WorldWideScience

Sample records for large-scale neural interactions

  1. Large-scale multielectrode recording and stimulation of neural activity

    International Nuclear Information System (INIS)

    Sher, A.; Chichilnisky, E.J.; Dabrowski, W.; Grillo, A.A.; Grivich, M.; Gunning, D.; Hottowy, P.; Kachiguine, S.; Litke, A.M.; Mathieson, K.; Petrusca, D.

    2007-01-01

    Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

  2. Learning from large scale neural simulations

    DEFF Research Database (Denmark)

    Serban, Maria

    2017-01-01

    Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

  3. Large-scale simulations of plastic neural networks on neuromorphic hardware

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-04-01

    Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

  4. Ssecrett and neuroTrace: Interactive visualization and analysis tools for large-scale neuroscience data sets

    KAUST Repository

    Jeong, Wonki; Beyer, Johanna; Hadwiger, Markus; Blue, Rusty; Law, Charles; Vá zquez Reina, Amelio; Reid, Rollie Clay; Lichtman, Jeff W M D; Pfister, Hanspeter

    2010-01-01

    Recent advances in optical and electron microscopy let scientists acquire extremely high-resolution images for neuroscience research. Data sets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible, interactive tools, the authors introduce Ssecrett and NeuroTrace, two tools they designed for interactive exploration and analysis of large-scale optical- and electron-microscopy images to reconstruct complex neural circuits of the mammalian nervous system. © 2010 IEEE.

  5. Ssecrett and neuroTrace: Interactive visualization and analysis tools for large-scale neuroscience data sets

    KAUST Repository

    Jeong, Wonki

    2010-05-01

    Recent advances in optical and electron microscopy let scientists acquire extremely high-resolution images for neuroscience research. Data sets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible, interactive tools, the authors introduce Ssecrett and NeuroTrace, two tools they designed for interactive exploration and analysis of large-scale optical- and electron-microscopy images to reconstruct complex neural circuits of the mammalian nervous system. © 2010 IEEE.

  6. Visual attention mitigates information loss in small- and large-scale neural codes

    Science.gov (United States)

    Sprague, Thomas C; Saproo, Sameer; Serences, John T

    2015-01-01

    Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502

  7. Visual attention mitigates information loss in small- and large-scale neural codes.

    Science.gov (United States)

    Sprague, Thomas C; Saproo, Sameer; Serences, John T

    2015-04-01

    The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. SCALE INTERACTION IN A MIXING LAYER. THE ROLE OF THE LARGE-SCALE GRADIENTS

    KAUST Repository

    Fiscaletti, Daniele

    2015-08-23

    The interaction between scales is investigated in a turbulent mixing layer. The large-scale amplitude modulation of the small scales already observed in other works depends on the crosswise location. Large-scale positive fluctuations correlate with a stronger activity of the small scales on the low speed-side of the mixing layer, and a reduced activity on the high speed-side. However, from physical considerations we would expect the scales to interact in a qualitatively similar way within the flow and across different turbulent flows. Therefore, instead of the large-scale fluctuations, the large-scale gradients modulation of the small scales has been additionally investigated.

  9. Streaming Parallel GPU Acceleration of Large-Scale filter-based Spiking Neural Networks

    NARCIS (Netherlands)

    L.P. Slazynski (Leszek); S.M. Bohte (Sander)

    2012-01-01

    htmlabstractThe arrival of graphics processing (GPU) cards suitable for massively parallel computing promises a↵ordable large-scale neural network simulation previously only available at supercomputing facil- ities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of

  10. Techniques for extracting single-trial activity patterns from large-scale neural recordings

    Science.gov (United States)

    Churchland, Mark M; Yu, Byron M; Sahani, Maneesh; Shenoy, Krishna V

    2008-01-01

    Summary Large, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies – some employing simultaneous recording, some not – indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior. PMID:18093826

  11. SCALE INTERACTION IN A MIXING LAYER. THE ROLE OF THE LARGE-SCALE GRADIENTS

    KAUST Repository

    Fiscaletti, Daniele; Attili, Antonio; Bisetti, Fabrizio; Elsinga, Gerrit E.

    2015-01-01

    from physical considerations we would expect the scales to interact in a qualitatively similar way within the flow and across different turbulent flows. Therefore, instead of the large-scale fluctuations, the large-scale gradients modulation of the small scales has been additionally investigated.

  12. Large-scale information flow in conscious and unconscious states: an ECoG study in monkeys.

    Directory of Open Access Journals (Sweden)

    Toru Yanagawa

    Full Text Available Consciousness is an emergent property of the complex brain network. In order to understand how consciousness is constructed, neural interactions within this network must be elucidated. Previous studies have shown that specific neural interactions between the thalamus and frontoparietal cortices; frontal and parietal cortices; and parietal and temporal cortices are correlated with levels of consciousness. However, due to technical limitations, the network underlying consciousness has not been investigated in terms of large-scale interactions with high temporal and spectral resolution. In this study, we recorded neural activity with dense electrocorticogram (ECoG arrays and used the spectral Granger causality to generate a more comprehensive network that relates to consciousness in monkeys. We found that neural interactions were significantly different between conscious and unconscious states in all combinations of cortical region pairs. Furthermore, the difference in neural interactions between conscious and unconscious states could be represented in 4 frequency-specific large-scale networks with unique interaction patterns: 2 networks were related to consciousness and showed peaks in alpha and beta bands, while the other 2 networks were related to unconsciousness and showed peaks in theta and gamma bands. Moreover, networks in the unconscious state were shared amongst 3 different unconscious conditions, which were induced either by ketamine and medetomidine, propofol, or sleep. Our results provide a novel picture that the difference between conscious and unconscious states is characterized by a switch in frequency-specific modes of large-scale communications across the entire cortex, rather than the cessation of interactions between specific cortical regions.

  13. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    Science.gov (United States)

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  14. Thermal interaction in crusted melt jets with large-scale structures

    Energy Technology Data Exchange (ETDEWEB)

    Sugiyama, Ken-ichiro; Sotome, Fuminori; Ishikawa, Michio [Hokkaido Univ., Sapporo (Japan). Faculty of Engineering

    1998-01-01

    The objective of the present study is to experimentally observe thermal interaction which would be capable of triggering due to entrainment, or entrapment in crusted melt jets with `large-scale structure`. The present experiment was carried out by dropping molten zinc and molten tin of 100 grams, of which mass was sufficient to generate large-scale structures of melt jets. The experimental results show that the thermal interaction of entrapment type occurs in molten-zinc jets with rare probability, and the thermal interaction of entrainment type occurs in molten tin jets with high probability. The difference of thermal interaction between molten zinc and molten tin may attribute to differences of kinematic viscosity and melting point between them. (author)

  15. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    Science.gov (United States)

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

  16. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    Directory of Open Access Journals (Sweden)

    Runchun Mark Wang

    2015-05-01

    Full Text Available We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP and Spike Timing Dependent Delay Plasticity (STDDP. We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2^26 (64M synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted and/or delayed pre-synaptic spike to the target synapse in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2^36 (64G synaptic adaptors on a current high-end FPGA platform.

