Tovey, Daniel; Traynor, Daniel; Trefzger, Thomas; Treis, Johannes; Tremblet, Louis; Tricoli, Alesandro; Trigger, Isabel Marian; Trincaz-Duvoid, Sophie; Trinh, Thi Nguyet; Tripiana, Martin; Triplett, Nathan; Trischuk, William; Trivedi, Arjun; Trocmé, Benjamin; Troncon, Clara; Trottier-McDonald, Michel; Trzupek, Adam; Tsarouchas, Charilaos; Tseng, Jeffrey; Tsiakiris, Menelaos; Tsiareshka, Pavel; Tsionou, Dimitra; Tsipolitis, Georgios; Tsiskaridze, Vakhtang; Tskhadadze, Edisher; Tsukerman, Ilya; Tsulaia, Vakhtang; Tsung, Jieh-Wen; Tsuno, Soshi; Tsybychev, Dmitri; Tua, Alan; Tuggle, Joseph; Turala, Michal; Turecek, Daniel; Turk Cakir, Ilkay; Turlay, Emmanuel; Tuts, Michael; Tykhonov, Andrii; Tylmad, Maja; Tyndel, Mike; Typaldos, Dimitrios; Tyrvainen, Harri; Tzanakos, George; Uchida, Kirika; Ueda, Ikuo; Ueno, Ryuichi; Ugland, Maren; Uhlenbrock, Mathias; Uhrmacher, Michael; Ukegawa, Fumihiko; Unal, Guillaume; Underwood, David; Undrus, Alexander; Unel, Gokhan; Unno, Yoshinobu; Urbaniec, Dustin; Urkovsky, Evgeny; Urquijo, Phillip; Urrejola, Pedro; Usai, Giulio; Uslenghi, Massimiliano; Vacavant, Laurent; Vacek, Vaclav; Vachon, Brigitte; Vahsen, Sven; Valderanis, Chrysostomos; Valenta, Jan; Valente, Paolo; Valentinetti, Sara; Valkar, Stefan; Valladolid Gallego, Eva; Vallecorsa, Sofia; Valls Ferrer, Juan Antonio; van der Graaf, Harry; van der Kraaij, Erik; van der Poel, Egge; van der Ster, Daniel; Van Eijk, Bob; van Eldik, Niels; van Gemmeren, Peter; van Kesteren, Zdenko; van Vulpen, Ivo; Vandelli, Wainer; Vandoni, Giovanna; Vaniachine, Alexandre; Vankov, Peter; Vannucci, Francois; Varela Rodriguez, Fernando; Vari, Riccardo; Varnes, Erich; Varouchas, Dimitris; Vartapetian, Armen; Varvell, Kevin; Vassilakopoulos, Vassilios; Vazeille, Francois; Vegni, Guido; Veillet, Jean-Jacques; Vellidis, Constantine; Veloso, Filipe; Veness, Raymond; Veneziano, Stefano; Ventura, Andrea; Ventura, Daniel; Ventura, Silvia; Venturi, Manuela; Venturi, Nicola; Vercesi, Valerio; Verducci, Monica; Verkerke, Wouter; Vermeulen, Jos; Vest, Anja; Vetterli, Michel; Vichou, Irene; Vickey, Trevor; Viehhauser, Georg; Viel, Simon; Villa, Mauro; Villaplana Perez, Miguel; Vilucchi, Elisabetta; Vincter, Manuella; Vinek, Elisabeth; Vinogradov, Vladimir; Virchaux, Marc; Viret, Sébastien; Virzi, Joseph; Vitale, Antonio; Vitells, Ofer; Vivarelli, Iacopo; Vives Vaque, Francesc; Vlachos, Sotirios; Vlasak, Michal; Vlasov, Nikolai; Vogel, Adrian; Vokac, Petr; Volpi, Matteo; Volpini, Giovanni; von der Schmitt, Hans; von Loeben, Joerg; von Radziewski, Holger; von Toerne, Eckhard; Vorobel, Vit; Vorobiev, Alexander; Vorwerk, Volker; Vos, Marcel; Voss, Rudiger; Voss, Thorsten Tobias; Vossebeld, Joost; Vovenko, Anatoly; Vranjes, Nenad; Vranjes Milosavljevic, Marija; Vrba, Vaclav; Vreeswijk, Marcel; Vu Anh, Tuan; Vuillermet, Raphael; Vukotic, Ilija; Wagner, Wolfgang; Wagner, Peter; Wahlen, Helmut; Wakabayashi, Jun; Walbersloh, Jorg; Walch, Shannon; Walder, James; Walker, Rodney; Walkowiak, Wolfgang; Wall, Richard; Waller, Peter; Wang, Chiho; Wang, Haichen; Wang, Jike; Wang, Jin; Wang, Joshua C; Wang, Song-Ming; Warburton, Andreas; Ward, Patricia; Warsinsky, Markus; Watkins, Peter; Watson, Alan; Watson, Miriam; Watts, Gordon; Watts, Stephen; Waugh, Anthony; Waugh, Ben; Weber, Jens; Weber, Marc; Weber, Michele; Weber, Pavel; Weidberg, Anthony; Weingarten, Jens; Weiser, Christian; Wellenstein, Hermann; Wells, Phillippa; Wen, Mei; Wenaus, Torre; Wendler, Shanti; Weng, Zhili; Wengler, Thorsten; Wenig, Siegfried; Wermes, Norbert; Werner, Matthias; Werner, Per; Werth, Michael; Wessels, Martin; Whalen, Kathleen; Wheeler-Ellis, Sarah Jane; Whitaker, Scott; White, Andrew; White, Martin; White, Sebastian; Whitehead, Samuel Robert; Whiteson, Daniel; Whittington, Denver; Wicek, Francois; Wicke, Daniel; Wickens, Fred; Wiedenmann, Werner; Wielers, Monika; Wienemann, Peter; Wiglesworth, Craig; Wiik, Liv Antje Mari; Wildauer, Andreas; Wildt, Martin Andre; Wilhelm, Ivan; Wilkens, Henric George; Will, Jonas Zacharias; Williams, Eric; Williams, Hugh; Willis, William; Willocq, Stephane; Wilson, John; Wilson, Michael Galante; Wilson, Alan; Wingerter-Seez, Isabelle; Winkelmann, Stefan; Winklmeier, Frank; Wittgen, Matthias; Wolter, Marcin Wladyslaw; Wolters, Helmut; Wooden, Gemma; Wosiek, Barbara; Wotschack, Jorg; Woudstra, Martin; Wraight, Kenneth; Wright, Catherine; Wrona, Bozydar; Wu, Sau Lan; Wu, Xin; Wulf, Evan; Wunstorf, Renate; Wynne, Benjamin; Xaplanteris, Leonidas; Xella, Stefania; Xie, Song; Xie, Yigang; Xu, Chao; Xu, Da; Xu, Guofa; Yabsley, Bruce; Yamada, Miho; Yamamoto, Akira; Yamamoto, Kyoko; Yamamoto, Shimpei; Yamamura, Taiki; Yamaoka, Jared; Yamazaki, Takayuki; Yamazaki, Yuji; Yan, Zhen; Yang, Haijun; Yang, Un-Ki; Yang, Yi; Yang, Yi; Yang, Zhaoyu; Yanush, Serguei; Yao, Weiming; Yao, Yushu; Yasu, Yoshiji; Ye, Jingbo; Ye, Shuwei; Yilmaz, Metin; Yoosoofmiya, Reza; Yorita, Kohei; Yoshida, Riktura; Young, Charles; Youssef, Saul; Yu, Dantong; Yu, Jaehoon; Yu, Jie; Yuan, Li; Yurkewicz, Adam; Zaets, Vassilli; Zaidan, Remi; Zaitsev, Alexander; Zajacova, Zuzana; Zalite, Youris; Zanello, Lucia; Zarzhitsky, Pavel; Zaytsev, Alexander; Zdrazil, Marian; Zeitnitz, Christian; Zeller, Michael; Zema, Pasquale Federico; Zemla, Andrzej; Zendler, Carolin; Zenin, Anton; Zenin, Oleg; Ženiš, Tibor; Zenonos, Zenonas; Zenz, Seth; Zerwas, Dirk; Zevi della Porta, Giovanni; Zhan, Zhichao; Zhang, Huaqiao; Zhang, Jinlong; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Long; Zhao, Tianchi; Zhao, Zhengguo; Zhemchugov, Alexey; Zheng, Shuchen; Zhong, Jiahang; Zhou, Bing; Zhou, Ning; Zhou, Yue; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Yingchun; Zhuang, Xuai; Zhuravlov, Vadym; Zieminska, Daria; Zilka, Branislav; Zimmermann, Robert; Zimmermann, Simone; Zimmermann, Stephanie; Ziolkowski, Michael; Zitoun, Robert; Živković, Lidija; Zmouchko, Viatcheslav; Zobernig, Georg; Zoccoli, Antonio; Zolnierowski, Yves; Zsenei, Andras; zur Nedden, Martin; Zutshi, Vishnu; Zwalinski, Lukasz
2010-01-01
Using the ATLAS detector, observations have been made of a centrality-dependent dijet asymmetry in the collisions of lead ions at the Large Hadron Collider. In a sample of lead-lead events with a per-nucleon center of mass energy of 2.76 TeV, selected with a minimum bias trigger, jets are reconstructed in fine-grained, longitudinally-segmented electromagnetic and hadronic calorimeters. The underlying event is measured and subtracted event-by-event, giving estimates of jet transverse energy above the ambient background. The transverse energies of dijets in opposite hemispheres is observed to become systematically more unbalanced with increasing event centrality leading to a large number of events which contain highly asymmetric dijets. This is the first observation of an enhancement of events with such large dijet asymmetries, not observed in proton-proton collisions, and which may point to an interpretation in terms of strong jet energy loss in a hot, dense medium.
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Characterization and prediction of extreme events in turbulence
Fonda, Enrico; Iyer, Kartik P.; Sreenivasan, Katepalli R.
2017-11-01
Extreme events in Nature such as tornadoes, large floods and strong earthquakes are rare but can have devastating consequences. The predictability of these events is very limited at present. Extreme events in turbulence are the very large events in small scales that are intermittent in character. We examine events in energy dissipation rate and enstrophy which are several tens to hundreds to thousands of times the mean value. To this end we use our DNS database of homogeneous and isotropic turbulence with Taylor Reynolds numbers spanning a decade, computed with different small scale resolutions and different box sizes, and study the predictability of these events using machine learning. We start with an aggressive data augmentation to virtually increase the number of these rare events by two orders of magnitude and train a deep convolutional neural network to predict their occurrence in an independent data set. The goal of the work is to explore whether extreme events can be predicted with greater assurance than can be done by conventional methods (e.g., D.A. Donzis & K.R. Sreenivasan, J. Fluid Mech. 647, 13-26, 2010).
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Search for FCNC in top-quark events in ATLAS
Cristinziani, Markus
2013-01-01
Searches for flavor changing neutral current (FCNC) processes in top-quark production and decays by the ATLAS Collaboration are presented. Data collected from $pp$ collisions at the LHC at a centre-of-mass energy of 7 TeV during 2011, corresponding to an integrated luminosity of 2.05/fb, are used. In a first analysis single top-quarks produced via FCNC are searched for. Candidate events with a semileptonic top-quark decay signature are classified as signal or background-like events by using several kinematic variables as input to a neural network. No signal is observed in the neural network output distribution and a Bayesian upper limit is placed on the production cross-section. The observed upper limit is converted using a model-independent approach into upper limits on the coupling strengths k_{ugt}/Lambda ug) cg) Zq FCNC (q=u,c) channel, and the other through the Standard Model dominant mode t-->Wb. Only the decays of the Z boson to charged leptons and leptonic W-boson decays are considered as signal. No e...
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Search for FCNC in top-quark events in ATLAS
Cristinziani, M; The ATLAS collaboration
2012-01-01
Searches for flavor changing neutral current (FCNC) processes in top-quark production and decays by the ATLAS Collaboration are presented. Data collected from $pp$ collisions at the LHC at a centre-of-mass energy of $\\sqrt{s}=7$ GeV during 2011, corresponding to an integrated luminosity of 2.05 fb$^{-1}$, are used. In a first analysis single top-quarks produced via FCNC are searched for. Candidate events with a semileptonic top-quark decay signature are classified as signal or background-like events by using several kinematic variables as input to a neural network. No signal is observed in the neural network output distribution and a Bayesian upper limit is placed on the production cross-section. The observed upper limit is converted using a model-independent approach into upper limits on the coupling strengths $\\kappa_{ugt}/\\Lambda < 6.9\\cdot 10^{-3}$ TeV$^{-1}$ and $\\kappa_{cgt}/\\Lambda < 1.6 \\cdot 10^{-2}$ TeV$^{-1}$, where $\\Lambda$ is the new physics scale, and on the branching fractions ${\\cal B}(...
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Gelatin methacrylamide hydrogel with graphene nanoplatelets for neural cell-laden 3D bioprinting.
Wei Zhu; Harris, Brent T; Zhang, Lijie Grace
2016-08-01
Nervous system is extremely complex which leads to rare regrowth of nerves once injury or disease occurs. Advanced 3D bioprinting strategy, which could simultaneously deposit biocompatible materials, cells and supporting components in a layer-by-layer manner, may be a promising solution to address neural damages. Here we presented a printable nano-bioink composed of gelatin methacrylamide (GelMA), neural stem cells, and bioactive graphene nanoplatelets to target nerve tissue regeneration in the assist of stereolithography based 3D bioprinting technique. We found the resultant GelMA hydrogel has a higher compressive modulus with an increase of GelMA concentration. The porous GelMA hydrogel can provide a biocompatible microenvironment for the survival and growth of neural stem cells. The cells encapsulated in the hydrogel presented good cell viability at the low GelMA concentration. Printed neural construct exhibited well-defined architecture and homogenous cell distribution. In addition, neural stem cells showed neuron differentiation and neurites elongation within the printed construct after two weeks of culture. These findings indicate the 3D bioprinted neural construct has great potential for neural tissue regeneration.
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A digital pixel cell for address event representation image convolution processing
Camunas-Mesa, Luis; Acosta-Jimenez, Antonio; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe
2005-06-01
Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number of neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate events according to their information levels. Neurons with more information (activity, derivative of activities, contrast, motion, edges,...) generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. AER technology has been used and reported for the implementation of various type of image sensors or retinae: luminance with local agc, contrast retinae, motion retinae,... Also, there has been a proposal for realizing programmable kernel image convolution chips. Such convolution chips would contain an array of pixels that perform weighted addition of events. Once a pixel has added sufficient event contributions to reach a fixed threshold, the pixel fires an event, which is then routed out of the chip for further processing. Such convolution chips have been proposed to be implemented using pulsed current mode mixed analog and digital circuit techniques. In this paper we present a fully digital pixel implementation to perform the weighted additions and fire the events. This way, for a given technology, there is a fully digital implementation reference against which compare the mixed signal implementations. We have designed, implemented and tested a fully digital AER convolution pixel. This pixel will be used to implement a full AER convolution chip for programmable kernel image convolution processing.
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[Folic acid: Primary prevention of neural tube defects. Literature Review].
Llamas Centeno, M J; Miguélez Lago, C
2016-03-01
Neural tube defects (NTD) are the most common congenital malformations of the nervous system, they have a multifactorial etiology, are caused by exposure to chemical, physical or biological toxic agents, factors deficiency, diabetes, obesity, hyperthermia, genetic alterations and unknown causes. Some of these factors are associated with malnutrition by interfering with the folic acid metabolic pathway, the vitamin responsible for neural tube closure. Its deficit produce anomalies that can cause abortions, stillbirths or newborn serious injuries that cause disability, impaired quality of life and require expensive treatments to try to alleviate in some way the alterations produced in the embryo. Folic acid deficiency is considered the ultimate cause of the production of neural tube defects, it is clear the reduction in the incidence of Espina Bifida after administration of folic acid before conception, this leads us to want to further study the action of folic acid and its application in the primary prevention of neural tube defects. More than 40 countries have made the fortification of flour with folate, achieving encouraging data of decrease in the prevalence of neural tube defects. This paper attempts to make a literature review, which clarify the current situation and future of the prevention of neural tube defects.