  17. Scale interactions in a mixing layer – the role of the large-scale gradients

    KAUST Repository

    Fiscaletti, D.

    2016-02-15

    © 2016 Cambridge University Press. The interaction between the large and the small scales of turbulence is investigated in a mixing layer, at a Reynolds number based on the Taylor microscale of , via direct numerical simulations. The analysis is performed in physical space, and the local vorticity root-mean-square (r.m.s.) is taken as a measure of the small-scale activity. It is found that positive large-scale velocity fluctuations correspond to large vorticity r.m.s. on the low-speed side of the mixing layer, whereas, they correspond to low vorticity r.m.s. on the high-speed side. The relationship between large and small scales thus depends on position if the vorticity r.m.s. is correlated with the large-scale velocity fluctuations. On the contrary, the correlation coefficient is nearly constant throughout the mixing layer and close to unity if the vorticity r.m.s. is correlated with the large-scale velocity gradients. Therefore, the small-scale activity appears closely related to large-scale gradients, while the correlation between the small-scale activity and the large-scale velocity fluctuations is shown to reflect a property of the large scales. Furthermore, the vorticity from unfiltered (small scales) and from low pass filtered (large scales) velocity fields tend to be aligned when examined within vortical tubes. These results provide evidence for the so-called \\'scale invariance\\' (Meneveau & Katz, Annu. Rev. Fluid Mech., vol. 32, 2000, pp. 1-32), and suggest that some of the large-scale characteristics are not lost at the small scales, at least at the Reynolds number achieved in the present simulation.

  18. COMBINED EFFECTS OF GALAXY INTERACTIONS AND LARGE-SCALE ENVIRONMENT ON GALAXY PROPERTIES

    International Nuclear Information System (INIS)

    Park, Changbom; Choi, Yun-Young

    2009-01-01

    We inspect the coupled dependence of physical parameters of the Sloan Digital Sky Survey galaxies on the small-scale (distance to and morphology of the nearest neighbor galaxy) and the large-scale (background density smoothed over 20 nearby galaxies) environments. The impacts of interaction on galaxy properties are detected at least out to the neighbor separation corresponding to the virial radius of galaxies, which is typically between 200 and 400 h -1 kpc for the galaxies in our sample. To detect these long-range interaction effects, it is crucial to divide galaxy interactions into four cases dividing the morphology of target and neighbor galaxies into early and late types. We show that there are two characteristic neighbor-separation scales where the galaxy interactions cause abrupt changes in the properties of galaxies. The first scale is the virial radius of the nearest neighbor galaxy r vir,nei . Many physical parameters start to deviate from those of extremely isolated galaxies at the projected neighbor separation r p of about r vir,nei . The second scale is at r p ∼ 0.05r vir,nei = 10-20 h -1 kpc, and is the scale at which the galaxies in pairs start to merge. We find that late-type neighbors enhance the star formation activity of galaxies while early-type neighbors reduce it, and that these effects occur within r vir,nei . The hot halo gas and cold disk gas must be participating in the interactions at separations less than the virial radius of the galaxy plus dark halo system. Our results also show that the role of the large-scale density in determining galaxy properties is minimal once luminosity and morphology are fixed. We propose that the weak residual dependence of galaxy properties on the large-scale density is due to the dependence of the halo gas property on the large-scale density.

  19. PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

    Directory of Open Access Journals (Sweden)

    Di Jin

    2017-07-01

    Full Text Available Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is ‘important’? How can multiple researchers explore a new graph dataset collectively and “help” each other with their findings? In this article, we present Perseus-Hub, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. Perseus-Hub uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In Perseus-Hub, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution marked by other users or experts. Moreover, Perseus-Hub guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks.

  20. Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

    Directory of Open Access Journals (Sweden)

    Mohsen Alavash

    2017-06-01

    Full Text Available Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations. The speed at which we make perceptual decisions varies. This translation of sensory information into perceptual decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural-network embodiment supporting perceptual decision-making is unclear. We addressed this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, we traced the large-scale network

  1. GPU-accelerated brain connectivity reconstruction and visualization in large-scale electron micrographs

    KAUST Repository

    Jeong, Wonki

    2011-01-01

    This chapter introduces a GPU-accelerated interactive, semiautomatic axon segmentation and visualization system. Two challenging problems have been addressed: the interactive 3D axon segmentation and the interactive 3D image filtering and rendering of implicit surfaces. The reconstruction of neural connections to understand the function of the brain is an emerging and active research area in neuroscience. With the advent of high-resolution scanning technologies, such as 3D light microscopy and electron microscopy (EM), reconstruction of complex 3D neural circuits from large volumes of neural tissues has become feasible. Among them, only EM data can provide sufficient resolution to identify synapses and to resolve extremely narrow neural processes. These high-resolution, large-scale datasets pose challenging problems, for example, how to process and manipulate large datasets to extract scientifically meaningful information using a compact representation in a reasonable processing time. The running time of the multiphase level set segmentation method has been measured on the CPU and GPU. The CPU version is implemented using the ITK image class and the ITK distance transform filter. The numerical part of the CPU implementation is similar to the GPU implementation for fair comparison. The main focus of this chapter is introducing the GPU algorithms and their implementation details, which are the core components of the interactive segmentation and visualization system. © 2011 Copyright © 2011 NVIDIA Corporation and Wen-mei W. Hwu Published by Elsevier Inc. All rights reserved..

  2. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  3. BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

    Science.gov (United States)

    Schweppe, Devin K; Huttlin, Edward L; Harper, J Wade; Gygi, Steven P

    2018-01-05

    The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.

  4. Event management for large scale event-driven digital hardware spiking neural networks.

    Science.gov (United States)

    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.

  5. Simultaneous neural and movement recording in large-scale immersive virtual environments.

    Science.gov (United States)

    Snider, Joseph; Plank, Markus; Lee, Dongpyo; Poizner, Howard

    2013-10-01

    Virtual reality (VR) allows precise control and manipulation of rich, dynamic stimuli that, when coupled with on-line motion capture and neural monitoring, can provide a powerful means both of understanding brain behavioral relations in the high dimensional world and of assessing and treating a variety of neural disorders. Here we present a system that combines state-of-the-art, fully immersive, 3D, multi-modal VR with temporally aligned electroencephalographic (EEG) recordings. The VR system is dynamic and interactive across visual, auditory, and haptic interactions, providing sight, sound, touch, and force. Crucially, it does so with simultaneous EEG recordings while subjects actively move about a 20 × 20 ft² space. The overall end-to-end latency between real movement and its simulated movement in the VR is approximately 40 ms. Spatial precision of the various devices is on the order of millimeters. The temporal alignment with the neural recordings is accurate to within approximately 1 ms. This powerful combination of systems opens up a new window into brain-behavioral relations and a new means of assessment and rehabilitation of individuals with motor and other disorders.

  6. Large-scale generation of human iPSC-derived neural stem cells/early neural progenitor cells and their neuronal differentiation.

    Science.gov (United States)

    D'Aiuto, Leonardo; Zhi, Yun; Kumar Das, Dhanjit; Wilcox, Madeleine R; Johnson, Jon W; McClain, Lora; MacDonald, Matthew L; Di Maio, Roberto; Schurdak, Mark E; Piazza, Paolo; Viggiano, Luigi; Sweet, Robert; Kinchington, Paul R; Bhattacharjee, Ayantika G; Yolken, Robert; Nimgaonka, Vishwajit L; Nimgaonkar, Vishwajit L

    2014-01-01

    Induced pluripotent stem cell (iPSC)-based technologies offer an unprecedented opportunity to perform high-throughput screening of novel drugs for neurological and neurodegenerative diseases. Such screenings require a robust and scalable method for generating large numbers of mature, differentiated neuronal cells. Currently available methods based on differentiation of embryoid bodies (EBs) or directed differentiation of adherent culture systems are either expensive or are not scalable. We developed a protocol for large-scale generation of neuronal stem cells (NSCs)/early neural progenitor cells (eNPCs) and their differentiation into neurons. Our scalable protocol allows robust and cost-effective generation of NSCs/eNPCs from iPSCs. Following culture in neurobasal medium supplemented with B27 and BDNF, NSCs/eNPCs differentiate predominantly into vesicular glutamate transporter 1 (VGLUT1) positive neurons. Targeted mass spectrometry analysis demonstrates that iPSC-derived neurons express ligand-gated channels and other synaptic proteins and whole-cell patch-clamp experiments indicate that these channels are functional. The robust and cost-effective differentiation protocol described here for large-scale generation of NSCs/eNPCs and their differentiation into neurons paves the way for automated high-throughput screening of drugs for neurological and neurodegenerative diseases.

  7. An interactive display system for large-scale 3D models

    Science.gov (United States)

    Liu, Zijian; Sun, Kun; Tao, Wenbing; Liu, Liman

    2018-04-01

    With the improvement of 3D reconstruction theory and the rapid development of computer hardware technology, the reconstructed 3D models are enlarging in scale and increasing in complexity. Models with tens of thousands of 3D points or triangular meshes are common in practical applications. Due to storage and computing power limitation, it is difficult to achieve real-time display and interaction with large scale 3D models for some common 3D display software, such as MeshLab. In this paper, we propose a display system for large-scale 3D scene models. We construct the LOD (Levels of Detail) model of the reconstructed 3D scene in advance, and then use an out-of-core view-dependent multi-resolution rendering scheme to realize the real-time display of the large-scale 3D model. With the proposed method, our display system is able to render in real time while roaming in the reconstructed scene and 3D camera poses can also be displayed. Furthermore, the memory consumption can be significantly decreased via internal and external memory exchange mechanism, so that it is possible to display a large scale reconstructed scene with over millions of 3D points or triangular meshes in a regular PC with only 4GB RAM.

  8. Large grazers modify effects of aboveground-belowground interactions on small-scale plant community composition

    NARCIS (Netherlands)

    Veen, G. F. (Ciska); Geuverink, Elzemiek; Olff, Han; Schmid, Bernhard

    Aboveground and belowground organisms influence plant community composition by local interactions, and their scale of impact may vary from millimeters belowground to kilometers aboveground. However, it still poorly understood how large grazers that select their forage on large spatial scales

  9. Large grazers modify effects of aboveground–belowground interactions on small-scale plant community composition

    NARCIS (Netherlands)

    Veen, G.F.; Geuverink, E.; Olff, H.

    2012-01-01

    Aboveground and belowground organisms influence plant community composition by local interactions, and their scale of impact may vary from millimeters belowground to kilometers aboveground. However, it still poorly understood how large grazers that select their forage on large spatial scales

  10. A large-scale soil-structure interaction experiment: Design and construction

    International Nuclear Information System (INIS)

    Tang, H.T.; Tang, Y.K.; Stepp, J.C.; Wall, I.B.; Lin, E.; Cheng, S.C.; Lee, S.K.

    1989-01-01

    This paper describes the design and construction phase of the Large-Scale Soil-Structure Interaction Experiment project jointly sponsored by EPRI and Taipower. The project has two objectives: 1. to obtain an earthquake database which can be used to substantiate soil-structure interaction (SSI) models and analysis methods; and 2. to quantify nuclear power plant reactor containment and internal components seismic margin based on earthquake experience data. These objectives were accomplished by recording and analyzing data from two instrumented, scaled down, reinforced concrete containment structures during seismic events. The two model structures are sited in a high seismic region in Taiwan (SMART-1). A strong-motion seismic array network is located at the site. The containment models (1/4- and 1/12-scale) were constructed and instrumented specially for this experiment. Construction was completed and data recording began in September 1985. By November 1986, 18 strong motion earthquakes ranging from Richter magnitude 4.5 to 7.0 were recorded. (orig./HP)

  11. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

    Science.gov (United States)

    Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

    2015-01-01

    Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  12. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2015-01-01

    Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  13. Large-scale ab initio configuration interaction calculations for light nuclei

    International Nuclear Information System (INIS)

    Maris, Pieter; Potter, Hugh; Vary, James P; Aktulga, H Metin; Ng, Esmond G; Yang Chao; Caprio, Mark A; Çatalyürek, Ümit V; Saule, Erik; Oryspayev, Dossay; Sosonkina, Masha; Zhou Zheng

    2012-01-01

    In ab-initio Configuration Interaction calculations, the nuclear wavefunction is expanded in Slater determinants of single-nucleon wavefunctions and the many-body Schrodinger equation becomes a large sparse matrix problem. The challenge is to reach numerical convergence to within quantified numerical uncertainties for physical observables using finite truncations of the infinite-dimensional basis space. We discuss strategies for constructing and solving the resulting large sparse matrix eigenvalue problems on current multicore computer architectures. Several of these strategies have been implemented in the code MFDn, a hybrid MPI/OpenMP Fortran code for ab-initio nuclear structure calculations that can scale to 100,000 cores and more. Finally, we will conclude with some recent results for 12 C including emerging collective phenomena such as rotational band structures using SRG evolved chiral N3LO interactions.

  14. A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

    Directory of Open Access Journals (Sweden)

    Roger V Hoang

    2013-10-01

    Full Text Available Computational Neuroscience is an emerging field that provides unique opportunities to studycomplex brain structures through realistic neural simulations. However, as biological details are added tomodels, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they haveshown significant improvement in execution time compared to Central Processing Units (CPUs. Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks,the NeoCortical Simulator version 6 (NCS6. NCS6 is a free, open-source, parallelizable, and scalable simula-tor, designed to run on clusters of multiple machines, potentially with high performance computing devicesin each of them. It has built-in leaky-integrate-and-fire (LIF and Izhikevich (IZH neuron models, but usersalso have the capability to design their own plug-in interface for different neuron types as desired. NCS6is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing dataacross these heterogeneous clusters of CPUs and GPUs.

  15. Large-scale seismic test for soil-structure interaction research in Hualien, Taiwan

    International Nuclear Information System (INIS)

    Ueshima, T.; Kokusho, T.; Okamoto, T.