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Modulation of neural activity during object naming: Effects of time and practice
Turennout, M.I. van; Bielamowicz, L.; Martin, A.
2003-01-01
Repeated exposure to objects improves our ability to identify and name them, even after a long delay. Previous brain imaging studies have demonstrated that this experience-related facilitation of object naming is associated with neural changes in distinct brain regions. We used event-related
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Anger under control: neural correlates of frustration as a function of trait aggression.
Directory of Open Access Journals (Sweden)
Christina M Pawliczek
Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.
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ATLAS event at 13 TeV - Highest mass dijets angular event in 2015 data
ATLAS Collaboration
2015-01-01
The highest-mass dijet event passing the angular selection collected in 2015 (Event 478442529, Run 280464): the two central high-pT jets have an invariant mass of 7.9 TeV, the three leading jets have a pT of 1.99, 1.86 and 0.74 TeV respectively. The missing transverse momentum in this event is 46 GeV
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Moreau, Marc; Néant, Isabelle; Webb, Sarah E; Miller, Andrew L; Riou, Jean-François; Leclerc, Catherine
2016-03-01
During embryogenesis, a rise in intracellular Ca(2+) is known to be a widespread trigger for directing stem cells towards a specific tissue fate, but the precise Ca(2+) signalling mechanisms involved in achieving these pleiotropic effects are still poorly understood. In this review, we compare the Ca(2+) signalling events that appear to be one of the first steps in initiating and regulating both neural determination (neural induction) and kidney development (nephrogenesis). We have highlighted the necessary and sufficient role played by Ca(2+) influx and by Ca(2+) transients in the determination and differentiation of pools of neural or renal precursors. We have identified new Ca(2+) target genes involved in neural induction and we showed that the same Ca(2+) early target genes studied are not restricted to neural tissue but are also present in other tissues, principally in the pronephros. In this review, we also described a mechanism whereby the transcriptional control of gene expression during neurogenesis and nephrogenesis might be directly controlled by Ca(2+) signalling. This mechanism involves members of the Kcnip family such that a change in their binding properties to specific DNA sites is a result of Ca(2+) binding to EF-hand motifs. The different functions of Ca(2+) signalling during these two events illustrate the versatility of Ca(2+) as a second messenger. Copyright © 2015 Elsevier Ltd. All rights reserved.
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ATLAS Event Display: Run 2 Heavy Ion Collision
ATLAS Collaboration
2018-01-01
Event display of a lead-lead collision with a large transverse momentum photon. In this event, the expected balancing jet is not visible by eye, consistent with it being degraded by its passage through the quark-gluon plasma.
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Stimulus-dependent suppression of chaos in recurrent neural networks
International Nuclear Information System (INIS)
Rajan, Kanaka; Abbott, L. F.; Sompolinsky, Haim
2010-01-01
Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a 'resonant' frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.
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Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.
Ly, Cheng
2015-12-01
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.
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Kim, Suhwan; Baek, Juyeong; Jung, Unsang; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon
2013-05-01
Recently, Mouse neuroblastoma cells are considered as an attractive model for the study of human neurological and prion diseases, and intensively used as a model system in different areas. Among those areas, differentiation of neuro2a (N2A) cells, receptor mediated ion current, and glutamate induced physiological response are actively investigated. The reason for the interest to mouse neuroblastoma N2A cells is that they have a fast growing rate than other cells in neural origin with a few another advantages. This study evaluated the calcium oscillations and neural spikes recording of mouse neuroblastoma N2A cells in an epileptic condition. Based on our observation of neural spikes in mouse N2A cell with our proposed imaging modality, we report that mouse neuroblastoma N2A cells can be an important model related to epileptic activity studies. It is concluded that the mouse neuroblastoma N2A cells produce the epileptic spikes in vitro in the same way as produced by the neurons or the astrocytes. This evidence advocates the increased and strong level of neurotransmitters release by enhancement in free calcium using the 4-aminopyridine which causes the mouse neuroblastoma N2A cells to produce the epileptic spikes and calcium oscillation.
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Directory of Open Access Journals (Sweden)
Yuanquan Song
2009-12-01
Full Text Available In humans, mutations in electron transfer flavoprotein (ETF or electron transfer flavoprotein dehydrogenase (ETFDH lead to MADD/glutaric aciduria type II, an autosomal recessively inherited disorder characterized by a broad spectrum of devastating neurological, systemic and metabolic symptoms. We show that a zebrafish mutant in ETFDH, xavier, and fibroblast cells from MADD patients demonstrate similar mitochondrial and metabolic abnormalities, including reduced oxidative phosphorylation, increased aerobic glycolysis, and upregulation of the PPARG-ERK pathway. This metabolic dysfunction is associated with aberrant neural proliferation in xav, in addition to other neural phenotypes and paralysis. Strikingly, a PPARG antagonist attenuates aberrant neural proliferation and alleviates paralysis in xav, while PPARG agonists increase neural proliferation in wild type embryos. These results show that mitochondrial dysfunction, leading to an increase in aerobic glycolysis, affects neurogenesis through the PPARG-ERK pathway, a potential target for therapeutic intervention.
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Emotion identification and aging: Behavioral and neural age-related changes.
Gonçalves, Ana R; Fernandes, Carina; Pasion, Rita; Ferreira-Santos, Fernando; Barbosa, Fernando; Marques-Teixeira, João
2018-05-01
Aging is known to alter the processing of facial expressions of emotion (FEE), however the impact of this alteration is less clear. Additionally, there is little information about the temporal dynamics of the neural processing of facial affect. We examined behavioral and neural age-related changes in the identification of FEE using event-related potentials. Furthermore, we analyze the relationship between behavioral/neural responses and neuropsychological functioning. To this purpose, 30 younger adults, 29 middle-aged adults and 26 older adults identified FEE. The behavioral results showed a similar performance between groups. The neural results showed no significant differences between groups for the P100 component and an increased N170 amplitude in the older group. Furthermore, a pattern of asymmetric activation was evident in the N170 component. Results also suggest deficits in facial feature decoding abilities, reflected by a reduced N250 amplitude in older adults. Neuropsychological functioning predicts P100 modulation, but does not seem to influence emotion identification ability. The findings suggest the existence of a compensatory function that would explain the age-equivalent performance in emotion identification. The study may help future research addressing behavioral and neural processes involved on processing of FEE in neurodegenerative conditions. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
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Neural processing of short-term recurrence in songbird vocal communication.
Directory of Open Access Journals (Sweden)
Gabriël J L Beckers
Full Text Available BACKGROUND: Many situations involving animal communication are dominated by recurring, stereotyped signals. How do receivers optimally distinguish between frequently recurring signals and novel ones? Cortical auditory systems are known to be pre-attentively sensitive to short-term delivery statistics of artificial stimuli, but it is unknown if this phenomenon extends to the level of behaviorally relevant delivery patterns, such as those used during communication. METHODOLOGY/PRINCIPAL FINDINGS: We recorded and analyzed complete auditory scenes of spontaneously communicating zebra finch (Taeniopygia guttata pairs over a week-long period, and show that they can produce tens of thousands of short-range contact calls per day. Individual calls recur at time scales (median interval 1.5 s matching those at which mammalian sensory systems are sensitive to recent stimulus history. Next, we presented to anesthetized birds sequences of frequently recurring calls interspersed with rare ones, and recorded, in parallel, action and local field potential responses in the medio-caudal auditory forebrain at 32 unique sites. Variation in call recurrence rate over natural ranges leads to widespread and significant modulation in strength of neural responses. Such modulation is highly call-specific in secondary auditory areas, but not in the main thalamo-recipient, primary auditory area. CONCLUSIONS/SIGNIFICANCE: Our results support the hypothesis that pre-attentive neural sensitivity to short-term stimulus recurrence is involved in the analysis of auditory scenes at the level of delivery patterns of meaningful sounds. This may enable birds to efficiently and automatically distinguish frequently recurring vocalizations from other events in their auditory scene.
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Application of recurrent neural networks for drought projections in California
Le, J. A.; El-Askary, H. M.; Allali, M.; Struppa, D. C.
2017-05-01
We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015-2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998-1999 El Niño event. RNN training considered PZI data during 1896-2006 that was validated against the 2006-2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1 month. This gives strong confidence to the forecasted precipitation indicator. The 2015-2016 El Niño season proved to be relatively weak as compared with the 1997-1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions.
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Identification of radon anomalies in soil gas using decision trees and neural networks
International Nuclear Information System (INIS)
Zmazek, B.; Dzeroski, S.; Torkar, D.; Vaupotic, J.; Kobal, I.
2010-01-01
The time series of radon ( 222 Rn) concentration in soil gas at a fault, together with the environmental parameters, have been analysed applying two machine learning techniques: (I) decision trees and (II) neural networks, with the aim at identifying radon anomalies caused by seismic events and not simply ascribed to the effect of the environmental parameters. By applying neural networks, 10 radon anomalies were observed for 12 earthquakes, while with decision trees, the anomaly was found for every earthquake, but, undesirably, some anomalies appeared also during periods without earthquakes. (authors)
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The Matrix Element Method at Next-to-Leading Order
Campbell, John M.; Giele, Walter T.; Williams, Ciaran
2012-01-01
This paper presents an extension of the matrix element method to next-to-leading order in perturbation theory. To accomplish this we have developed a method to calculate next-to-leading order weights on an event-by-event basis. This allows for the definition of next-to-leading order likelihoods in exactly the same fashion as at leading order, thus extending the matrix element method to next-to-leading order. A welcome by-product of the method is the straightforward and efficient generation of...
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External events analysis for experimental fusion facilities
International Nuclear Information System (INIS)
Cadwallader, L.C.
1990-01-01
External events are those off-normal events that threaten facilities either from outside or inside the building. These events, such as floods, fires, and earthquakes, are among the leading risk contributors for fission power plants, and the nature of fusion facilities indicates that they may also lead fusion risk. This paper gives overviews of analysis methods, references good analysis guidance documents, and gives design tips for mitigating the effects of floods and fires, seismic events, and aircraft impacts. Implications for future fusion facility siting are also discussed. Sites similar to fission plant sites are recommended. 46 refs
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The amazing world of smashed protons and lead ions
Antonella Del Rosso
2013-01-01
When a single proton (p) is smashed against a lead ion (Pb), unexpected events may occur: in the most violent p-Pb collisions, correlations of particles exhibit similar features as in lead-lead collisions where quark-gluon plasma is formed. This and other amazing results were presented by the ALICE experiment at the SQM2013 conference held in Birmingham from 21 to 27 July. Event display from the proton-lead run, in January 2013. This event was generated by the High Level Trigger (HLT) of the ALICE experiment. “Jet quenching” is one of the most powerful signatures of quark-gluon plasma (QGP) formed in high-energy lead-lead collisions. QGP is expected to exist only in specific conditions involving extremely hot temperatures and a very high particle concentration. These conditions are not expected to apply in the case of less “dense” particle collisions such as proton-lead collisions. “When we observe the results of these collisions in ALICE, ...
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Understanding the Implications of Neural Population Activity on Behavior
Briguglio, John
Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests
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Neural bases of ingroup altruistic motivation in soccer fans.
Bortolini, Tiago; Bado, Patrícia; Hoefle, Sebastian; Engel, Annerose; Zahn, Roland; de Oliveira Souza, Ricardo; Dreher, Jean-Claude; Moll, Jorge
2017-11-23
Humans have a strong need to belong to social groups and a natural inclination to benefit ingroup members. Although the psychological mechanisms behind human prosociality have extensively been studied, the specific neural systems bridging group belongingness and altruistic motivation remain to be identified. Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance. We designed an effort measure based on handgrip strength to assess the motivation to earn money (i) for oneself, (ii) for anonymous ingroup fans, or (iii) for a neutral group of anonymous non-fans. While overlapping valuation signals in the medial orbitofrontal cortex (mOFC) were observed for the three conditions, the subgenual cingulate cortex (SCC) exhibited increased functional connectivity with the mOFC as well as stronger hemodynamic responses for ingroup versus outgroup decisions. These findings indicate a key role for the SCC, a region previously implicated in altruistic decisions and group affiliation, in dovetailing altruistic motivations with neural valuation systems in real-life ingroup behaviour.
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An application of neural networks to process and materials control
International Nuclear Information System (INIS)
Howell, J.A.; Whiteson, R.
1991-01-01
Process control consists of two basic elements: a model of the process and knowledge of the desired control algorithm. In some cases the level of the control algorithm is merely supervisory, as in an alarm-reporting or anomaly-detection system. If the model of the process is known, then a set of equations may often be solved explicitly to provide the control algorithm. Otherwise, the model has to be discovered through empirical studies. Neural networks have properties that make them useful in this application. They can learn (make internal models from experience or observations). The problem of anomaly detection in materials control systems fits well into this general control framework. To successfully model a process with a neutral network, a good set of observables must be chosen. These observables must in some sense adequately span the space of representable events, so that a signature metric can be built for normal operation. In this way, a non-normal event, one that does not fit within the signature, can be detected. In this paper, we discuss the issues involved in applying a neural network model to anomaly detection in materials control systems. These issues include data selection and representation, network architecture, prediction of events, the use of simulated data, and software tools. 10 refs., 4 figs., 1 tab
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Moroz, Leonid L.
2015-01-01
The origins of neural systems and centralized brains are one of the major transitions in evolution. These events might occur more than once over 570–600 million years. The convergent evolution of neural circuits is evident from a diversity of unique adaptive strategies implemented by ctenophores, cnidarians, acoels, molluscs, and basal deuterostomes. But, further integration of biodiversity research and neuroscience is required to decipher critical events leading to development of complex integrative and cognitive functions. Here, we outline reference species and interdisciplinary approaches in reconstructing the evolution of nervous systems. In the “omic” era, it is now possible to establish fully functional genomics laboratories aboard of oceanic ships and perform sequencing and real-time analyses of data at any oceanic location (named here as Ship-Seq). In doing so, fragile, rare, cryptic, and planktonic organisms, or even entire marine ecosystems, are becoming accessible directly to experimental and physiological analyses by modern analytical tools. Thus, we are now in a position to take full advantages from countless “experiments” Nature performed for us in the course of 3.5 billion years of biological evolution. Together with progress in computational and comparative genomics, evolutionary neuroscience, proteomic and developmental biology, a new surprising picture is emerging that reveals many ways of how nervous systems evolved. As a result, this symposium provides a unique opportunity to revisit old questions about the origins of biological complexity. PMID:26163680
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Investigating the Neural Correlates of Emotion–Cognition Interaction Using an Affective Stroop Task
Directory of Open Access Journals (Sweden)
Nora M. Raschle
2017-09-01
Full Text Available The human brain has the capacity to integrate various sources of information and continuously adapts our behavior according to situational needs in order to allow a healthy functioning. Emotion–cognition interactions are a key example for such integrative processing. However, the neuronal correlates investigating the effects of emotion on cognition remain to be explored and replication studies are needed. Previous neuroimaging studies have indicated an involvement of emotion and cognition related brain structures including parietal and prefrontal cortices and limbic brain regions. Here, we employed whole brain event-related functional magnetic resonance imaging (fMRI during an affective number Stroop task and aimed at replicating previous findings using an adaptation of an existing task design in 30 healthy young adults. The Stroop task is an indicator of cognitive control and enables the quantification of interference in relation to variations in cognitive load. By the use of emotional primes (negative/neutral prior to Stroop task performance, an emotional variation is added as well. Behavioral in-scanner data showed that negative primes delayed and disrupted cognitive processing. Trials with high cognitive demand furthermore negatively influenced cognitive control mechanisms. Neuronally, the emotional primes consistently activated emotion-related brain regions (e.g., amygdala, insula, and prefrontal brain regions while Stroop task performance lead to activations in cognition networks of the brain (prefrontal cortices, superior temporal lobe, and insula. When assessing the effect of emotion on cognition, increased cognitive demand led to decreases in neural activation in response to emotional stimuli (negative > neutral within prefrontal cortex, amygdala, and insular cortex. Overall, these results suggest that emotional primes significantly impact cognitive performance and increasing cognitive demand leads to reduced neuronal activation in
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Energy Technology Data Exchange (ETDEWEB)
Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)
1996-12-31
The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.
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DEFF Research Database (Denmark)
Aad...[], G.; Dam, Mogens; Hansen, John Renner
2010-01-01
By using the ATLAS detector, observations have been made of a centrality-dependent dijet asymmetry in the collisions of lead ions at the Large Hadron Collider. In a sample of lead-lead events with a per-nucleon center of mass energy of 2.76 TeV, selected with a minimum bias trigger, jets...... are reconstructed in fine-grained, longitudinally segmented electromagnetic and hadronic calorimeters. The transverse energies of dijets in opposite hemispheres are observed to become systematically more unbalanced with increasing event centrality leading to a large number of events which contain highly asymmetric...
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PROFESSIONAL AND NONPROFESSIONAL EVENT MANAGERS: AGENTS’ CHARACTERISTICS OF EVENT-ACTIVITIES FIELD
Directory of Open Access Journals (Sweden)
Natalia Nikolaevna Startseva
2015-03-01
Full Text Available The main research question focuses on studying of the «new», non-traditional, recent forming professional group of event managers in modernRussia. This article provides the event group boundaries, its numerical and structural composition. On the basis of existing community event stereotypes, perceptions of event managers, supervisors’ event agencies and customers such the all agents included in the event activities, the author designed the image of professional and nonprofessional manager.Scientific, theoretical and practical significance of the work is leading to characterize agent-field-event activities and outline the prospects for the professional groups’ formation of event managers in modernRussia.Conceptuality and validity of the study is based on using theoretical and methodological comparative, functional and activity approaches.The obtained results can be used for further investigations in the event managers’ field and for other professional-groups, as well as useful in the study plan such as «Sociology of professions and professional groups» and «Sociology of culture and spiritual life».
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Neural responses to exclusion predict susceptibility to social influence.
Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G
2014-05-01
Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.
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A neural network device for on-line particle identification in cosmic ray experiments
International Nuclear Information System (INIS)
Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.
2004-01-01
On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification
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Kujawa, Autumn; Proudfit, Greg H.; Laptook, Rebecca; Klein, Daniel N.
2014-01-01
Children of parents with depression exhibit neural abnormalities in reward processing. Examining contributions of parenting could provide insight into the development of these abnormalities and to the etiology of depression. We evaluated whether early parenting moderates the effects of parental depression on a neural measure of reward and loss processing in mid-late childhood. Parenting was assessed when children were preschoolers. At age nine, children completed an event-related potential as...
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Real-time classification of signals from three-component seismic sensors using neural nets
Bowman, B. C.; Dowla, F.
1992-05-01
Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.
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Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen
2013-02-01
This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.
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International Nuclear Information System (INIS)
Smith, Patrick I.
2003-01-01
Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing
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Directory of Open Access Journals (Sweden)
Tomer Halevy
2016-10-01
Full Text Available Down syndrome (DS is the leading genetic cause of mental retardation and is caused by a third copy of human chromosome 21. The different pathologies of DS involve many tissues with a distinct array of neural phenotypes. Here we characterize embryonic stem cell lines with DS (DS-ESCs, and focus on the neural aspects of the disease. Our results show that neural progenitor cells (NPCs differentiated from five independent DS-ESC lines display increased apoptosis and downregulation of forehead developmental genes. Analysis of differentially expressed genes suggested RUNX1 as a key transcription regulator in DS-NPCs. Using genome editing we were able to disrupt all three copies of RUNX1 in DS-ESCs, leading to downregulation of several RUNX1 target developmental genes accompanied by reduced apoptosis and neuron migration. Our work sheds light on the role of RUNX1 and the importance of dosage balance in the development of neural phenotypes in DS.
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Directory of Open Access Journals (Sweden)
Natália Kocsel
2017-05-01
Full Text Available Cumulative evidence suggests that trait rumination can be defined as an abstract information processing mode, which leads people to constantly anticipate the likely impact of present events on future events and experiences. A previous study with remitted depressed patients suggested that enhanced rumination tendencies distort brain mechanisms of anticipatory processes associated with reward and loss cues. In the present study, we explored the impact of trait rumination on neural activity during reward and loss anticipation among never-depressed people. We analyzed the data of 37 healthy controls, who performed the monetary incentive delay (MID task which was designed for the simultaneous measurement of the anticipation (motivational and consumption (hedonic phase of reward processing, during functional magnetic resonance imaging (fMRI. Our results show that rumination—after controlling for age, gender, and current mood—significantly influenced neural responses to reward (win cues compared to loss cues. Blood-oxygenation-level-dependent (BOLD activity in the left inferior frontal gyrus (IFG triangularis, left anterior insula, and left rolandic operculum was positively related to Ruminative Response Scale (RRS scores. We did not detect any significant rumination-related activations associated with win-neutral or loss-neutral cues and with reward or loss consumption. Our results highlight the influence of trait rumination on reward anticipation in a non-depressed sample. They also suggest that for never-depressed ruminators rewarding cues are more salient than loss cues. BOLD response during reward consumption did not relate to rumination, suggesting that rumination mainly relates to processing of the motivational (wanting aspect of reward rather than the hedonic (liking aspect, at least in the absence of pathological mood.
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Regulation of Msx genes by a Bmp gradient is essential for neural crest specification.
Tribulo, Celeste; Aybar, Manuel J; Nguyen, Vu H; Mullins, Mary C; Mayor, Roberto
2003-12-01
There is evidence in Xenopus and zebrafish embryos that the neural crest/neural folds are specified at the border of the neural plate by a precise threshold concentration of a Bmp gradient. In order to understand the molecular mechanism by which a gradient of Bmp is able to specify the neural crest, we analyzed how the expression of Bmp targets, the Msx genes, is regulated and the role that Msx genes has in neural crest specification. As Msx genes are directly downstream of Bmp, we analyzed Msx gene expression after experimental modification in the level of Bmp activity by grafting a bead soaked with noggin into Xenopus embryos, by expressing in the ectoderm a dominant-negative Bmp4 or Bmp receptor in Xenopus and zebrafish embryos, and also through Bmp pathway component mutants in the zebrafish. All the results show that a reduction in the level of Bmp activity leads to an increase in the expression of Msx genes in the neural plate border. Interestingly, by reaching different levels of Bmp activity in animal cap ectoderm, we show that a specific concentration of Bmp induces msx1 expression to a level similar to that required to induce neural crest. Our results indicate that an intermediate level of Bmp activity specifies the expression of Msx genes in the neural fold region. In addition, we have analyzed the role that msx1 plays on neural crest specification. As msx1 has a role in dorsoventral pattering, we have carried out conditional gain- and loss-of-function experiments using different msx1 constructs fused to a glucocorticoid receptor element to avoid an early effect of this factor. We show that msx1 expression is able to induce all other early neural crest markers tested (snail, slug, foxd3) at the time of neural crest specification. Furthermore, the expression of a dominant negative of Msx genes leads to the inhibition of all the neural crest markers analyzed. It has been previously shown that snail is one of the earliest genes acting in the neural crest
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Stochastic modeling for neural spiking events based on fractional superstatistical Poisson process
Konno, Hidetoshi; Tamura, Yoshiyasu
2018-01-01
In neural spike counting experiments, it is known that there are two main features: (i) the counting number has a fractional power-law growth with time and (ii) the waiting time (i.e., the inter-spike-interval) distribution has a heavy tail. The method of superstatistical Poisson processes (SSPPs) is examined whether these main features are properly modeled. Although various mixed/compound Poisson processes are generated with selecting a suitable distribution of the birth-rate of spiking neurons, only the second feature (ii) can be modeled by the method of SSPPs. Namely, the first one (i) associated with the effect of long-memory cannot be modeled properly. Then, it is shown that the two main features can be modeled successfully by a class of fractional SSPP (FSSPP).
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Neural Based Orthogonal Data Fitting The EXIN Neural Networks
Cirrincione, Giansalvo
2008-01-01
Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh
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Ge Detector Data Classification with Neural Networks
Wilson, Carly; Martin, Ryan; Majorana Collaboration
2014-09-01
The Majorana Demonstrator experiment is searching for neutrinoless double beta-decay using p-type point contact PPC germanium detectors at the Sanford Underground Research Facility, in South Dakota. Pulse shape discrimination can be used in PPC detectors to distinguish signal-like events from backgrounds. This research program explored the possibility of building a self-organizing map that takes data collected from germanium detectors and classifies the events as either signal or background. Self organizing maps are a type of neural network that are self-learning and less susceptible to being biased from imperfect training data. We acknowledge support from the Office of Nuclear Physics in the DOE Office of Science, the Particle and Nuclear Astrophysics Program of the National Science Foundation and the Russian Foundation for Basic Research.
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Neural ECM in laminar organization and connectivity development in healthy and diseased human brain
Jovanov Milošević, Nataša; Judaš, Miloš; Aronica, Eleonora; Kostovic, Ivica
2014-01-01
The neural extracellular matrix (ECM) provides a supportive framework for differentiating cells and their processes and regulates morphogenetic events by spatially and temporally relevant localization of signaling molecules and by direct signaling via receptor and/or coreceptor-mediated action. The
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Event-by-event jet quenching and higher Fourier moments of hard probes
Energy Technology Data Exchange (ETDEWEB)
Fries, Rainer J. [Cyclotron Institute and Department of Physics and Astronomy, Texas A and M University, College Station TX 77845 (United States); RIKEN/BNL Research Center, Brookhaven National Laboratory, Upton NY 11973 (United States); Rodriguez, Ricardo [Department of Mathematics and Physics, Ave Maria University, Ave Maria FL 34142 (United States); Cyclotron Institute, Texas A and M University, College Station TX 77845 (United States)
2011-04-01
We investigate the effect of event-by-event fluctuations of the fireball created in high energy nuclear collisions on hard probe observables. We show that spatial inhomogeneities lead to changes in the nuclear suppression factor of high momentum hadrons which can be absorbed in the quenching strength q-hat. This can increase the theoretical uncertainty on extracted values of q-hat by up to 50%. We also investigate effects on azimuthal asymmetries v{sub 2} and dihadron correlation functions. The latter show a promising residual signal of event-by-event quenching that might allow us to estimate the size of spatial inhomogeneities in the fireball from experimental data.
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Ultra-low noise miniaturized neural amplifier with hardware averaging.
Dweiri, Yazan M; Eggers, Thomas; McCallum, Grant; Durand, Dominique M
2015-08-01
Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments. We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.
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Barratt Impulsivity and Neural Regulation of Physiological Arousal.
Directory of Open Access Journals (Sweden)
Sheng Zhang
Full Text Available Theories of personality have posited an increased arousal response to external stimulation in impulsive individuals. However, there is a dearth of studies addressing the neural basis of this association.We recorded skin conductance in 26 individuals who were assessed with Barratt Impulsivity Scale (BIS-11 and performed a stop signal task during functional magnetic resonance imaging. Imaging data were processed and modeled with Statistical Parametric Mapping. We used linear regressions to examine correlations between impulsivity and skin conductance response (SCR to salient events, identify the neural substrates of arousal regulation, and examine the relationship between the regulatory mechanism and impulsivity.Across subjects, higher impulsivity is associated with greater SCR to stop trials. Activity of the ventromedial prefrontal cortex (vmPFC negatively correlated to and Granger caused skin conductance time course. Furthermore, higher impulsivity is associated with a lesser strength of Granger causality of vmPFC activity on skin conductance, consistent with diminished control of physiological arousal to external stimulation. When men (n = 14 and women (n = 12 were examined separately, however, there was evidence suggesting association between impulsivity and vmPFC regulation of arousal only in women.Together, these findings confirmed the link between Barratt impulsivity and heightened arousal to salient stimuli in both genders and suggested the neural bases of altered regulation of arousal in impulsive women. More research is needed to explore the neural processes of arousal regulation in impulsive individuals and in clinical conditions that implicate poor impulse control.
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Barratt Impulsivity and Neural Regulation of Physiological Arousal.
Zhang, Sheng; Hu, Sien; Hu, Jianping; Wu, Po-Lun; Chao, Herta H; Li, Chiang-shan R
2015-01-01
Theories of personality have posited an increased arousal response to external stimulation in impulsive individuals. However, there is a dearth of studies addressing the neural basis of this association. We recorded skin conductance in 26 individuals who were assessed with Barratt Impulsivity Scale (BIS-11) and performed a stop signal task during functional magnetic resonance imaging. Imaging data were processed and modeled with Statistical Parametric Mapping. We used linear regressions to examine correlations between impulsivity and skin conductance response (SCR) to salient events, identify the neural substrates of arousal regulation, and examine the relationship between the regulatory mechanism and impulsivity. Across subjects, higher impulsivity is associated with greater SCR to stop trials. Activity of the ventromedial prefrontal cortex (vmPFC) negatively correlated to and Granger caused skin conductance time course. Furthermore, higher impulsivity is associated with a lesser strength of Granger causality of vmPFC activity on skin conductance, consistent with diminished control of physiological arousal to external stimulation. When men (n = 14) and women (n = 12) were examined separately, however, there was evidence suggesting association between impulsivity and vmPFC regulation of arousal only in women. Together, these findings confirmed the link between Barratt impulsivity and heightened arousal to salient stimuli in both genders and suggested the neural bases of altered regulation of arousal in impulsive women. More research is needed to explore the neural processes of arousal regulation in impulsive individuals and in clinical conditions that implicate poor impulse control.
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Invariant recognition drives neural representations of action sequences.
Directory of Open Access Journals (Sweden)
Andrea Tacchetti
2017-12-01
Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.
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The neural basis of responsibility attribution in decision-making.
Directory of Open Access Journals (Sweden)
Peng Li
Full Text Available Social responsibility links personal behavior with societal expectations and plays a key role in affecting an agent's emotional state following a decision. However, the neural basis of responsibility attribution remains unclear. In two previous event-related brain potential (ERP studies we found that personal responsibility modulated outcome evaluation in gambling tasks. Here we conducted a functional magnetic resonance imaging (fMRI study to identify particular brain regions that mediate responsibility attribution. In a context involving team cooperation, participants completed a task with their teammates and on each trial received feedback about team success and individual success sequentially. We found that brain activity differed between conditions involving team success vs. team failure. Further, different brain regions were associated with reinforcement of behavior by social praise vs. monetary reward. Specifically, right temporoparietal junction (RTPJ was associated with social pride whereas dorsal striatum and dorsal anterior cingulate cortex (ACC were related to reinforcement of behaviors leading to personal gain. The present study provides evidence that the RTPJ is an important region for determining whether self-generated behaviors are deserving of praise in a social context.
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Track finding with neural networks in ALICE ITS
International Nuclear Information System (INIS)
Batyunya, B.V.; Belikov, Yu.A.; Fedunov, A.G.; Zinchenko, A.I.
1996-01-01
A program based on Neural Networks technique was developed for track recognition in ALICE ITS. The efficiency of this program was estimated on Monte Carlo simulated events for particles with p T ≤ 100 MeV/c. The advantages of this program are the high performance, the capability of track finding in conditions of the high particle multiplicity and the ability to find tracks distorted by the multiple Coulomb scattering and the energy losses. 7 refs., 1 tab
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Neural activity when people solve verbal problems with insight.
Directory of Open Access Journals (Sweden)
Mark Jung-Beeman
2004-04-01
Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.
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Play It Again: Neural Responses to Reunion with Excluders Predicted by Attachment Patterns
White, Lars O.; Wu, Jia; Borelli, Jessica L.; Mayes, Linda C.; Crowley, Michael J.