    1995-01-01

    It is important to evaluate dynamic soil-structure interaction more accurately in the aseismic design of important facilities such as nuclear power plants. A large-scale model structure with about 1/4th of commercial nuclear power plants was constructed on the gravelly layers in seismically active Hualien, Taiwan. This international joint project is called 'the Hualien LSST Project', where 'LSST' is short for Large-Scale Seismic Test. In this paper, research tasks and responsibilities, the process of the construction work and research tasks along the time-line, main results obtained up to now, and so on in this Project are described. (J.P.N.)

  16. "Annotated Lectures": Student-Instructor Interaction in Large-Scale Global Education

    Directory of Open Access Journals (Sweden)

    Roger Diehl

    2009-10-01

    Full Text Available We describe an "Annotated Lectures" system, which will be used in a global virtual teaching and student collaboration event on embodied intelligence presented by the University of Zurich. The lectures will be broadcasted via video-conference to lecture halls of different universities around the globe. Among other collaboration features, an "Annotated Lectures" system will be implemented in a 3D collaborative virtual environment and used by the participating students to make annotations to the video-recorded lectures, which will be sent to and answered by their supervisors, and forwarded to the lecturers in an aggregated way. The "Annotated Lectures" system aims to overcome the issues of limited studentinstructor interaction in large-scale education, and to foster an intercultural and multidisciplinary discourse among students who review the lectures in a group. After presenting the concept of the "Annotated Lectures" system, we discuss a prototype version including a description of the technical components and its expected benefit for large-scale global education.

  17. ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models.

    Science.gov (United States)

    Kahng, Minsuk; Andrews, Pierre Y; Kalro, Aditya; Polo Chau, Duen Horng

    2017-08-30

    While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.

  18. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

    Science.gov (United States)

    Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

    2017-04-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

  19. Precision Scaling of Neural Networks for Efficient Audio Processing

    OpenAIRE

    Ko, Jong Hwan; Fromm, Josh; Philipose, Matthai; Tashev, Ivan; Zarar, Shuayb

    2017-01-01

    While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In this paper, we study the impact of scaling the precision of neural networks on the performance of two common audio processing tasks, namely, voice-activity detection and single-channel speech enhancement. We determine the optimal pair of weight/neuron bit precision by exploring its impact on both the performance and ...

  20. Fluid-structure interaction simulation of floating structures interacting with complex, large-scale ocean waves and atmospheric turbulence with application to floating offshore wind turbines

    Science.gov (United States)

    Calderer, Antoni; Guo, Xin; Shen, Lian; Sotiropoulos, Fotis

    2018-02-01

    We develop a numerical method for simulating coupled interactions of complex floating structures with large-scale ocean waves and atmospheric turbulence. We employ an efficient large-scale model to develop offshore wind and wave environmental conditions, which are then incorporated into a high resolution two-phase flow solver with fluid-structure interaction (FSI). The large-scale wind-wave interaction model is based on a two-fluid dynamically-coupled approach that employs a high-order spectral method for simulating the water motion and a viscous solver with undulatory boundaries for the air motion. The two-phase flow FSI solver is based on the level set method and is capable of simulating the coupled dynamic interaction of arbitrarily complex bodies with airflow and waves. The large-scale wave field solver is coupled with the near-field FSI solver with a one-way coupling approach by feeding into the latter waves via a pressure-forcing method combined with the level set method. We validate the model for both simple wave trains and three-dimensional directional waves and compare the results with experimental and theoretical solutions. Finally, we demonstrate the capabilities of the new computational framework by carrying out large-eddy simulation of a floating offshore wind turbine interacting with realistic ocean wind and waves.

  1. Prioritization of gene regulatory interactions from large-scale modules in yeast

    Directory of Open Access Journals (Sweden)

    Bringas Ricardo

    2008-01-01

    Full Text Available Abstract Background The identification of groups of co-regulated genes and their transcription factors, called transcriptional modules, has been a focus of many studies about biological systems. While methods have been developed to derive numerous modules from genome-wide data, individual links between regulatory proteins and target genes still need experimental verification. In this work, we aim to prioritize regulator-target links within transcriptional modules based on three types of large-scale data sources. Results Starting with putative transcriptional modules from ChIP-chip data, we first derive modules in which target genes show both expression and function coherence. The most reliable regulatory links between transcription factors and target genes are established by identifying intersection of target genes in coherent modules for each enriched functional category. Using a combination of genome-wide yeast data in normal growth conditions and two different reference datasets, we show that our method predicts regulatory interactions with significantly higher predictive power than ChIP-chip binding data alone. A comparison with results from other studies highlights that our approach provides a reliable and complementary set of regulatory interactions. Based on our results, we can also identify functionally interacting target genes, for instance, a group of co-regulated proteins related to cell wall synthesis. Furthermore, we report novel conserved binding sites of a glycoprotein-encoding gene, CIS3, regulated by Swi6-Swi4 and Ndd1-Fkh2-Mcm1 complexes. Conclusion We provide a simple method to prioritize individual TF-gene interactions from large-scale transcriptional modules. In comparison with other published works, we predict a complementary set of regulatory interactions which yields a similar or higher prediction accuracy at the expense of sensitivity. Therefore, our method can serve as an alternative approach to prioritization for

  2. Modelling high Reynolds number wall-turbulence interactions in laboratory experiments using large-scale free-stream turbulence.

    Science.gov (United States)

    Dogan, Eda; Hearst, R Jason; Ganapathisubramani, Bharathram

    2017-03-13

    A turbulent boundary layer subjected to free-stream turbulence is investigated in order to ascertain the scale interactions that dominate the near-wall region. The results are discussed in relation to a canonical high Reynolds number turbulent boundary layer because previous studies have reported considerable similarities between these two flows. Measurements were acquired simultaneously from four hot wires mounted to a rake which was traversed through the boundary layer. Particular focus is given to two main features of both canonical high Reynolds number boundary layers and boundary layers subjected to free-stream turbulence: (i) the footprint of the large scales in the logarithmic region on the near-wall small scales, specifically the modulating interaction between these scales, and (ii) the phase difference in amplitude modulation. The potential for a turbulent boundary layer subjected to free-stream turbulence to 'simulate' high Reynolds number wall-turbulence interactions is discussed. The results of this study have encouraging implications for future investigations of the fundamental scale interactions that take place in high Reynolds number flows as it demonstrates that these can be achieved at typical laboratory scales.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'. © 2017 The Author(s).

  3. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

    Science.gov (United States)

    Gerstner, Wulfram

    2017-01-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

  4. Large scale sodium interactions. Part 1. Test facility design

    International Nuclear Information System (INIS)

    King, D.L.; Smaardyk, J.E.; Sallach, R.A.