2013-01-01
Reunion behavior following stressful separations from caregivers is often considered the single most sensitive clue to infant attachment patterns. Extending these ideas to middle childhood/early adolescence, we examined participants' neural responses to reunion with peers who had previously excluded them. We recorded event-related potentials…
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Field-theoretic approach to fluctuation effects in neural networks
International Nuclear Information System (INIS)
Buice, Michael A.; Cowan, Jack D.
2007-01-01
A well-defined stochastic theory for neural activity, which permits the calculation of arbitrary statistical moments and equations governing them, is a potentially valuable tool for theoretical neuroscience. We produce such a theory by analyzing the dynamics of neural activity using field theoretic methods for nonequilibrium statistical processes. Assuming that neural network activity is Markovian, we construct the effective spike model, which describes both neural fluctuations and response. This analysis leads to a systematic expansion of corrections to mean field theory, which for the effective spike model is a simple version of the Wilson-Cowan equation. We argue that neural activity governed by this model exhibits a dynamical phase transition which is in the universality class of directed percolation. More general models (which may incorporate refractoriness) can exhibit other universality classes, such as dynamic isotropic percolation. Because of the extremely high connectivity in typical networks, it is expected that higher-order terms in the systematic expansion are small for experimentally accessible measurements, and thus, consistent with measurements in neocortical slice preparations, we expect mean field exponents for the transition. We provide a quantitative criterion for the relative magnitude of each term in the systematic expansion, analogous to the Ginsburg criterion. Experimental identification of dynamic universality classes in vivo is an outstanding and important question for neuroscience
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Information content of neural networks with self-control and variable activity
International Nuclear Information System (INIS)
Bolle, D.; Amari, S.I.; Dominguez Carreta, D.R.C.; Massolo, G.
2001-01-01
A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations
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Computational modeling of neural activities for statistical inference
Kolossa, Antonio
2016-01-01
This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .
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Generating Seismograms with Deep Neural Networks
Krischer, L.; Fichtner, A.
2017-12-01
The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of
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Neural Network-Based Model for Landslide Susceptibility and Soil Longitudinal Profile Analyses
DEFF Research Database (Denmark)
Farrokhzad, F.; Barari, Amin; Choobbasti, A. J.
2011-01-01
The purpose of this study was to create an empirical model for assessing the landslide risk potential at Savadkouh Azad University, which is located in the rural surroundings of Savadkouh, about 5 km from the city of Pol-Sefid in northern Iran. The soil longitudinal profile of the city of Babol......, located 25 km from the Caspian Sea, also was predicted with an artificial neural network (ANN). A multilayer perceptron neural network model was applied to the landslide area and was used to analyze specific elements in the study area that contributed to previous landsliding events. The ANN models were...... studies in landslide susceptibility zonation....
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QSAR modelling using combined simple competitive learning networks and RBF neural networks.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
2018-04-01
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
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2010-12-17
By using the ATLAS detector, observations have been made of a centrality-dependent dijet asymmetry in the collisions of lead ions at the Large Hadron Collider. In a sample of lead-lead events with a per-nucleon center of mass energy of 2.76 TeV, selected with a minimum bias trigger, jets are reconstructed in fine-grained, longitudinally segmented electromagnetic and hadronic calorimeters. The transverse energies of dijets in opposite hemispheres are observed to become systematically more unbalanced with increasing event centrality leading to a large number of events which contain highly asymmetric dijets. This is the first observation of an enhancement of events with such large dijet asymmetries, not observed in proton-proton collisions, which may point to an interpretation in terms of strong jet energy loss in a hot, dense medium.
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SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.
Jimenez-Romero, Cristian; Johnson, Jeffrey
2017-01-01
The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.
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Vascular pattern of the dentate gyrus is regulated by neural progenitors.
Pombero, Ana; Garcia-Lopez, Raquel; Estirado, Alicia; Martinez, Salvador
2018-05-01
Neurogenesis is a vital process that begins during early embryonic development and continues until adulthood, though in the latter case, it is restricted to the subventricular zone and the subgranular zone of the dentate gyrus (DG). In particular, the DG's neurogenic properties are structurally and functionally unique, which may be related to its singular vascular pattern. Neurogenesis and angiogenesis share molecular signals and act synergistically, supporting the concept of a neurogenic niche as a functional unit between neural precursors cells and their environment, in which the blood vessels play an important role. Whereas it is well known that vascular development controls neural proliferation in the embryonary and in the adult brain, by releasing neurotrophic factors; the potential influence of neural cells on vascular components during angiogenesis is largely unknown. We have demonstrated that the reduction of neural progenitors leads to a significant impairment of vascular development. Since VEGF is a potential regulator in the neurogenesis-angiogenesis crosstalk, we were interested in assessing the possible role of this molecule in the hippocampal neurovascular development. Our results showed that VEGF is the molecule involved in the regulation of vascular development by neural progenitor cells in the DG.
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The role of stochasticity in an information-optimal neural population code
International Nuclear Information System (INIS)
Stocks, N G; Nikitin, A P; McDonnell, M D; Morse, R P
2009-01-01
In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.
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International Nuclear Information System (INIS)
Denby, Bruce; Lindsey, Clark; Lyons, Louis
1992-01-01
The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive
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Moroz, Leonid L
2015-12-01
The origins of neural systems and centralized brains are one of the major transitions in evolution. These events might occur more than once over 570-600 million years. The convergent evolution of neural circuits is evident from a diversity of unique adaptive strategies implemented by ctenophores, cnidarians, acoels, molluscs, and basal deuterostomes. But, further integration of biodiversity research and neuroscience is required to decipher critical events leading to development of complex integrative and cognitive functions. Here, we outline reference species and interdisciplinary approaches in reconstructing the evolution of nervous systems. In the "omic" era, it is now possible to establish fully functional genomics laboratories aboard of oceanic ships and perform sequencing and real-time analyses of data at any oceanic location (named here as Ship-Seq). In doing so, fragile, rare, cryptic, and planktonic organisms, or even entire marine ecosystems, are becoming accessible directly to experimental and physiological analyses by modern analytical tools. Thus, we are now in a position to take full advantages from countless "experiments" Nature performed for us in the course of 3.5 billion years of biological evolution. Together with progress in computational and comparative genomics, evolutionary neuroscience, proteomic and developmental biology, a new surprising picture is emerging that reveals many ways of how nervous systems evolved. As a result, this symposium provides a unique opportunity to revisit old questions about the origins of biological complexity. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.
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A Neural Assembly-Based View on Word Production: The Bilingual Test Case
Strijkers, Kristof
2016-01-01
I will propose a tentative framework of how words in two languages could be organized in the cerebral cortex based on neural assembly theory, according to which neurons that fire synchronously are bound into large-scale distributed functional units (assemblies), which represent a mental event as a whole ("gestalt"). For language this…
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Chen, Xi; Shen, Wei-Bin; Yang, Penghua; Dong, Daoyin; Sun, Winny; Yang, Peixin
2018-06-01
Maternal diabetes induces neural tube defects by suppressing neurogenesis in the developing neuroepithelium. Our recent study further revealed that high glucose inhibited embryonic stem cell differentiation into neural lineage cells. However, the mechanism whereby high glucose suppresses neural differentiation is unclear. To investigate whether high glucose-induced oxidative stress and endoplasmic reticulum (ER) stress lead to the inhibition of neural differentiation, the effect of high glucose on neural stem cell (the C17.2 cell line) differentiation was examined. Neural stem cells were cultured in normal glucose (5 mM) or high glucose (25 mM) differentiation medium for 3, 5, and 7 days. High glucose suppressed neural stem cell differentiation by significantly decreasing the expression of the neuron marker Tuj1 and the glial cell marker GFAP and the numbers of Tuj1 + and GFAP + cells. The antioxidant enzyme superoxide dismutase mimetic Tempol reversed high glucose-decreased Tuj1 and GFAP expression and restored the numbers of neurons and glial cells differentiated from neural stem cells. Hydrogen peroxide treatment imitated the inhibitory effect of high glucose on neural stem cell differentiation. Both high glucose and hydrogen peroxide triggered ER stress, whereas Tempol blocked high glucose-induced ER stress. The ER stress inhibitor, 4-phenylbutyrate, abolished the inhibition of high glucose or hydrogen peroxide on neural stem cell differentiation. Thus, oxidative stress and its resultant ER stress mediate the inhibitory effect of high glucose on neural stem cell differentiation.
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Stochastic modeling for neural spiking events based on fractional superstatistical Poisson process
Directory of Open Access Journals (Sweden)
Hidetoshi Konno
2018-01-01
Full Text Available In neural spike counting experiments, it is known that there are two main features: (i the counting number has a fractional power-law growth with time and (ii the waiting time (i.e., the inter-spike-interval distribution has a heavy tail. The method of superstatistical Poisson processes (SSPPs is examined whether these main features are properly modeled. Although various mixed/compound Poisson processes are generated with selecting a suitable distribution of the birth-rate of spiking neurons, only the second feature (ii can be modeled by the method of SSPPs. Namely, the first one (i associated with the effect of long-memory cannot be modeled properly. Then, it is shown that the two main features can be modeled successfully by a class of fractional SSPP (FSSPP.
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Neural processing of emotional-intensity predicts emotion regulation choice.
Shafir, Roni; Thiruchselvam, Ravi; Suri, Gaurav; Gross, James J; Sheppes, Gal
2016-12-01
Emotional-intensity is a core characteristic of affective events that strongly determines how individuals choose to regulate their emotions. Our conceptual framework suggests that in high emotional-intensity situations, individuals prefer to disengage attention using distraction, which can more effectively block highly potent emotional information, as compared with engagement reappraisal, which is preferred in low emotional-intensity. However, existing supporting evidence remains indirect because prior intensity categorization of emotional stimuli was based on subjective measures that are potentially biased and only represent the endpoint of emotional-intensity processing. Accordingly, this study provides the first direct evidence for the role of online emotional-intensity processing in predicting behavioral regulatory-choices. Utilizing the high temporal resolution of event-related potentials, we evaluated online neural processing of stimuli's emotional-intensity (late positive potential, LPP) prior to regulatory-choices between distraction and reappraisal. Results showed that enhanced neural processing of intensity (enhanced LPP amplitudes) uniquely predicted (above subjective measures of intensity) increased tendency to subsequently choose distraction over reappraisal. Additionally, regulatory-choices led to adaptive consequences, demonstrated in finding that actual implementation of distraction relative to reappraisal-choice resulted in stronger attenuation of LPPs and self-reported arousal. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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Noise-enhanced categorization in a recurrently reconnected neural network
International Nuclear Information System (INIS)
Monterola, Christopher; Zapotocky, Martin
2005-01-01
We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails
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Noise-enhanced categorization in a recurrently reconnected neural network
Monterola, Christopher; Zapotocky, Martin
2005-03-01
We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.
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Periconceptional Folate Deficiency and Implications in Neural Tube Defects
Directory of Open Access Journals (Sweden)
J. Safi
2012-01-01
Full Text Available Nutritional deficiencies are preventable etiological and epigenetic factors causing congenital abnormalities, first cause of infant mortality. Folate deficiency has a well-established teratogenic effect, leading to an increasing risk of neural tube defects. This paper highlights the most recent medical literature about folate deficiency, be it maternal or paternal. It then focuses on associated deficiencies as nutritional deficiencies are multiple and interrelated. Observational and interventional studies have all been consistent with a 50–70% protective effect of adequate women consumption of folates on neural tube defects. Since strategies to modify women’s dietary habits and vitamin use have achieved little progress, scientific as well as political effort is mandatory in order to implement global preventive public health strategies aimed at improving the alimentation of women in reproductive age, especially folic acid supplementation. Even with the recent breakthrough of fetal surgery for myelomeningocele, the emphasis should still be on prevention as the best practice rather than treatment of neural tube defects.
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Next-to-next-leading order correction to 3-jet rate and event-shape ...
Indian Academy of Sciences (India)
portunity to test QCD by measuring the energy dependence of different ... event shape data was not satisfactory largely due to the scale uncertainty of the pertur- .... )3 d ¯C dy. + O. ( α4 s. ) . (5). Here the event-shape distribution is normalized to the ..... [1] A Gehrmann-De Ridder, T Gehrmann, E W N Glover and G Heinrich, J.
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Maternal life event stress and congenital anomalies.
Carmichael, S L; Shaw, G M
2000-01-01
We used data from a population-based case-control study to explore the relation between certain life events during the periconceptional period and several types of congenital anomalies. We ascertained cases from pregnancies ending in 1987-1989 and randomly selected controls from eligible liveborn infants. In telephone interviews, women reported deaths of anyone close to them. They also reported job losses or separations/divorces, for themselves or anyone close to them. Experiencing at least one stressful event during the periconceptional period was associated with a prevalence odds ratio of 1.4-1.5 for the delivery of infants with conotruncal heart defects, neural tube defects, and isolated cleft lip with or without palate. These associations tended to be restricted to women who were not obese and women with less than or equal to a high school education. This study suggests that women who experience stressful life events around the time of conception or early gestation may be at increased risk of delivering infants with certain congenital anomalies.
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Park, Dong-Wook; Ness, Jared P; Brodnick, Sarah K; Esquibel, Corinne; Novello, Joseph; Atry, Farid; Baek, Dong-Hyun; Kim, Hyungsoo; Bong, Jihye; Swanson, Kyle I; Suminski, Aaron J; Otto, Kevin J; Pashaie, Ramin; Williams, Justin C; Ma, Zhenqiang
2018-01-23
Electrical stimulation using implantable electrodes is widely used to treat various neuronal disorders such as Parkinson's disease and epilepsy and is a widely used research tool in neuroscience studies. However, to date, devices that help better understand the mechanisms of electrical stimulation in neural tissues have been limited to opaque neural electrodes. Imaging spatiotemporal neural responses to electrical stimulation with minimal artifact could allow for various studies that are impossible with existing opaque electrodes. Here, we demonstrate electrical brain stimulation and simultaneous optical monitoring of the underlying neural tissues using carbon-based, fully transparent graphene electrodes implanted in GCaMP6f mice. Fluorescence imaging of neural activity for varying electrical stimulation parameters was conducted with minimal image artifact through transparent graphene electrodes. In addition, full-field imaging of electrical stimulation verified more efficient neural activation with cathode leading stimulation compared to anode leading stimulation. We have characterized the charge density limitation of capacitive four-layer graphene electrodes as 116.07-174.10 μC/cm 2 based on electrochemical impedance spectroscopy, cyclic voltammetry, failure bench testing, and in vivo testing. This study demonstrates the transparent ability of graphene neural electrodes and provides a method to further increase understanding and potentially improve therapeutic electrical stimulation in the central and peripheral nervous systems.
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Krejčí, Jana; Legartová, Soňa; Bártová, Eva
2017-01-01
Cajal bodies (CBs) are important compartments containing accumulated proteins that preferentially regulate RNA-related nuclear events, including splicing. Here, we studied the nuclear distribution pattern of CBs in neurogenesis. In adult brains, coilin was present at a high density, but CB formation was absent in the nuclei of the choroid plexus of the lateral ventricles. Cells of the adult hippocampus were characterized by a crescent-like morphology of coilin protein. We additionally observed a 70 kDa splice variant of coilin in adult mouse brains, which was different to embryonic brains and mouse pluripotent embryonic stem cells (mESCs), characterized by the 80 kDa standard variant of coilin. Here, we also showed that depletion of coilin is induced during neural differentiation and HDAC1 deficiency in mESCs caused coilin accumulation inside the fibrillarin-positive region of the nucleoli. A similar distribution pattern was observed in adult brain hippocampi, characterized by lower levels of both coilin and HDAC1. In summary, we observed that neural differentiation and HDAC1 deficiency lead to coilin depletion and coilin accumulation in body-like structures inside the nucleoli.
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Expression of cardiac neural crest and heart genes isolated by modified differential display.