    1977-01-01

    During the design of the test facility for large scale sodium interaction testing, an attempt was made to keep the system as simple and yet versatile as possible; therefore, a once through design was employed as opposed to any type of conventional sodium ''loop.'' The initial series of tests conducted at the facility call for rapidly dropping from 20 kg to 225 kg of sodium at temperatures from 825 0 K to 1125 0 K into concrete crucibles. The basic system layout is described. A commercial drum heater is used to melt the sodium which is in 55 gallon drums and then a slight argon pressurization is used to force the liquid sodium through a metallic filter and into a dump tank. Then the sodium dump tank is heated to the desired temperature. A diaphragm is mechanically ruptured and the sodium is dumped into a crucible that is housed inside a large steel test chamber

  5. A large-scale perspective on stress-induced alterations in resting-state networks

    Science.gov (United States)

    Maron-Katz, Adi; Vaisvaser, Sharon; Lin, Tamar; Hendler, Talma; Shamir, Ron

    2016-02-01

    Stress is known to induce large-scale neural modulations. However, its neural effect once the stressor is removed and how it relates to subjective experience are not fully understood. Here we used a statistically sound data-driven approach to investigate alterations in large-scale resting-state functional connectivity (rsFC) induced by acute social stress. We compared rsfMRI profiles of 57 healthy male subjects before and after stress induction. Using a parcellation-based univariate statistical analysis, we identified a large-scale rsFC change, involving 490 parcel-pairs. Aiming to characterize this change, we employed statistical enrichment analysis, identifying anatomic structures that were significantly interconnected by these pairs. This analysis revealed strengthening of thalamo-cortical connectivity and weakening of cross-hemispheral parieto-temporal connectivity. These alterations were further found to be associated with change in subjective stress reports. Integrating report-based information on stress sustainment 20 minutes post induction, revealed a single significant rsFC change between the right amygdala and the precuneus, which inversely correlated with the level of subjective recovery. Our study demonstrates the value of enrichment analysis for exploring large-scale network reorganization patterns, and provides new insight on stress-induced neural modulations and their relation to subjective experience.

  6. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    OpenAIRE

    S Safinaz; A V Ravi Kumar

    2017-01-01

    In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames t...

  7. Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation.

    Science.gov (United States)

    Li, Jian-Feng; Bush, Jenifer; Xiong, Yan; Li, Lei; McCormack, Matthew

    2011-01-01

    Protein-protein interactions (PPIs) constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC) as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs) and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

  8. Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation.

    Directory of Open Access Journals (Sweden)

    Jian-Feng Li

    Full Text Available Protein-protein interactions (PPIs constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

  9. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    Directory of Open Access Journals (Sweden)

    S Safinaz

    2017-08-01

    Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

  10. Abnormal binding and disruption in large scale networks involved in human partial seizures

    Directory of Open Access Journals (Sweden)

    Bartolomei Fabrice

    2013-12-01

    Full Text Available There is a marked increase in the amount of electrophysiological and neuroimaging works dealing with the study of large scale brain connectivity in the epileptic brain. Our view of the epileptogenic process in the brain has largely evolved over the last twenty years from the historical concept of “epileptic focus” to a more complex description of “Epileptogenic networks” involved in the genesis and “propagation” of epileptic activities. In particular, a large number of studies have been dedicated to the analysis of intracerebral EEG signals to characterize the dynamic of interactions between brain areas during temporal lobe seizures. These studies have reported that large scale functional connectivity is dramatically altered during seizures, particularly during temporal lobe seizure genesis and development. Dramatic changes in neural synchrony provoked by epileptic rhythms are also responsible for the production of ictal symptoms or changes in patient’s behaviour such as automatisms, emotional changes or consciousness alteration. Beside these studies dedicated to seizures, large-scale network connectivity during the interictal state has also been investigated not only to define biomarkers of epileptogenicity but also to better understand the cognitive impairments observed between seizures.

  11. Computational investigation of large-scale vortex interaction with flexible bodies

    Science.gov (United States)

    Connell, Benjamin; Yue, Dick K. P.

    2003-11-01

    The interaction of large-scale vortices with flexible bodies is examined with particular interest paid to the energy and momentum budgets of the system. Finite difference direct numerical simulation of the Navier-Stokes equations on a moving curvilinear grid is coupled with a finite difference structural solver of both a linear membrane under tension and linear Euler-Bernoulli beam. The hydrodynamics and structural dynamics are solved simultaneously using an iterative procedure with the external structural forcing calculated from the hydrodynamics at the surface and the flow-field velocity boundary condition given by the structural motion. We focus on an investigation into the canonical problem of a vortex-dipole impinging on a flexible membrane. It is discovered that the structural properties of the membrane direct the interaction in terms of the flow evolution and the energy budget. Pressure gradients associated with resonant membrane response are shown to sustain the oscillatory motion of the vortex pair. Understanding how the key mechanisms in vortex-body interactions are guided by the structural properties of the body is a prerequisite to exploiting these mechanisms.

  12. Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults.

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng

    The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small

  13. Interactive exploration of large-scale time-varying data using dynamic tracking graphs

    KAUST Repository

    Widanagamaachchi, W.

    2012-10-01

    Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the "tracks" of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science. © 2012 IEEE.

  14. Orientation Encoding and Viewpoint Invariance in Face Recognition: Inferring Neural Properties from Large-Scale Signals.

    Science.gov (United States)

    Ramírez, Fernando M

    2018-05-01

    Viewpoint-invariant face recognition is thought to be subserved by a distributed network of occipitotemporal face-selective areas that, except for the human anterior temporal lobe, have been shown to also contain face-orientation information. This review begins by highlighting the importance of bilateral symmetry for viewpoint-invariant recognition and face-orientation perception. Then, monkey electrophysiological evidence is surveyed describing key tuning properties of face-selective neurons-including neurons bimodally tuned to mirror-symmetric face-views-followed by studies combining functional magnetic resonance imaging (fMRI) and multivariate pattern analyses to probe the representation of face-orientation and identity information in humans. Altogether, neuroimaging studies suggest that face-identity is gradually disentangled from face-orientation information along the ventral visual processing stream. The evidence seems to diverge, however, regarding the prevalent form of tuning of neural populations in human face-selective areas. In this context, caveats possibly leading to erroneous inferences regarding mirror-symmetric coding are exposed, including the need to distinguish angular from Euclidean distances when interpreting multivariate pattern analyses. On this basis, this review argues that evidence from the fusiform face area is best explained by a view-sensitive code reflecting head angular disparity, consistent with a role of this area in face-orientation perception. Finally, the importance is stressed of explicit models relating neural properties to large-scale signals.

  15. Searching for signatures of dark matter-dark radiation interaction in observations of large-scale structure

    Science.gov (United States)

    Pan, Zhen; Kaplinghat, Manoj; Knox, Lloyd

    2018-05-01

    In this paper, we conduct a search in the latest large-scale structure measurements for signatures of the dark matter-dark radiation interaction proposed by Buen-Abad et al. (2015). We show that prior claims of an inference of this interaction at ˜3 σ significance rely on a use of the Sunyaev-Zeldovich cluster mass function that ignores uncertainty in the mass-observable relationship. Including this uncertainty we find that the inferred level of interaction remains consistent with the data, but so does zero interaction; i.e., there is no longer a preference for nonzero interaction. We also point out that inference of the shape and amplitude of the matter power spectrum from Ly α forest measurements is highly inconsistent with the predictions of the Λ CDM model conditioned on Planck cosmic microwave background temperature, polarization, and lensing power spectra, and that the dark matter-dark radiation model can restore that consistency. We also phenomenologically generalize the model of Buen-Abad et al. (2015) to allow for interaction rates with different scalings with temperature, and find that the original scaling is preferred by the data.