Martinsen, Brad J; Groebner, Nathan J; Frasier, Allison J; Lohr, Jamie L
2003-08-01
The invasion of the cardiac neural crest (CNC) into the outflow tract (OFT) and subsequent outflow tract septation are critical events during vertebrate heart development. We have performed four modified differential display screens in the chick embryo to identify genes that may be involved in CNC, OFT, secondary heart field, and heart development. The screens included differential display of RNA isolated from three different axial segments containing premigratory cranial neural crest cells; of RNA from distal outflow tract, proximal outflow tract, and atrioventricular tissue of embryonic chick hearts; and of RNA isolated from left and right cranial tissues, including the early heart fields. These screens have resulted in the identification of the five cDNA clones presented here, which are expressed in the cardiac neural crest, outflow tract and developing heart in patterns that are unique in heart development.
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Prestimulus subsequent memory effects for auditory and visual events.
Otten, Leun J; Quayle, Angela H; Puvaneswaran, Bhamini
2010-06-01
It has been assumed that the effective encoding of information into memory primarily depends on neural activity elicited when an event is initially encountered. Recently, it has been shown that memory formation also relies on neural activity just before an event. The precise role of such activity in memory is currently unknown. Here, we address whether prestimulus activity affects the encoding of auditory and visual events, is set up on a trial-by-trial basis, and varies as a function of the type of recognition judgment an item later receives. Electrical brain activity was recorded from the scalps of 24 healthy young adults while they made semantic judgments on randomly intermixed series of visual and auditory words. Each word was preceded by a cue signaling the modality of the upcoming word. Auditory words were preceded by auditory cues and visual words by visual cues. A recognition memory test with remember/know judgments followed after a delay of about 45 min. As observed previously, a negative-going, frontally distributed modulation just before visual word onset predicted later recollection of the word. Crucially, the same effect was found for auditory words and observed on stay as well as switch trials. These findings emphasize the flexibility and general role of prestimulus activity in memory formation, and support a functional interpretation of the activity in terms of semantic preparation. At least with an unpredictable trial sequence, the activity is set up anew on each trial.
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Phytochemicals for taming agitated immune-endocrine-neural axis.
Patel, Seema
2017-07-01
Homeostasis of immune-endocrine-neural axis is paramount for human health. If this axis gets agitated due to age, genetic variations, environmental exposures or lifestyle assaults, a cascade of adverse reactions occurs in human body. Cytokines, hormones and neurotransmitters, the effector molecules of this axis behave erratically, leading to a gamut of neural, endocrine, autoimmune, and metabolic diseases. Current panel of drugs can tackle some of them but not in a sustainable, benign way as a myriad of side effects, causal of them have been documented. In this context, phytochemicals, the secondary metabolites of plants seem beneficial. These bioactive constituents encompassing polyphenols, alkaloids, flavonoids, terpenoids, tannins, lignans, stilbenoids (resveratrol), saponins, polysaccharides, glycosides, and lectins etc. have been proven to exert antioxidant, anti-inflammatory, hypolipidemic, hypotensive, antidiabetic, anticancer, immunomodulatory, anti-allergic, analgesic, hepatoprotective, neuroprotective, dermatoprotective, and antimicrobial properties, among a litany of other biological effects. This review presents a holistic perspective of common afflictions resultant of immune-endocrine-neural axis disruption, and the phytochemicals capable of restoring their normalcy and mitigating the ailments. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
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Khajeh, M.; Pourkarami, A.; Arefnejad, E.; Bohlooli, M.; Khatibi, A.; Ghaffari-Moghaddam, M.; Zareian-Jahromi, S.
2017-09-01
Chitosan-zinc oxide nanoparticles (CZPs) were developed for solid-phase extraction. Combined artificial neural network-ant colony optimization (ANN-ACO) was used for the simultaneous preconcentration and determination of lead (Pb2+) ions in water samples prior to graphite furnace atomic absorption spectrometry (GF AAS). The solution pH, mass of adsorbent CZPs, amount of 1-(2-pyridylazo)-2-naphthol (PAN), which was used as a complexing agent, eluent volume, eluent concentration, and flow rates of sample and eluent were used as input parameters of the ANN model, and the percentage of extracted Pb2+ ions was used as the output variable of the model. A multilayer perception network with a back-propagation learning algorithm was used to fit the experimental data. The optimum conditions were obtained based on the ACO. Under the optimized conditions, the limit of detection for Pb2+ ions was found to be 0.078 μg/L. This procedure was also successfully used to determine the amounts of Pb2+ ions in various natural water samples.
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Smith, Alec; Lohrenz, Terry; King, Justin; Montague, P Read; Camerer, Colin F
2014-07-22
Groups of humans routinely misassign value to complex future events, especially in settings involving the exchange of resources. If properly structured, experimental markets can act as excellent probes of human group-level valuation mechanisms during pathological overvaluations--price bubbles. The connection between the behavioral and neural underpinnings of such phenomena has been absent, in part due to a lack of enabling technology. We used a multisubject functional MRI paradigm to measure neural activity in human subjects participating in experimental asset markets in which endogenous price bubbles formed and crashed. Although many ideas exist about how and why such bubbles may form and how to identify them, our experiment provided a window on the connection between neural responses and behavioral acts (buying and selling) that created the bubbles. We show that aggregate neural activity in the nucleus accumbens (NAcc) tracks the price bubble and that NAcc activity aggregated within a market predicts future price changes and crashes. Furthermore, the lowest-earning subjects express a stronger tendency to buy as a function of measured NAcc activity. Conversely, we report a signal in the anterior insular cortex in the highest earners that precedes the impending price peak, is associated with a higher propensity to sell in high earners, and that may represent a neural early warning signal in these subjects. Such markets could be a model system to understand neural and behavior mechanisms in other settings where emergent group-level activity exhibits mistaken belief or valuation.
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The Neural Cell Adhesion Molecule NCAM2/OCAM/RNCAM, a Close Relative to NCAM
DEFF Research Database (Denmark)
Kulahin, Nikolaj; Walmod, Peter
2008-01-01
molecule (NCAM) is a well characterized, ubiquitously expressed CAM that is highly expressed in the nervous system. In addition to mediating cell adhesion, NCAM participates in a multitude of cellular events, including survival, migration, and differentiation of cells, outgrowth of neurites, and formation......Cell adhesion molecules (CAMs) constitute a large class of plasma membrane-anchored proteins that mediate attachment between neighboring cells and between cells and the surrounding extracellular matrix (ECM). However, CAMs are more than simple mediators of cell adhesion. The neural cell adhesion...... and plasticity of synapses. NCAM shares an overall sequence identity of approximately 44% with the neural cell adhesion molecule 2 (NCAM2), a protein also known as olfactory cell adhesion molecule (OCAM) and Rb-8 neural cell adhesion molecule (RNCAM), and the region-for-region sequence homology between the two...
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Endogenous and Exogenous Attention Shifts are Mediated by the Same Large-Scale Neural Network.
Peelen, M.V.; Heslenfeld, D.J.; Theeuwes, J.
2004-01-01
Event-related fMRI was used to examine the neural basis of endogenous (top-down) and exogenous (bottom-up) spatial orienting. Shifts of attention were induced by central (endogenous) or peripheral (exogenous) cues. Reaction times on subsequently presented targets showed the expected pattern of
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Task-dependent neural bases of perceiving emotionally expressive targets
Directory of Open Access Journals (Sweden)
Jamil eZaki
2012-08-01
Full Text Available Social cognition is fundamentally interpersonal: individuals’ behavior and dispositions critically affect their interaction partners’ information processing. However, cognitive neuroscience studies, partially because of methodological constraints, have remained largely perceiver-centric: focusing on the abilities, motivations, and goals of social perceivers while largely ignoring interpersonal effects. Here, we address this knowledge gap by examining the neural bases of perceiving emotionally expressive and inexpressive social targets. Sixteen perceivers were scanned using fMRI while they watched targets discussing emotional autobiographical events. Perceivers continuously rated each target’s emotional state or eye-gaze direction. The effects of targets’ emotional expressivity on perceiver’s brain activity depended on task set: when perceivers explicitly attended to targets’ emotions, expressivity predicted activity in neural structures—including medial prefrontal and posterior cingulate cortex—associated with drawing inferences about mental states. When perceivers instead attended to targets’ eye-gaze, target expressivity predicted activity in regions—including somatosensory cortex, fusiform gyrus, and motor cortex—associated with monitoring sensorimotor states and biological motion. These findings suggest that expressive targets affect information processing in manner that depends on perceivers’ goals. More broadly, these data provide an early step towards understanding the neural bases of interpersonal social cognition.
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Directory of Open Access Journals (Sweden)
Steven F. Maier
2015-01-01
Full Text Available It has been known for many years that the ability to exert behavioral control over an adverse event blunts the behavioral and neurochemical impact of the event. More recently, it has become clear that the experience of behavioral control over adverse events also produces enduring changes that reduce the effects of subsequent negative events, even if they are uncontrollable and quite different from the original event controlled. This review focuses on the mechanism by which control both limits the impact of the stressor being experienced and produces enduring, trans-situational “immunization”. The evidence will suggest that control is detected by a corticostriatal circuit involving the ventral medial prefrontal cortex (mPFC and the posterior dorsomedial striatum (DMS. Once control is detected, other mPFC neurons that project to stress-responsive brainstem (dorsal raphe nucleus, DRN and limbic (amygdala structures exert top–down inhibitory control over the activation of these structures that is produced by the adverse event. These structures, such as the DRN and amygdala, in turn regulate the proximate mediators of the behavioral and physiological responses produced by adverse events, and so control blunts these responses. Importantly, the joint occurrence of control and adverse events seems to produce enduring plastic changes in the top–down inhibitory mPFC system such that this system is now activated by later adverse events even if they are uncontrollable, thereby reducing the impact of these events. Other issues are discussed that include a whether other processes such as safety signals and exercise, that lead to resistance/resilience, also use the mPFC circuitry or do so in other ways; b whether control has similar effects and neural mediation in humans, and c the relationship of this work to clinical phenomena.
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Directory of Open Access Journals (Sweden)
Dileep Kumar
2017-01-01
Full Text Available Traditionally permanent acoustic sensors leak detection techniques have been proven to be very effective in water distribution pipes. However, these methods need long distance deployment and proper position of sensors and cannot be implemented on underground pipelines. An inline-inspection acoustic device is developed which consists of acoustic sensors. The device will travel by the flow of water through the pipes which record all noise events and detect small leaks. However, it records all the noise events regarding background noises, but the time domain noisy acoustic signal cannot manifest complete features such as the leak flow rate which does not distinguish the leak signal and environmental disturbance. This paper presents an algorithm structure with the modularity of wavelet and neural network, which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks. This study validates that the time domain is not evident to the complete features regarding noisy leak signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes. The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise and background noise, and by neural network implementation, the method improves the classification performance of extracted features.
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The impact of perceived quality on online buying decisions: an event-related potentials perspective.
Wang, Jing; Han, Weiwei
2014-10-01
Consumer neuroscience can provide useful insights into the neural foundations of consumer decisions, such as perceived quality. One of the applications is to guide attribute configuration of products to fit consumers' expectations on the basis of individual preferences. In this study, we required 20 participants to decide whether to buy the product provided in the stimuli and to respond as soon as possible. According to their reports of expectations after the experiment, we subdivided the stimuli into two conditions. Condition 1 contained the stimuli that fit individual preferences, whereas Condition 2 contained the other stimuli. An essential component of event-related potentials (ERPs), the P300, was elicited in the two conditions and distributed over almost all parietal and occipital regions. Products in Condition 1 induced a higher P300 amplitude than those in Condition 2. The results show that evaluating product attributes is a cognitive process that modulates attention in the aforementioned regions. When participants evaluate the alternatives, categorical processing occurred on the basis of similarity judgment. The situation in Condition 1 produced a similarity overlap between the product and the expectation and resulted in a higher P300. Otherwise, there was no overlap, leading to a smaller P300. Hence, the P300 may be a useful neural endogenous indicator for measuring consumers' evaluations of products in marketing research.
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Reduction of degraded events in miniaturized proportional counters
Energy Technology Data Exchange (ETDEWEB)
Plaga, R.; Kirsten, T. (Max Planck Inst. fuer Kernphysik, Heidelberg (Germany))
1991-11-15
A method to reduce the number of degraded events in miniaturized proportional counters is described. A shaping of the outer cathode leads to a more uniform gas gain along the counter axis. The method is useful in situations in which the total number of decay events is very low. The effects leading to degraded events are studied theoretically and experimentally. The usefulness of the method is demonstrated by using it for the proportional counter of the GALLEX solar neutrino experiment. (orig.).
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International Nuclear Information System (INIS)
Vintenat, Lionel
1999-01-01
A good quality often attributed to artificial neural networks is fault tolerance. In general presentation works, this property is almost always introduced as 'natural', i.e. being obtained without any specific precaution during learning. Besides, space environment is known to be aggressive towards on-board hardware, inducing various abnormal operations. Particularly, digital components suffer from upset phenomenon, i.e. misplaced switches of memory flip-flops. These two observations lead to the question: would neural chips constitute an interesting and robust solution to implement some board functions of spacecrafts? First, the various aspects of the problem are detailed: artificial neural networks and their fault tolerance, neural chips, space environment and resulting failures. Further to this presentation, a particular technique to carry out neural chips is selected because of its simplicity, and especially because it requires few memory flip-flops: random pulse streams. An original method for star recognition inside a field-of-view is then proposed for the board function 'attitude computation'. This method relies on a winner-takes-all competition network, and on a Kohonen self-organized map. An hardware implementation of those two neural models is then proposed using random pulse streams. Thanks to this realization, on one hand difficulties related to that particular implementation technique can be highlighted, and on the other hand a first evaluation of its practical fault tolerance can be carried out. (author) [fr
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Tissue fusion during early mammalian development requires coordination of multiple cell types, the extracellular matrix, and complex signaling pathways. Fusion events during processes including heart development, neural tube closure, and palatal fusion are dependent on signaling ...
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Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.
Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp
2017-04-01
According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford
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Construction and updating of event models in auditory event processing.
Huff, Markus; Maurer, Annika E; Brich, Irina; Pagenkopf, Anne; Wickelmaier, Florian; Papenmeier, Frank
2018-02-01
Humans segment the continuous stream of sensory information into distinct events at points of change. Between 2 events, humans perceive an event boundary. Present theories propose changes in the sensory information to trigger updating processes of the present event model. Increased encoding effort finally leads to a memory benefit at event boundaries. Evidence from reading time studies (increased reading times with increasing amount of change) suggest that updating of event models is incremental. We present results from 5 experiments that studied event processing (including memory formation processes and reading times) using an audio drama as well as a transcript thereof as stimulus material. Experiments 1a and 1b replicated the event boundary advantage effect for memory. In contrast to recent evidence from studies using visual stimulus material, Experiments 2a and 2b found no support for incremental updating with normally sighted and blind participants for recognition memory. In Experiment 3, we replicated Experiment 2a using a written transcript of the audio drama as stimulus material, allowing us to disentangle encoding and retrieval processes. Our results indicate incremental updating processes at encoding (as measured with reading times). At the same time, we again found recognition performance to be unaffected by the amount of change. We discuss these findings in light of current event cognition theories. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Predictions of SEP events by means of a linear filter and layer-recurrent neural network
Czech Academy of Sciences Publication Activity Database
Valach, F.; Revallo, M.; Hejda, Pavel; Bochníček, Josef
2011-01-01
Roč. 69, č. 9-10 (2011), s. 758-766 ISSN 0094-5765 R&D Projects: GA AV ČR(CZ) IAA300120608; GA MŠk OC09070 Grant - others:VEGA(SK) 2/0015/11; VEGA(SK) 2/0022/11 Institutional research plan: CEZ:AV0Z30120515 Keywords : coronal mass ejection * X-ray flare * solar energetic particles * artificial neural network Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 0.614, year: 2011
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A model of interval timing by neural integration.
Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip
2011-06-22
We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.