  16. Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions.

    Science.gov (United States)

    Delaforge, Elise; Milles, Sigrid; Huang, Jie-Rong; Bouvier, Denis; Jensen, Malene Ringkjøbing; Sattler, Michael; Hart, Darren J; Blackledge, Martin

    2016-01-01

    Intrinsically disordered linkers provide multi-domain proteins with degrees of conformational freedom that are often essential for function. These highly dynamic assemblies represent a significant fraction of all proteomes, and deciphering the physical basis of their interactions represents a considerable challenge. Here we describe the difficulties associated with mapping the large-scale domain dynamics and describe two recent examples where solution state methods, in particular NMR spectroscopy, are used to investigate conformational exchange on very different timescales.

  17. Large-Scale Ocean Circulation-Cloud Interactions Reduce the Pace of Transient Climate Change

    Science.gov (United States)

    Trossman, D. S.; Palter, J. B.; Merlis, T. M.; Huang, Y.; Xia, Y.

    2016-01-01

    Changes to the large scale oceanic circulation are thought to slow the pace of transient climate change due, in part, to their influence on radiative feedbacks. Here we evaluate the interactions between CO2-forced perturbations to the large-scale ocean circulation and the radiative cloud feedback in a climate model. Both the change of the ocean circulation and the radiative cloud feedback strongly influence the magnitude and spatial pattern of surface and ocean warming. Changes in the ocean circulation reduce the amount of transient global warming caused by the radiative cloud feedback by helping to maintain low cloud coverage in the face of global warming. The radiative cloud feedback is key in affecting atmospheric meridional heat transport changes and is the dominant radiative feedback mechanism that responds to ocean circulation change. Uncertainty in the simulated ocean circulation changes due to CO2 forcing may contribute a large share of the spread in the radiative cloud feedback among climate models.

  18. Spectral fingerprints of large-scale neuronal interactions

    NARCIS (Netherlands)

    Siegel, M.; Donner, T.H.; Engel, A.K.

    2012-01-01

    Cognition results from interactions among functionally specialized but widely distributed brain regions; however, neuroscience has so far largely focused on characterizing the function of individual brain regions and neurons therein. Here we discuss recent studies that have instead investigated the

  19. Dissecting the large-scale galactic conformity

    Science.gov (United States)

    Seo, Seongu

    2018-01-01

    Galactic conformity is an observed phenomenon that galaxies located in the same region have similar properties such as star formation rate, color, gas fraction, and so on. The conformity was first observed among galaxies within in the same halos (“one-halo conformity”). The one-halo conformity can be readily explained by mutual interactions among galaxies within a halo. Recent observations however further witnessed a puzzling connection among galaxies with no direct interaction. In particular, galaxies located within a sphere of ~5 Mpc radius tend to show similarities, even though the galaxies do not share common halos with each other ("two-halo conformity" or “large-scale conformity”). Using a cosmological hydrodynamic simulation, Illustris, we investigate the physical origin of the two-halo conformity and put forward two scenarios. First, back-splash galaxies are likely responsible for the large-scale conformity. They have evolved into red galaxies due to ram-pressure stripping in a given galaxy cluster and happen to reside now within a ~5 Mpc sphere. Second, galaxies in strong tidal field induced by large-scale structure also seem to give rise to the large-scale conformity. The strong tides suppress star formation in the galaxies. We discuss the importance of the large-scale conformity in the context of galaxy evolution.

  20. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

    Science.gov (United States)

    Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A

    2014-05-01

    Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

  1. Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Elise Delaforge

    2016-09-01

    Full Text Available Intrinsically disordered linkers provide multi-domain proteins with degrees of conformational freedom that are often essential for function. These highly dynamic assemblies represent a significant fraction of all proteomes, and deciphering the physical basis of their interactions represents a considerable challenge. Here we describe the difficulties associated with mapping the large-scale domain dynamics and describe two recent examples where solution state methods, in particular NMR spectroscopy, are used to investigate conformational exchange on very different timescales.

  2. Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

    KAUST Repository

    Sicat, Ronell B.

    2015-11-25

    The resolutions of acquired image and volume data are ever increasing. However, the resolutions of commodity display devices remain limited. This leads to an increasing gap between data and display resolutions. To bridge this gap, the standard approach is to employ output-sensitive operations on multi-resolution data representations. Output-sensitive operations facilitate interactive applications since their required computations are proportional only to the size of the data that is visible, i.e., the output, and not the full size of the input. Multi-resolution representations, such as image mipmaps, and volume octrees, are crucial in providing these operations direct access to any subset of the data at any resolution corresponding to the output. Despite its widespread use, this standard approach has some shortcomings in three important application areas, namely non-linear image operations, multi-resolution volume rendering, and large-scale image exploration. This dissertation presents new multi-resolution representations for large-scale images and volumes that address these shortcomings. Standard multi-resolution representations require low-pass pre-filtering for anti- aliasing. However, linear pre-filters do not commute with non-linear operations. This becomes problematic when applying non-linear operations directly to any coarse resolution levels in standard representations. Particularly, this leads to inaccurate output when applying non-linear image operations, e.g., color mapping and detail-aware filters, to multi-resolution images. Similarly, in multi-resolution volume rendering, this leads to inconsistency artifacts which manifest as erroneous differences in rendering outputs across resolution levels. To address these issues, we introduce the sparse pdf maps and sparse pdf volumes representations for large-scale images and volumes, respectively. These representations sparsely encode continuous probability density functions (pdfs) of multi-resolution pixel

  3. Perceptual Decision Making Through the Eyes of a Large-scale Neural Model of V1

    Directory of Open Access Journals (Sweden)

    Jianing eShi

    2013-04-01

    Full Text Available Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper we investigate the complementary issue of sparse decoding, for example given activity generated by a realistic mapping of the visual scene to neuronal spike trains, how do downstream neurons best utilize this representation to generate a decision. Specifically we consider both sparse (L1 regularized and non-sparse (L2 regularized linear decoding for mapping the neural dynamics of a large-scale spiking neuron model of primary visual cortex (V1 to a two alternative forced choice (2-AFC perceptual decision. We show that while both sparse and non-sparse linear decoding yield discrimination results quantitatively consistent with human psychophysics, sparse linear decoding is more efficient in terms of the number of selected informative dimension.