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Lee, Woogul; Reeve, Johnmarshall; Xue, Yiqun; Xiong, Jinhu
2012-05-01
The contemporary neural understanding of motivation is based almost exclusively on the neural mechanisms of incentive motivation. Recognizing this as a limitation, we used event-related functional magnetic resonance imaging (fMRI) to pursue the viability of expanding the neural understanding of motivation by initiating a pioneering study of intrinsic motivation by scanning participants' neural activity when they decided to act for intrinsic reasons versus when they decided to act for extrinsic reasons. As expected, intrinsic reasons for acting more recruited insular cortex activity while extrinsic reasons for acting more recruited posterior cingulate cortex (PCC) activity. The results demonstrate that engagement decisions based on intrinsic motivation are more determined by weighing the presence of spontaneous self-satisfactions such as interest and enjoyment while engagement decisions based on extrinsic motivation are more determined by weighing socially-acquired stored values as to whether the environmental incentive is attractive enough to warrant action.
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Directory of Open Access Journals (Sweden)
Eva eSchoenberger
2014-03-01
Full Text Available Functional brain imaging studies have improved our knowledge of the neural localization of language functions and the functional recovery after a lesion. However, the neural correlates of agrammatic symptoms in aphasia remain largely unknown. The present fMRI study examined the neural correlates of morpho-syntactic encoding and agrammatic errors in continuous language production by combining three approaches. First, the neural mechanisms underlying natural morpho-syntactic processing in a picture description task were analyzed in 15 healthy speakers. Second, agrammatic-like speech behavior was induced in the same group of healthy speakers to study the underlying functional processes by limiting the utterance length. In a third approach, five agrammatic participants performed the picture description task to gain insights in the neural correlates of agrammatism and the functional reorganization of language processing after stroke. In all approaches, utterances were analyzed for syntactic completeness, complexity and morphology. Event-related data analysis was conducted by defining every clause-like unit (CLU as an event with its onset-time and duration. Agrammatic and correct CLUs were contrasted. Due to the small sample size as well as heterogeneous lesion sizes and sites with lesion foci in the insula lobe, inferior frontal, superior temporal and inferior parietal areas the activation patterns in the agrammatic speakers were analyzed on a single subject level. In the group of healthy speakers, posterior temporal and inferior parietal areas were associated with greater morpho-syntactic demands in complete and complex CLUs. The intentional manipulation of morpho-syntactic structures and the omission of function words were associated with additional inferior frontal activation. Overall, the results revealed that the investigation of the neural correlates of agrammatic language production can be reasonably conducted with an overt language production
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Schönberger, Eva; Heim, Stefan; Meffert, Elisabeth; Pieperhoff, Peter; da Costa Avelar, Patricia; Huber, Walter; Binkofski, Ferdinand; Grande, Marion
2014-01-01
Functional brain imaging studies have improved our knowledge of the neural localization of language functions and the functional reorganization after a lesion. However, the neural correlates of agrammatic symptoms in aphasia remain largely unknown. The present fMRI study examined the neural correlates of morpho-syntactic encoding and agrammatic errors in continuous language production by combining three approaches. First, the neural mechanisms underlying natural morpho-syntactic processing in a picture description task were analyzed in 15 healthy speakers. Second, agrammatic-like speech behavior was induced in the same group of healthy speakers to study the underlying functional processes by limiting the utterance length. In a third approach, five agrammatic participants performed the picture description task to gain insights in the neural correlates of agrammatism and the functional reorganization of language processing after stroke. In all approaches, utterances were analyzed for syntactic completeness, complexity, and morphology. Event-related data analysis was conducted by defining every clause-like unit (CLU) as an event with its onset-time and duration. Agrammatic and correct CLUs were contrasted. Due to the small sample size as well as heterogeneous lesion sizes and sites with lesion foci in the insula lobe, inferior frontal, superior temporal and inferior parietal areas the activation patterns in the agrammatic speakers were analyzed on a single subject level. In the group of healthy speakers, posterior temporal and inferior parietal areas were associated with greater morpho-syntactic demands in complete and complex CLUs. The intentional manipulation of morpho-syntactic structures and the omission of function words were associated with additional inferior frontal activation. Overall, the results revealed that the investigation of the neural correlates of agrammatic language production can be reasonably conducted with an overt language production paradigm.
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Evolvable Neural Software System
Curtis, Steven A.
2009-01-01
The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.
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Synthesis on accumulation of putative neurotransmitters by cultured neural crest cells
International Nuclear Information System (INIS)
Maxwell, G.D.; Sietz, P.D.; Rafford, C.E.
1982-01-01
The events mediating the differentiation of embryonic neural crest cells into several types of neurons are incompletely understood. In order to probe one aspect of this differentiation, we have examined the capacity of cultured quail trunk neural crest cells to synthesize, from radioactive precursors, and store several putative neurotransmitter compounds. These neural crest cultures develop the capacity to synthesize and accumulate acetylcholine and the catecholamines norepinephrine and dopamine. In contrast, detectable but relatively little synthesis and accumulation of 5-hydroxytryptamine gamma-aminobutyric acid, or octopamine from the appropriate radiolabeled precursors were observed. The capacity for synthesis and accumulation of radiolabeled acetylcholine and catecholamines is very low or absent at 2 days in vitro. Between 3 and 7 days in vitro, there is a marked rise in both catecholamine and acetylcholine accumulation in the cultures. These findings suggest that, under the particular conditions used in these experiments, the development of neurotransmitter biosynthesis in trunk neural crest cells ijs restricted and resembles, at least partially, the pattern observed in vivo. The development of this capacity to synthesize and store radiolabeled acetylcholine and catecholamines from the appropriate radioactive precursors coincides closely with the development of the activities of the synthetic enzymes choline acetyltransferase and dopamine beta-hydroxylase reported by others
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Advances in neural networks computational intelligence for ICT
Esposito, Anna; Morabito, Francesco; Pasero, Eros
2016-01-01
This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...
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Nanowire electrodes for high-density stimulation and measurement of neural circuits
Directory of Open Access Journals (Sweden)
Jacob T. Robinson
2013-03-01
Full Text Available Brain-machine interfaces (BMIs that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.
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Lexical decision making in adults with dyslexia: an event-related potential study
Directory of Open Access Journals (Sweden)
Karen E. Waldie
2012-12-01
Full Text Available http://dx.doi.org/10.5007/2175-8026.2012n63p37 Performance on a lexical decision task was investigated in 12 English speaking adults with dyslexia. two age-matched comparison groups of unimpaired readers were included: 14 monolingual adults and 15 late proficient bilinguals. The aim of the study was to determine the timing of neural events with event-related potentials (ErPs during lexical decision-making between individuals with dyslexia and unimpaired readers (both unilingual and bilingual. ErPs were calculated for posterior sites in the left and right hemispheres and the P1 and n170 components were compared between groups. Event-related EEG coherence (measuring the synchrony of neural events during lexical tasks both between and within cerebral hemispheres was also calculated for seven electrode pairs (three pairs at symmetrical locations between hemispheres, and two pairs within each hemisphere. We chose to recruit two comparison groups of unimpaired readers to better clarify the findings resulting from the right hemisphere (EEG coherence analysis. That is, both late-proficient bilinguals and adults with dyslexia are thought to rely on right hemisphere resources during reading. We hypothesized that those with dyslexia would show less within-hemisphere coherence and more between-hemisphere coherence than bilingual individuals. dyslexics had both lower amplitude and longer latency n170 activation than unimpaired readers, suggesting asynchronous neural activity. Dyslexics showed greater synchrony between hemispheres in gamma range frequencies whereas the bilingual group showed greater synchrony in the theta frequency band (both within and between hemispheres. This study demonstrates that individuals with developmental dyslexia have reduced amplitudes in the n170 and higher synchrony between hemispheres during a reading task. The differences may be due to an asynchrony of neuronal activity at the point where
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The differentiation of embryonic stem cells seeded on electrospun nanofibers into neural lineages.
Xie, Jingwei; Willerth, Stephanie M; Li, Xiaoran; Macewan, Matthew R; Rader, Allison; Sakiyama-Elbert, Shelly E; Xia, Younan
2009-01-01
Due to advances in stem cell biology, embryonic stem (ES) cells can be induced to differentiate into a particular mature cell lineage when cultured as embryoid bodies. Although transplantation of ES cells-derived neural progenitor cells has been demonstrated with some success for either spinal cord injury repair in small animal model, control of ES cell differentiation into complex, viable, higher ordered tissues is still challenging. Mouse ES cells have been induced to become neural progenitors by adding retinoic acid to embryoid body cultures for 4 days. In this study, we examine the use of electrospun biodegradable polymers as scaffolds not only for enhancing the differentiation of mouse ES cells into neural lineages but also for promoting and guiding the neurite outgrowth. A combination of electrospun fiber scaffolds and ES cells-derived neural progenitor cells could lead to the development of a better strategy for nerve injury repair.
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Consolidation of Complex Events via Reinstatement in Posterior Cingulate Cortex
Keidel, James L.; Ing, Leslie P.; Horner, Aidan J.
2015-01-01
It is well-established that active rehearsal increases the efficacy of memory consolidation. It is also known that complex events are interpreted with reference to prior knowledge. However, comparatively little attention has been given to the neural underpinnings of these effects. In healthy adults humans, we investigated the impact of effortful, active rehearsal on memory for events by showing people several short video clips and then asking them to recall these clips, either aloud (Experiment 1) or silently while in an MRI scanner (Experiment 2). In both experiments, actively rehearsed clips were remembered in far greater detail than unrehearsed clips when tested a week later. In Experiment 1, highly similar descriptions of events were produced across retrieval trials, suggesting a degree of semanticization of the memories had taken place. In Experiment 2, spatial patterns of BOLD signal in medial temporal and posterior midline regions were correlated when encoding and rehearsing the same video. Moreover, the strength of this correlation in the posterior cingulate predicted the amount of information subsequently recalled. This is likely to reflect a strengthening of the representation of the video's content. We argue that these representations combine both new episodic information and stored semantic knowledge (or “schemas”). We therefore suggest that posterior midline structures aid consolidation by reinstating and strengthening the associations between episodic details and more generic schematic information. This leads to the creation of coherent memory representations of lifelike, complex events that are resistant to forgetting, but somewhat inflexible and semantic-like in nature. SIGNIFICANCE STATEMENT Memories are strengthened via consolidation. We investigated memory for lifelike events using video clips and showed that rehearsing their content dramatically boosts memory consolidation. Using MRI scanning, we measured patterns of brain activity while
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Automatic detection of esophageal pressure events. Is there an alternative to rule-based criteria?
DEFF Research Database (Denmark)
Kruse-Andersen, S; Rütz, K; Kolberg, Jens Godsk
1995-01-01
of relevant pressure peaks at the various recording levels. Until now, this selection has been performed entirely by rule-based systems, requiring each pressure deflection to fit within predefined rigid numerical limits in order to be detected. However, due to great variations in the shapes of the pressure...... curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...
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ATLAS Event Display: Light-by-Light Scattering
ATLAS Collaboration
2017-01-01
An event display of light-by-light scattering in ultra-peripheral lead+lead collisions at 5.02 TeV with the ATLAS detector at the LHC. The event 461251458 from run 287931 recorded on 13 December 2015 at 09:51:07 is shown. Two back-to-back photons with an invariant mass of 24 GeV with no additional activity in the detector are presented. All calorimeter cells with E>500 MeV are shown.
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Chaotic diagonal recurrent neural network
International Nuclear Information System (INIS)
Wang Xing-Yuan; Zhang Yi
2012-01-01
We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)
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Computational modeling of neural plasticity for self-organization of neural networks.
Chrol-Cannon, Joseph; Jin, Yaochu
2014-11-01
Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Application of artificial neural nets to Shashlik calorimetry
International Nuclear Information System (INIS)
Bonesini, M.; Paganoni, M.; Terranova, F.
1997-01-01
Artificial neural networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given. (orig.)
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The role of stochasticity in an information-optimal neural population code
Energy Technology Data Exchange (ETDEWEB)
Stocks, N G; Nikitin, A P [School of Engineering, University of Warwick, Coventry CV4 7AL (United Kingdom); McDonnell, M D [Institute for Telecommunications Research, University of South Australia, SA 5095 (Australia); Morse, R P, E-mail: n.g.stocks@warwick.ac.u [School of Life and Health Sciences, Aston University, Birmingham B4 7ET (United Kingdom)
2009-12-01
In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.
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Li, Xiaoqing; Zhang, Yuping; Xia, Jinyan; Swaab, Tamara Y
2017-07-28
Although numerous studies have demonstrated that the language processing system can predict upcoming content during comprehension, there is still no clear picture of the anticipatory stage of predictive processing. This electroencephalograph study examined the cognitive and neural oscillatory mechanisms underlying anticipatory processing during language comprehension, and the consequences of this prediction for bottom-up processing of predicted/unpredicted content. Participants read Mandarin Chinese sentences that were either strongly or weakly constraining and that contained critical nouns that were congruent or incongruent with the sentence contexts. We examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. The results revealed that, at the integration stage, the strong-constraint condition (compared to the weak-constraint condition) elicited a reduced N400 and reduced theta activity (4-7Hz) for the congruent nouns, but induced beta (13-18Hz) and theta (4-7Hz) power decreases for the incongruent nouns, indicating benefits of confirmed predictions and potential costs of disconfirmed predictions. More importantly, at the anticipatory stage, the strongly constraining context elicited an enhanced sustained anterior negativity and beta power decrease (19-25Hz), which indicates that strong prediction places a higher processing load on the anticipatory stage of processing. The differences (in the ease of processing and the underlying neural oscillatory activities) between anticipatory and integration stages of lexical processing were discussed with regard to predictive processing models. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Study of t$\\bar{t}$ production in tau jets channel at CDFII using neural networks
Energy Technology Data Exchange (ETDEWEB)
Amerio, Silvia [Univ. of Trento (Italy)
2005-12-01
CDF (Collider Detector at Fermilab) is a particle detector located at Fermi National Laboratories, near Chicago. it allows to study decay products of p$\\bar{p}$ collisions at center-of-mass energy of 1.96 TeV. During its first period of data taking (RunI), CDF observed for the first time the top quark (1995). The current period of data taking (RunII) is devoted to precise measurements of top properties and to search for new physics. This thesis work is about the top decay channel named τ + jets. A t$\\bar{t}$ pair decays in two W bosons and two b quarks. In a τ + jets event, one out of the two W decays into two jets of hadrons, while the other produces a τ lepton and a neutrino; the τ decays semileptonically in one or more charged and neutral pions while b quarks hadronize producing two jets of particles. Thus the final state of a τ + jets event has this specific signature: five jets, one τ-like, i.e. narrow and with low track multiplicity, two from b quarks, two from a W boson and a large amount of missing energy from two τ neutrinos. They search for this signal in 311 pb-1 of data collected with TOP{_}MULTIJET trigger. They use neural networks to separate signal from background and on the selected sample they perform a t$\\bar{t}$ production cross section measurement. The thesis is structured as follows: in Chapter 1 they outline the physics of top and τ, concentrating on their discovery, production mechanisms and current physics results involving them. Chapter 2 is devoted to the description of the experimental setup: the accelerator complex first and CDF detector then. The trigger system is described in Chapter 3, while Chapter 4 shows how particles are reconstructed exploiting information from different CDF subdetectors. With Chapter 5 they begin to present their analysis: we use a feed forward neural network based on a minimization algorithm developed in Trento University, called Reactive Taboo Search (RTS), especially designed to rapidly
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Study of t anti-t production in tau jets channel at CDFII using neural networks
International Nuclear Information System (INIS)
Amerio, Silvia
2005-01-01
CDF (Collider Detector at Fermilab) is a particle detector located at Fermi National Laboratories, near Chicago. it allows to study decay products of p(bar p) collisions at center-of-mass energy of 1.96 TeV. During its first period of data taking (RunI), CDF observed for the first time the top quark (1995). The current period of data taking (RunII) is devoted to precise measurements of top properties and to search for new physics. This thesis work is about the top decay channel named τ + jets. A t(bar t) pair decays in two W bosons and two b quarks. In a τ + jets event, one out of the two W decays into two jets of hadrons, while the other produces a τ lepton and a neutrino; the τ decays semileptonically in one or more charged and neutral pions while b quarks hadronize producing two jets of particles. Thus the final state of a τ + jets event has this specific signature: five jets, one τ-like, i.e. narrow and with low track multiplicity, two from b quarks, two from a W boson and a large amount of missing energy from two τ neutrinos. They search for this signal in 311 pb -1 of data collected with TOP( ) MULTIJET trigger. They use neural networks to separate signal from background and on the selected sample they perform a t(bar t) production cross section measurement. The thesis is structured as follows: in Chapter 1 they outline the physics of top and τ, concentrating on their discovery, production mechanisms and current physics results involving them. Chapter 2 is devoted to the description of the experimental setup: the accelerator complex first and CDF detector then. The trigger system is described in Chapter 3, while Chapter 4 shows how particles are reconstructed exploiting information from different CDF subdetectors. With Chapter 5 they begin to present their analysis: we use a feed forward neural network based on a minimization algorithm developed in Trento University, called Reactive Taboo Search (RTS), especially designed to rapidly escape from local
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Kreidler, Kathryn; Wray, Amanda Hampton; Usler, Evan; Weber, Christine
2017-01-01
Purpose: Maturation of neural processes for language may lag in some children who stutter (CWS), and event-related potentials (ERPs) distinguish CWS who have recovered from those who have persisted. The current study explores whether ERPs indexing semantic processing may distinguish children who will eventually persist in stuttering…
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Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise
Keeler, James D.; Pichler, Elgar E.; Ross, John
1989-03-01
We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may ``quench'' the neural patterns of activity to enhance the ability to learn details.
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Directory of Open Access Journals (Sweden)
Chien-Lin Huang
2015-11-01
Full Text Available This study applies Real-Time Recurrent Learning Neural Network (RTRLNN and Adaptive Network-based Fuzzy Inference System (ANFIS with novel heuristic techniques to develop an advanced prediction model of accumulated total inflow of a reservoir in order to solve the difficulties of future long lead-time highly varied uncertainty during typhoon attacks while using a real-time forecast. For promoting the temporal-spatial forecasted precision, the following original specialized heuristic inputs were coupled: observed-predicted inflow increase/decrease (OPIID rate, total precipitation, and duration from current time to the time of maximum precipitation and direct runoff ending (DRE. This study also investigated the temporal-spatial forecasted error feature to assess the feasibility of the developed models, and analyzed the output sensitivity of both single and combined heuristic inputs to determine whether the heuristic model is susceptible to the impact of future forecasted uncertainty/errors. Validation results showed that the long lead-time–predicted accuracy and stability of the RTRLNN-based accumulated total inflow model are better than that of the ANFIS-based model because of the real-time recurrent deterministic routing mechanism of RTRLNN. Simulations show that the RTRLNN-based model with coupled heuristic inputs (RTRLNN-CHI, average error percentage (AEP/average forecast lead-time (AFLT: 6.3%/49 h can achieve better prediction than the model with non-heuristic inputs (AEP of RTRLNN-NHI and ANFIS-NHI: 15.2%/31.8% because of the full consideration of real-time hydrological initial/boundary conditions. Besides, the RTRLNN-CHI model can promote the forecasted lead-time above 49 h with less than 10% of AEP which can overcome the previous forecasted limits of 6-h AFLT with above 20%–40% of AEP.
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Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection
Directory of Open Access Journals (Sweden)
Erik Marchi
2017-01-01
Full Text Available In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases.
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Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.
Marchi, Erik; Vesperini, Fabio; Squartini, Stefano; Schuller, Björn
2017-01-01
In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F -measure over the three databases.
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Event segmentation improves event memory up to one month later.
Flores, Shaney; Bailey, Heather R; Eisenberg, Michelle L; Zacks, Jeffrey M
2017-08-01
When people observe everyday activity, they spontaneously parse it into discrete meaningful events. Individuals who segment activity in a more normative fashion show better subsequent memory for the events. If segmenting events effectively leads to better memory, does asking people to attend to segmentation improve subsequent memory? To answer this question, participants viewed movies of naturalistic activity with instructions to remember the activity for a later test, and in some conditions additionally pressed a button to segment the movies into meaningful events or performed a control condition that required button-pressing but not attending to segmentation. In 5 experiments, memory for the movies was assessed at intervals ranging from immediately following viewing to 1 month later. Performing the event segmentation task led to superior memory at delays ranging from 10 min to 1 month. Further, individual differences in segmentation ability predicted individual differences in memory performance for up to a month following encoding. This study provides the first evidence that manipulating event segmentation affects memory over long delays and that individual differences in event segmentation are related to differences in memory over long delays. These effects suggest that attending to how an activity breaks down into meaningful events contributes to memory formation. Instructing people to more effectively segment events may serve as a potential intervention to alleviate everyday memory complaints in aging and clinical populations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
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Out-of-order event processing in kinetic data structures
DEFF Research Database (Denmark)
Abam, Mohammad; de Berg, Mark; Agrawal, Pankaj
2011-01-01
’s for the maintenance of several fundamental structures such as kinetic sorting and kinetic tournament trees, which overcome the difficulty by employing a refined event scheduling and processing technique. We prove that the new event scheduling mechanism leads to a KDS that is correct except for finitely many short......We study the problem of designing kinetic data structures (KDS’s for short) when event times cannot be computed exactly and events may be processed in a wrong order. In traditional KDS’s this can lead to major inconsistencies from which the KDS cannot recover. We present more robust KDS...
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Directory of Open Access Journals (Sweden)
Lucimar M.F. de Carvalho
2008-06-01
Full Text Available OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication architecture and an artificial neural network with backpropagation learning algorithm (ANNB. RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%; the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.OBJETIVO: Investigar diferentes operações aritméticas difusas para auxíliar no diagnóstico de eventos epilépticos e eventos não-epilépticos. MÉTODO: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB. RESULTADOS: A amostra estudada foi de 244 pacientes com maior freqüência no sexo feminino. O número de decisões corretas na fase de teste, obtidas através do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%; o melhor resultado de especificidade foi obtido com a RNAB (95,65%. CONCLUSÃO: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrões juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema.
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Dietrich, Felix
2017-01-01
Reconstructing the invariant mass in a Higgs boson decay event containing tau leptons turns out to be a challenging endeavour. The aim of this summer student project is to implement a new algorithm for this task, using deep neural networks and machine learning. The results are compared to SVFit, an existing algorithm that uses dynamical likelihood techniques. A neural network is found that reaches the accuracy of SVFit at low masses and even surpasses it at higher masses, while at the same time providing results a thousand times faster.
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Weiland, James D.
2011-07-01
strategy that can potentially optimize dosing, reduce side effects and extend implant battery life. The article by Liang et al investigates methods for closed loop control of epilepsy, using neural recording to detect imminent seizures and stimulation to halt the aberrant neural activity leading to seizure. Liu et al report on a model of basal ganglia function that could lead to optimized, closed-loop stimulation to reduce symptoms of Parkinson's disease while avoiding side effects. Our laboratory, as described in Ray et al, is investigating the interface between stimulating microelectrodes and the retina, to inform the design of a high-resolution retinal prosthesis. Three contributions address the issue of long-term stability of cortical recording, which remains a major hurdle to implementation of neural recording systems. The Utah group reports on the in vitro testing of a completely implantable, wireless neural recording system, demonstrating almost one year of reliable performance under simulated implant conditions. Shenoy's laboratory at Stanford demonstrates that useful signals can be recorded from research animals for over 2.5 years. Lempka et al describe a modeling approach to analyzing intracortical microelectrode recordings. These findings represent real and significant progress towards overcoming the final barriers to implementation of a reliable cortical interface. Planning is well underway for the 40th Neural Interfaces Conference, which will be held in Salt Lake City, Utah, in June 2012. The conference promises to continue the NIC tradition of showcasing the latest results from clinical trials of neural interface therapies while providing ample time for dynamic exchange amongst the interdisciplinary audience of engineers, scientists and clinicians.
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Apoptosis in neural crest cells by functional loss of APC tumor suppressor gene
Hasegawa, Sumitaka; Sato, Tomoyuki; Akazawa, Hiroshi; Okada, Hitoshi; Maeno, Akiteru; Ito, Masaki; Sugitani, Yoshinobu; Shibata, Hiroyuki; Miyazaki, Jun-ichi; Katsuki, Motoya; Yamauchi, Yasutaka; Yamamura, Ken-ichi; Katamine, Shigeru; Noda, Tetsuo
2002-01-01
Apc is a gene associated with familial adenomatous polyposis coli (FAP) and its inactivation is a critical step in colorectal tumor formation. The protein product, adenomatous polyposis coli (APC), acts to down-regulate intracellular levels of β-catenin, a key signal transducer in the Wnt signaling. Conditional targeting of Apc in the neural crest of mice caused massive apoptosis of cephalic and cardiac neural crest cells at about 11.5 days post coitum, resulting in craniofacial and cardiac anomalies at birth. Notably, the apoptotic cells localized in the regions where β-catenin had accumulated. In contrast to its role in colorectal epithelial cells, inactivation of APC leads to dysregulation of β-catenin/Wnt signaling with resultant apoptosis in certain tissues including neural crest cells. PMID:11756652
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Neural Parallel Engine: A toolbox for massively parallel neural signal processing.
Tam, Wing-Kin; Yang, Zhi
2018-05-01
Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.
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Planning and managing a corporate event : case: Event Activation Galaxy Studio Samsung S7/ S7 Edge
Bui, Quyen; Tran, Fa
2017-01-01
The thesis was written about the case: Event Activation Studio Galaxy Samsung S7/S7 Edge. Samsung is a global leading conglomerate company in the technology industry. This thesis discusses how to plan and manage an Event Activation for Samsung. The primary objective of this thesis was to provide a step-by-step guideline for planning and managing a corporate event. This objective was achieved by performing the following tasks: clarifying the influential role of corporate events in marketi...
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Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.
Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon
2016-01-01
Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.
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Biologically based neural circuit modelling for the study of fear learning and extinction
Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra
2016-11-01
The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.
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Directory of Open Access Journals (Sweden)
Tetsuo Yasugi
Full Text Available Neurogenesis is initiated by a set of basic Helix-Loop-Helix (bHLH transcription factors that specify neural progenitors and allow them to generate neurons in multiple rounds of asymmetric cell division. The Drosophila Daughterless (Da protein and its mammalian counterparts (E12/E47 act as heterodimerization factors for proneural genes and are therefore critically required for neurogenesis. Here, we demonstrate that Da can also be an inhibitor of the neural progenitor fate whose absence leads to stem cell overproliferation and tumor formation. We explain this paradox by demonstrating that Da induces the differentiation factor Prospero (Pros whose asymmetric segregation is essential for differentiation in one of the two daughter cells. Da co-operates with the bHLH transcription factor Asense, whereas the other proneural genes are dispensible. After mitosis, Pros terminates Asense expression in one of the two daughter cells. In da mutants, pros is not expressed, leading to the formation of lethal transplantable brain tumors. Our results define a transcriptional feedback loop that regulates the balance between self-renewal and differentiation in Drosophila optic lobe neuroblasts. They indicate that initiation of a neural differentiation program in stem cells is essential to prevent tumorigenesis.
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A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.
von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H
2016-10-26
The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability
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A review of evidence of health benefit from artificial neural networks in medical intervention.
Lisboa, P J G
2002-01-01
The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.
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Neural Control of the Lower Urinary Tract
de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki
2015-01-01
This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273
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DEFF Research Database (Denmark)
Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten
2007-01-01
By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...
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Hayakawa-Yano, Yoshika; Suyama, Satoshi; Nogami, Masahiro; Yugami, Masato; Koya, Ikuko; Furukawa, Takako; Zhou, Li; Abe, Manabu; Sakimura, Kenji; Takebayashi, Hirohide; Nakanishi, Atsushi; Okano, Hideyuki; Yano, Masato
2017-09-15
Cell type-specific transcriptomes are enabled by the action of multiple regulators, which are frequently expressed within restricted tissue regions. In the present study, we identify one such regulator, Quaking 5 (Qki5), as an RNA-binding protein (RNABP) that is expressed in early embryonic neural stem cells and subsequently down-regulated during neurogenesis. mRNA sequencing analysis in neural stem cell culture indicates that Qki proteins play supporting roles in the neural stem cell transcriptome and various forms of mRNA processing that may result from regionally restricted expression and subcellular localization. Also, our in utero electroporation gain-of-function study suggests that the nuclear-type Qki isoform Qki5 supports the neural stem cell state. We next performed in vivo transcriptome-wide protein-RNA interaction mapping to search for direct targets of Qki5 and elucidate how Qki5 regulates neural stem cell function. Combined with our transcriptome analysis, this mapping analysis yielded a bona fide map of Qki5-RNA interaction at single-nucleotide resolution, the identification of 892 Qki5 direct target genes, and an accurate Qki5-dependent alternative splicing rule in the developing brain. Last, our target gene list provides the first compelling evidence that Qki5 is associated with specific biological events; namely, cell-cell adhesion. This prediction was confirmed by histological analysis of mice in which Qki proteins were genetically ablated, which revealed disruption of the apical surface of the lateral wall in the developing brain. These data collectively indicate that Qki5 regulates communication between neural stem cells by mediating numerous RNA processing events and suggest new links between splicing regulation and neural stem cell states. © 2017 Hayakawa-Yano et al.; Published by Cold Spring Harbor Laboratory Press.
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Top quark event modelling and generators
Rahmat, Rahmat
2016-01-01
State-of-the-art theoretical predictions accurate to next-to-leading order QCD interfaced with Pythia8 and Herwig++ event generators are tested by comparing the unfolded ttbar differential data collected with the CMS detector at 8 TeV. These predictions are also compared with the underlying event activity distributions in ttbar events using CMS proton-proton data collected in 2015 at a center of mass energy of 13 TeV.
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Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
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Computing single step operators of logic programming in radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
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Computing single step operators of logic programming in radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-01-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
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Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval.
Xiao, Xiaoqian; Dong, Qi; Gao, Jiahong; Men, Weiwei; Poldrack, Russell A; Xue, Gui
2017-03-15
Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions. SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically
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International Nuclear Information System (INIS)
Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro; Costa, Rodrigo Moyses; Tello, Cledola Cassia Oliveira de
2015-01-01
A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)
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Energy Technology Data Exchange (ETDEWEB)
Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro, E-mail: tolentino@timoteo.cefetmg.br [Centro Federal de Educacao Tecnologica de Minas Gerais (CEFET-MG), Timoteo, MG (Brazil); Costa, Rodrigo Moyses, E-mail: rodrigo@moyses.com.br [Universidade de Itauna, Itauna, MG (Brazil); Tello, Cledola Cassia Oliveira de, E-mail: tellocc@cdtn.br [Centro de Desenvolvimento da Tecnologia Nucelar (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)
2015-07-01
A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)
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The Neural Basis of Vocal Pitch Imitation in Humans.
Belyk, Michel; Pfordresher, Peter Q; Liotti, Mario; Brown, Steven
2016-04-01
Vocal imitation is a phenotype that is unique to humans among all primate species, and so an understanding of its neural basis is critical in explaining the emergence of both speech and song in human evolution. Two principal neural models of vocal imitation have emerged from a consideration of nonhuman animals. One hypothesis suggests that putative mirror neurons in the inferior frontal gyrus pars opercularis of Broca's area may be important for imitation. An alternative hypothesis derived from the study of songbirds suggests that the corticostriate motor pathway performs sensorimotor processes that are specific to vocal imitation. Using fMRI with a sparse event-related sampling design, we investigated the neural basis of vocal imitation in humans by comparing imitative vocal production of pitch sequences with both nonimitative vocal production and pitch discrimination. The strongest difference between these tasks was found in the putamen bilaterally, providing a striking parallel to the role of the analogous region in songbirds. Other areas preferentially activated during imitation included the orofacial motor cortex, Rolandic operculum, and SMA, which together outline the corticostriate motor loop. No differences were seen in the inferior frontal gyrus. The corticostriate system thus appears to be the central pathway for vocal imitation in humans, as predicted from an analogy with songbirds.