  4. The role of attention in the tinnitus decompensation: reinforcement of a large-scale neural decompensation measure.

    Science.gov (United States)

    Low, Yin Fen; Trenado, Carlos; Delb, Wolfgang; Corona-Strauss, Farah I; Strauss, Daniel J

    2007-01-01

    Large-scale neural correlates of the tinnitus decompensation have been identified by using wavelet phase stability criteria of single sweep sequences of auditory late responses (ALRs). The suggested measure provided an objective quantification of the tinnitus decompensation and allowed for a reliable discrimination between a group of compensated and decompensated tinnitus patients. By interpreting our results with an oscillatory tinnitus model, our synchronization stability measure of ALRs can be linked to the focus of attention on the tinnitus signal. In the following study, we examined in detail the correlates of this attentional mechanism in healthy subjects. The results support our previous findings of the phase synchronization stability measure that reflected neural correlates of the fixation of attention to the tinnitus signal. In this case, enabling the differentiation between the attended and unattended conditions. It is concluded that the wavelet phase synchronization stability of ALRs single sweeps can be used as objective tinnitus decompensation measure and can be interpreted in the framework of the Jastreboff tinnitus model and adaptive resonance theory. Our studies confirm that the synchronization stability in ALR sequences is linked to attention. This measure is not only able to serve as objective quantification of the tinnitus decompensation, but also can be applied in all online and real time neurofeedback therapeutic approach where a direct stimulus locked attention monitoring is compulsory as if it based on a single sweeps processing.

  5. Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network

    Science.gov (United States)

    Sang, Nong; Zhang, Tianxu

    1997-12-01

    Relaxation matching is one of the most relevant methods for image matching. The original relaxation matching technique using point patterns is sensitive to rotations and scale changes. We improve the original point pattern relaxation matching technique to be invariant to rotations and scale changes. A method that makes the Hopfield neural network perform this matching process is discussed. An advantage of this is that the relaxation matching process can be performed in real time with the neural network's massively parallel capability to process information. Experimental results with large simulated images demonstrate the effectiveness and feasibility of the method to perform point patten relaxation matching invariant to rotations and scale changes and the method to perform this matching by the Hopfield neural network. In addition, we show that the method presented can be tolerant to small random error.

  6. Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Haikel Alhichri

    2018-01-01

    Full Text Available This paper deals with the problem of the classification of large-scale very high-resolution (VHR remote sensing (RS images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class. Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.

  7. Partially acoustic dark matter, interacting dark radiation, and large scale structure

    Energy Technology Data Exchange (ETDEWEB)

    Chacko, Zackaria [Maryland Center for Fundamental Physics, Department of Physics, University of Maryland,Stadium Dr., College Park, MD 20742 (United States); Cui, Yanou [Maryland Center for Fundamental Physics, Department of Physics, University of Maryland,Stadium Dr., College Park, MD 20742 (United States); Department of Physics and Astronomy, University of California-Riverside,University Ave, Riverside, CA 92521 (United States); Perimeter Institute, 31 Caroline Street, North Waterloo, Ontario N2L 2Y5 (Canada); Hong, Sungwoo [Maryland Center for Fundamental Physics, Department of Physics, University of Maryland,Stadium Dr., College Park, MD 20742 (United States); Okui, Takemichi [Department of Physics, Florida State University,College Avenue, Tallahassee, FL 32306 (United States); Tsai, Yuhsinz [Maryland Center for Fundamental Physics, Department of Physics, University of Maryland,Stadium Dr., College Park, MD 20742 (United States)

    2016-12-21

    The standard paradigm of collisionless cold dark matter is in tension with measurements on large scales. In particular, the best fit values of the Hubble rate H{sub 0} and the matter density perturbation σ{sub 8} inferred from the cosmic microwave background seem inconsistent with the results from direct measurements. We show that both problems can be solved in a framework in which dark matter consists of two distinct components, a dominant component and a subdominant component. The primary component is cold and collisionless. The secondary component is also cold, but interacts strongly with dark radiation, which itself forms a tightly coupled fluid. The growth of density perturbations in the subdominant component is inhibited by dark acoustic oscillations due to its coupling to the dark radiation, solving the σ{sub 8} problem, while the presence of tightly coupled dark radiation ameliorates the H{sub 0} problem. The subdominant component of dark matter and dark radiation continue to remain in thermal equilibrium until late times, inhibiting the formation of a dark disk. We present an example of a simple model that naturally realizes this scenario in which both constituents of dark matter are thermal WIMPs. Our scenario can be tested by future stage-IV experiments designed to probe the CMB and large scale structure.

  8. Partially acoustic dark matter, interacting dark radiation, and large scale structure

    International Nuclear Information System (INIS)

    Chacko, Zackaria; Cui, Yanou; Hong, Sungwoo; Okui, Takemichi; Tsai, Yuhsinz

    2016-01-01

    The standard paradigm of collisionless cold dark matter is in tension with measurements on large scales. In particular, the best fit values of the Hubble rate H 0 and the matter density perturbation σ 8 inferred from the cosmic microwave background seem inconsistent with the results from direct measurements. We show that both problems can be solved in a framework in which dark matter consists of two distinct components, a dominant component and a subdominant component. The primary component is cold and collisionless. The secondary component is also cold, but interacts strongly with dark radiation, which itself forms a tightly coupled fluid. The growth of density perturbations in the subdominant component is inhibited by dark acoustic oscillations due to its coupling to the dark radiation, solving the σ 8 problem, while the presence of tightly coupled dark radiation ameliorates the H 0 problem. The subdominant component of dark matter and dark radiation continue to remain in thermal equilibrium until late times, inhibiting the formation of a dark disk. We present an example of a simple model that naturally realizes this scenario in which both constituents of dark matter are thermal WIMPs. Our scenario can be tested by future stage-IV experiments designed to probe the CMB and large scale structure.

  9. Causal influence in neural systems: Reconciling mechanistic-reductionist and statistical perspectives. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino & S.L. Bressler

    Science.gov (United States)

    Griffiths, John D.

    2015-12-01

    The modern understanding of the brain as a large, complex network of interacting elements is a natural consequence of the Neuron Doctrine [1,2] that has been bolstered in recent years by the tools and concepts of connectomics. In this abstracted, network-centric view, the essence of neural and cognitive function derives from the flows between network elements of activity and information - or, more generally, causal influence. The appropriate characterization of causality in neural systems, therefore, is a question at the very heart of systems neuroscience.

  10. Fast and accurate solution for the SCUC problem in large-scale power systems using adapted binary programming and enhanced dual neural network

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M.P.; Sheikh-El-Eslami, M.K.; Catalão, J.P.S.

    2014-01-01

    Highlights: • A novel hybrid method based on decomposition of SCUC into QP and BP problems is proposed. • An adapted binary programming and an enhanced dual neural network model are applied. • The proposed EDNN is exactly convergent to the global optimal solution of QP. • An AC power flow procedure is developed for including contingency/security issues. • It is suited for large-scale systems, providing both accurate and fast solutions. - Abstract: This paper presents a novel hybrid method for solving the security constrained unit commitment (SCUC) problem. The proposed formulation requires much less computation time in comparison with other methods while assuring the accuracy of the results. Furthermore, the framework provided here allows including an accurate description of warmth-dependent startup costs, valve point effects, multiple fuel costs, forbidden zones of operation, and AC load flow bounds. To solve the nonconvex problem, an adapted binary programming method and enhanced dual neural network model are utilized as optimization tools, and a procedure for AC power flow modeling is developed for including contingency/security issues, as new contributions to earlier studies. Unlike classical SCUC methods, the proposed method allows to simultaneously solve the unit commitment problem and comply with the network limits. In addition to conventional test systems, a real-world large-scale power system with 493 units has been used to fully validate the effectiveness of the novel hybrid method proposed

  11. Coupled radiative gasdynamic interaction and non-equilibrium dissociation for large-scale returned space vehicles

    International Nuclear Information System (INIS)

    Surzhikov, S.