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A continuous-time neural model for sequential action.
Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard
2014-11-05
Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
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International Nuclear Information System (INIS)
Bandurin, D.V.; Skachkov, N.B.
2001-01-01
A neural network technique is used to discriminate between quark and gluon jets produced in the qg→q+γ and qq bar → g+γ processes at the LHC. Considering the network as a trigger and using the PYTHIA event generator and the full event fast simulation CMSJET package for the CMS detector we obtain signal-to-background ratios
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Neural control hierarchy of the heart has not evolved to deal with myocardial ischemia.
Kember, G; Armour, J A; Zamir, M
2013-08-01
The consequences of myocardial ischemia are examined from the standpoint of the neural control system of the heart, a hierarchy of three neuronal centers residing in central command, intrathoracic ganglia, and intrinsic cardiac ganglia. The basis of the investigation is the premise that while this hierarchical control system has evolved to deal with "normal" physiological circumstances, its response in the event of myocardial ischemia is unpredictable because the singular circumstances of this event are as yet not part of its evolutionary repertoire. The results indicate that the harmonious relationship between the three levels of control breaks down, because of a conflict between the priorities that they have evolved to deal with. Essentially, while the main priority in central command is blood demand, the priority at the intrathoracic and cardiac levels is heart rate. As a result of this breakdown, heart rate becomes less predictable and therefore less reliable as a diagnostic guide as to the traumatic state of the heart, which it is commonly used as such following an ischemic event. On the basis of these results it is proposed that under the singular conditions of myocardial ischemia a determination of neural control indexes in addition to cardiovascular indexes has the potential of enhancing clinical outcome.
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International Nuclear Information System (INIS)
Abdel-Aal, M.M.Z.
2004-01-01
Automation in large, complex systems such as chemical plants, electrical power generation, aerospace and nuclear plants has been steadily increasing in the recent past. automated diagnosis and control forms a necessary part of these systems,this contains thousands of alarms processing in every component, subsystem and system. so the accurate and speed of diagnosis of faults is an important factors in operation and maintaining their health and continued operation and in reducing of repair and recovery time. using of artificial intelligence facilitates the alarm classifications and faults diagnosis to control any abnormal events during the operation cycle of the plant. thesis work uses the artificial neural network as a powerful classification tool. the work basically is has two components, the first is to effectively train the neural network using particle swarm optimization, which non-derivative based technique. to achieve proper training of the neural network to fault classification problem and comparing this technique to already existing techniques
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Chiang, Hsueh-Sheng; Mudar, Raksha A; Pudhiyidath, Athula; Spence, Jeffrey S; Womack, Kyle B; Cullum, C Munro; Tanner, Jeremy A; Eroh, Justin; Kraut, Michael A; Hart, John
2015-01-01
Deficits in semantic memory in individuals with amnestic mild cognitive impairment (aMCI) have been previously reported, but the underlying neurobiological mechanisms remain to be clarified. We examined event-related potentials (ERPs) associated with semantic memory retrieval in 16 individuals with aMCI as compared to 17 normal controls using the Semantic Object Retrieval Task (EEG SORT). In this task, subjects judged whether pairs of words (object features) elicited retrieval of an object (retrieval trials) or not (non-retrieval trials). Behavioral findings revealed that aMCI subjects had lower accuracy scores and marginally longer reaction time compared to controls. We used a multivariate analytical technique (STAT-PCA) to investigate similarities and differences in ERPs between aMCI and control groups. STAT-PCA revealed a left fronto-temporal component starting at around 750 ms post-stimulus in both groups. However, unlike controls, aMCI subjects showed an increase in the frontal-parietal scalp potential that distinguished retrieval from non-retrieval trials between 950 and 1050 ms post-stimulus negatively correlated with the performance on the logical memory subtest of the Wechsler Memory Scale-III. Thus, individuals with aMCI were not only impaired in their behavioral performance on SORT relative to controls, but also displayed alteration in the corresponding ERPs. The altered neural activity in aMCI compared to controls suggests a more sustained and effortful search during object memory retrieval, which may be a potential marker indicating disease processes at the pre-dementia stage.
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SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.
Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan
2013-04-30
The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.
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Validation of defibrillator lead performance registry data
DEFF Research Database (Denmark)
Kristensen, Anders Elgaard; Larsen, Jacob Moesgaard; Nielsen, Jens Cosedis
2017-01-01
all reported surgical interventions due to defibrillator lead events in the Danish Pacemaker and ICD Register (DPIR) from 2000 to 2013. Medical records of all patients (n = 753) were examined blinded for 5 predefined intervention types and 18 reasons for lead intervention. The overall level...
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Address-event-based platform for bioinspired spiking systems
Jiménez-Fernández, A.; Luján, C. D.; Linares-Barranco, A.; Gómez-Rodríguez, F.; Rivas, M.; Jiménez, G.; Civit, A.
2007-05-01
Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows a real-time virtual massive connectivity between huge number neurons, located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate "events" according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems, it is absolutely necessary to have a computer interface that allows (a) reading AER interchip traffic into the computer and visualizing it on the screen, and (b) converting conventional frame-based video stream in the computer into AER and injecting it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. In the other hand, the use of a commercial personal computer implies to depend on software tools and operating systems that can make the system slower and un-robust. This paper addresses the problem of communicating several AER based chips to compose a powerful processing system. The problem was discussed in the Neuromorphic Engineering Workshop of 2006. The platform is based basically on an embedded computer, a powerful FPGA and serial links, to make the system faster and be stand alone (independent from a PC). A new platform is presented that allow to connect up to eight AER based chips to a Spartan 3 4000 FPGA. The FPGA is responsible of the network communication based in Address-Event and, at the same time, to map and transform the address space of the traffic to implement a pre-processing. A MMU microprocessor (Intel XScale 400MHz Gumstix Connex computer) is also connected to the FPGA
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The ATLAS collaboration
2014-01-01
This note presents the measurement of a $t$-channel single top-quark production fiducial cross-section in the lepton+jets channel with 20.3 fb$^{-1}$ of 8 TeV data using a neural-network discriminant. Events are selected by requiring exactly two jets, where one of the jets is required to be $b$-tagged. Signal events from $t$-channel single top-quark processes are enhanced using a neural-network discriminant based on final-state observables. The $t$-channel production cross-section in the fiducial region is obtained from a binned maximum-likelihood fit to the neural-network discriminant. A fiducial cross-section quoted within the detector acceptance of $\\sigma_{\\rm fid} = 3.37 \\pm 0.05 \\, (\\mathrm{stat.}) \\pm 0.47 \\, (\\mathrm{syst.}) \\pm 0.09 \\, (\\mathrm{lumi.})~\\text{pb}$ is obtained. The total inclusive $t$-channel cross-section is calculated using the acceptance predicted by various Monte Carlo generators. If the acceptance from the aMC@NLO+Herwig event generator is used, a value of $\\sigma_t = 82.6 \\pm 1.2...
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An Ensemble of Neural Networks for Online Electron Filtering at the ATLAS Experiment.
Da Fonseca Pinto, Joao Victor; The ATLAS collaboration
2018-01-01
In 2017 the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to improving the performance of filtering events containing electrons in the high-input rate online environment of the Large Hadron Collider at CERN, Geneva. The ensemble employs a concept of calorimetry rings. The training procedure and final structure of the ensemble are used to minimize fluctuations from detector response, according to the particle energy and position of incidence. A detailed study was carried out to assess profile distortions in crucial offline quantities through the usage of statistical tests and residual analysis. These details and the online performance of this algorithm during the 2017 data-taking will be presented.
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The Neural Correlates Underlying Belief Reasoning for Self and for Others: Evidence from ERPs.
Jiang, Qin; Wang, Qi; Li, Peng; Li, Hong
2016-01-01
Belief reasoning is typical mental state reasoning in theory of mind (ToM). Although previous studies have explored the neural bases of belief reasoning, the neural correlates of belief reasoning for self and for others are rarely addressed. The decoupling mechanism of distinguishing the mental state of others from one's own is essential for ToM processing. To address the electrophysiological bases underlying the decoupling mechanism, the present event-related potential study compared the time course of neural activities associated with belief reasoning for self and for others when the belief belonging to self was consistent or inconsistent with others. Results showed that during a 450-600 ms period, belief reasoning for self elicited a larger late positive component (LPC) than for others when beliefs were inconsistent with each other. The LPC divergence is assumed to reflect the categorization of agencies in ToM processes.
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Three-dimensional hydrogel cell culture systems for modeling neural tissue
Frampton, John
Two-dimensional (2-D) neural cell culture systems have served as physiological models for understanding the cellular and molecular events that underlie responses to physical and chemical stimuli, control sensory and motor function, and lead to the development of neurological diseases. However, the development of three-dimensional (3-D) cell culture systems will be essential for the advancement of experimental research in a variety of fields including tissue engineering, chemical transport and delivery, cell growth, and cell-cell communication. In 3-D cell culture, cells are provided with an environment similar to tissue, in which they are surrounded on all sides by other cells, structural molecules and adhesion ligands. Cells grown in 3-D culture systems display morphologies and functions more similar to those observed in vivo, and can be cultured in such a way as to recapitulate the structural organization and biological properties of tissue. This thesis describes a hydrogel-based culture system, capable of supporting the growth and function of several neural cell types in 3-D. Alginate hydrogels were characterized in terms of their biomechanical and biochemical properties and were functionalized by covalent attachment of whole proteins and peptide epitopes. Methods were developed for rapid cross-linking of alginate hydrogels, thus permitting the incorporation of cells into 3-D scaffolds without adversely affecting cell viability or function. A variety of neural cell types were tested including astrocytes, microglia, and neurons. Cells remained viable and functional for longer than two weeks in culture and displayed process outgrowth in 3-D. Cell constructs were created that varied in cell density, type and organization, providing experimental flexibility for studying cell interactions and behavior. In one set of experiments, 3-D glial-endothelial cell co-cultures were used to model blood-brain barrier (BBB) structure and function. This co-culture system was
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Neural mechanisms of reactivation-induced updating that enhance and distort memory
St. Jacques, Peggy L.; Olm, Christopher; Schacter, Daniel L.
2013-01-01
We remember a considerable number of personal experiences because we are frequently reminded of them, a process known as memory reactivation. Although memory reactivation helps to stabilize and update memories, reactivation may also introduce distortions if novel information becomes incorporated with memory. Here we used functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms mediating reactivation-induced updating in memory for events experienced during a museum tou...
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Directory of Open Access Journals (Sweden)
Xiaopu Zhang
2018-06-01
Full Text Available Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR. The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN and long short-term memory (LSTM is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96% with less transmitted data (about 90% was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.
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Zhang, Xiaopu; Lin, Jun; Chen, Zubin; Sun, Feng; Zhu, Xi; Fang, Gengfa
2018-06-05
Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.
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Sensitivity studies on the approaches for addressing multiple initiating events in fire events PSA
Energy Technology Data Exchange (ETDEWEB)
Kang, Dae Il; Lim, Ho Gon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2016-10-15
A single fire event within a fire compartment or a fire scenario can cause multiple initiating events (IEs). As an example, a fire in a turbine building fire area can cause a loss of the main feed-water (LOMF) and loss of off-site power (LOOP) IEs. Previous domestic fire events PSA had considered only the most severe initiating event among multiple initiating events. NUREG/CR-6850 and ANS/ASME PRA Standard require that multiple IEs are to be addressed in fire events PSA. In this paper, sensitivity studies on the approaches for addressing multiple IEs in fire events PSA for Hanul Unit 3 were performed and their results were presented. In this paper, sensitivity studies on the approaches for addressing multiple IEs in fire events PSA are performed and their results were presented. From the sensitivity analysis results, we can find that the incorporations of multiple IEs into fire events PSA model result in the core damage frequency (CDF) increase and may lead to the generation of the duplicate cutsets. Multiple IEs also can occur at internal flooding event or other external events such as seismic event. They should be considered in the constructions of PSA models in order to realistically estimate risk due to flooding or seismic events.
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Embryonic cell-cell adhesion: a key player in collective neural crest migration.
Barriga, Elias H; Mayor, Roberto
2015-01-01
Cell migration is essential for morphogenesis, adult tissue remodeling, wound healing, and cancer cell migration. Cells can migrate as individuals or groups. When cells migrate in groups, cell-cell interactions are crucial in order to promote the coordinated behavior, essential for collective migration. Interestingly, recent evidence has shown that cell-cell interactions are also important for establishing and maintaining the directionality of these migratory events. We focus on neural crest cells, as they possess extraordinary migratory capabilities that allow them to migrate and colonize tissues all over the embryo. Neural crest cells undergo an epithelial-to-mesenchymal transition at the same time than perform directional collective migration. Cell-cell adhesion has been shown to be an important source of planar cell polarity and cell coordination during collective movement. We also review molecular mechanisms underlying cadherin turnover, showing how the modulation and dynamics of cell-cell adhesions are crucial in order to maintain tissue integrity and collective migration in vivo. We conclude that cell-cell adhesion during embryo development cannot be considered as simple passive resistance to force, but rather participates in signaling events that determine important cell behaviors required for cell migration. © 2015 Elsevier Inc. All rights reserved.
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Emotionally anesthetized: media violence induces neural changes during emotional face processing.
Stockdale, Laura A; Morrison, Robert G; Kmiecik, Matthew J; Garbarino, James; Silton, Rebecca L
2015-10-01
Media violence exposure causes increased aggression and decreased prosocial behavior, suggesting that media violence desensitizes people to the emotional experience of others. Alterations in emotional face processing following exposure to media violence may result in desensitization to others' emotional states. This study used scalp electroencephalography methods to examine the link between exposure to violence and neural changes associated with emotional face processing. Twenty-five participants were shown a violent or nonviolent film clip and then completed a gender discrimination stop-signal task using emotional faces. Media violence did not affect the early visual P100 component; however, decreased amplitude was observed in the N170 and P200 event-related potentials following the violent film, indicating that exposure to film violence leads to suppression of holistic face processing and implicit emotional processing. Participants who had just seen a violent film showed increased frontal N200/P300 amplitude. These results suggest that media violence exposure may desensitize people to emotional stimuli and thereby require fewer cognitive resources to inhibit behavior. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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Inattentional Deafness: Visual Load Leads to Time-Specific Suppression of Auditory Evoked Responses.
Molloy, Katharine; Griffiths, Timothy D; Chait, Maria; Lavie, Nilli
2015-12-09
Due to capacity limits on perception, conditions of high perceptual load lead to reduced processing of unattended stimuli (Lavie et al., 2014). Accumulating work demonstrates the effects of visual perceptual load on visual cortex responses, but the effects on auditory processing remain poorly understood. Here we establish the neural mechanisms underlying "inattentional deafness"--the failure to perceive auditory stimuli under high visual perceptual load. Participants performed a visual search task of low (target dissimilar to nontarget items) or high (target similar to nontarget items) load. On a random subset (50%) of trials, irrelevant tones were presented concurrently with the visual stimuli. Brain activity was recorded with magnetoencephalography, and time-locked responses to the visual search array and to the incidental presence of unattended tones were assessed. High, compared to low, perceptual load led to increased early visual evoked responses (within 100 ms from onset). This was accompanied by reduced early (∼ 100 ms from tone onset) auditory evoked activity in superior temporal sulcus and posterior middle temporal gyrus. A later suppression of the P3 "awareness" response to the tones was also observed under high load. A behavioral experiment revealed reduced tone detection sensitivity under high visual load, indicating that the reduction in neural responses was indeed associated with reduced awareness of the sounds. These findings support a neural account of shared audiovisual resources, which, when depleted under load, leads to failures of sensory perception and awareness. The present work clarifies the neural underpinning of inattentional deafness under high visual load. The findings of near-simultaneous load effects on both visual and auditory evoked responses suggest shared audiovisual processing capacity. Temporary depletion of shared capacity in perceptually demanding visual tasks leads to a momentary reduction in sensory processing of auditory