    2012-01-01

    Graphical abstract: It has been shown that different coupled vibrational dissociation models, being applied for solving coupled radiative gasdynamic problems for large size space vehicles, exert noticeable effect on radiative heating of its surface at orbital entry on high altitudes (h ⩾ 70 km). This influence decreases with decreasing the space vehicles sizes. Figure shows translational (solid lines) and vibrational (dashed lines) temperatures in shock layer with (circle markers) and without (triangles markers) radiative-gasdynamic interaction for one trajectory point of entering space vehicle. Highlights: ► Nonequilibrium dissociation processes exert effect on radiation heating of space vehicles (SV). ► The radiation gas dynamic interaction enhances this influence. ► This influence increases with increasing the SV sizes. - Abstract: Radiative aerothermodynamics of large-scale space vehicles is considered for Earth orbital entry at zero angle of attack. Brief description of used radiative gasdynamic model of physically and chemically nonequilibrium, viscous, heat conductive and radiative gas of complex chemical composition is presented. Radiation gasdynamic (RadGD) interaction in high temperature shock layer is studied by means of numerical experiment. It is shown that radiation–gasdynamic coupling for orbital space vehicles of large size is important for high altitude part of entering trajectory. It is demonstrated that the use of different models of coupled vibrational dissociation (CVD) in conditions of RadGD interaction gives rise temperature variation in shock layer and, as a result, leads to significant variation of radiative heating of space vehicle.

  12. Neural networks within multi-core optic fibers.

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-07

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  13. Neutral Theory and Scale-Free Neural Dynamics

    Science.gov (United States)

    Martinello, Matteo; Hidalgo, Jorge; Maritan, Amos; di Santo, Serena; Plenz, Dietmar; Muñoz, Miguel A.

    2017-10-01

    Neural tissues have been consistently observed to be spontaneously active and to generate highly variable (scale-free distributed) outbursts of activity in vivo and in vitro. Understanding whether these heterogeneous patterns of activity stem from the underlying neural dynamics operating at the edge of a phase transition is a fascinating possibility, as criticality has been argued to entail many possible important functional advantages in biological computing systems. Here, we employ a well-accepted model for neural dynamics to elucidate an alternative scenario in which diverse neuronal avalanches, obeying scaling, can coexist simultaneously, even if the network operates in a regime far from the edge of any phase transition. We show that perturbations to the system state unfold dynamically according to a "neutral drift" (i.e., guided only by stochasticity) with respect to the background of endogenous spontaneous activity, and that such a neutral dynamics—akin to neutral theories of population genetics and of biogeography—implies marginal propagation of perturbations and scale-free distributed causal avalanches. We argue that causal information, not easily accessible to experiments, is essential to elucidate the nature and statistics of neural avalanches, and that neutral dynamics is likely to play an important role in the cortex functioning. We discuss the implications of these findings to design new empirical approaches to shed further light on how the brain processes and stores information.

  14. Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex.

    Science.gov (United States)

    Mejias, Jorge F; Murray, John D; Kennedy, Henry; Wang, Xiao-Jing

    2016-11-01

    Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

  15. Interactive extraction of neural structures with user-guided morphological diffusion

    KAUST Repository

    Yong Wan,; Otsuna, H.; Chi-Bin Chien,; Hansen, C.

    2012-01-01

    Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

  16. Interactive extraction of neural structures with user-guided morphological diffusion

    KAUST Repository

    Yong Wan,

    2012-10-01

    Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

  17. A large-scale soil-structure interaction experiment: Part I design and construction

    International Nuclear Information System (INIS)

    Tang, H.T.; Tang, Y.K.; Wall, I.B.; Lin, E.

    1987-01-01

    In the simulated earthquake experiments (SIMQUAKE) sponsored by EPRI, the detonation of vertical arrays of explosives propagated wave motions through the ground to the model structures. Although such a simulation can provide information about dynamic soil-structure interaction (SSI) characteristics in a strong motion environment, it lacks seismic wave scattering characteristics for studying seismic input to the soil-structure system and the effect of different kinds of wave composition to the soil-structure response. To supplement the inadequacy of the simulated earthquake SSI experiment, the Electric Power Research Institute (EPRI) and the Taiwan Power Company (Taipower) jointly sponsored a large scale SSI experiment in the field. The objectives of the experiment are: (1) to obtain actual strong motion earthquakes induced database in a soft-soil environment which will substantiate predictive and design SSI models;and (2) to assess nuclear power plant reactor containment internal components dynamic response and margins relating to actual earthquake-induced excitation. These objectives are accomplished by recording and analyzing data from two instrumented, scaled down, (1/4- and 1/12-scale) reinforced concrete containments sited in a high seismic region in Taiwan where a strong-motion seismic array network is located

  18. Forcings and feedbacks on convection in the 2010 Pakistan flood: Modeling extreme precipitation with interactive large-scale ascent

    Science.gov (United States)

    Nie, Ji; Shaevitz, Daniel A.; Sobel, Adam H.

    2016-09-01

    Extratropical extreme precipitation events are usually associated with large-scale flow disturbances, strong ascent, and large latent heat release. The causal relationships between these factors are often not obvious, however, the roles of different physical processes in producing the extreme precipitation event can be difficult to disentangle. Here we examine the large-scale forcings and convective heating feedback in the precipitation events, which caused the 2010 Pakistan flood within the Column Quasi-Geostrophic framework. A cloud-revolving model (CRM) is forced with large-scale forcings (other than large-scale vertical motion) computed from the quasi-geostrophic omega equation using input data from a reanalysis data set, and the large-scale vertical motion is diagnosed interactively with the simulated convection. Numerical results show that the positive feedback of convective heating to large-scale dynamics is essential in amplifying the precipitation intensity to the observed values. Orographic lifting is the most important dynamic forcing in both events, while differential potential vorticity advection also contributes to the triggering of the first event. Horizontal moisture advection modulates the extreme events mainly by setting the environmental humidity, which modulates the amplitude of the convection's response to the dynamic forcings. When the CRM is replaced by either a single-column model (SCM) with parameterized convection or a dry model with a reduced effective static stability, the model results show substantial discrepancies compared with reanalysis data. The reasons for these discrepancies are examined, and the implications for global models and theoretical models are discussed.

  19. Neural Signatures of Trust During Human-Automation Interactions

    Science.gov (United States)

    2016-04-01

    also automated devices such as a Global Positioning System. For instance, to provide advanced safety measures, the Transportation Safety...AFRL-AFOSR-VA-TR-2016-0160 Neural Signatures of Trust during Human- Automation Interactions Frank Krueger GEORGE MASON UNIVERSITY Final Report 04/01...SUBTITLE Neural Signatures of Trust during Human- Automation Interactions 5a. CONTRACT NUMBER FA9550-13-1-0017 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

  20. Large-scale Topographical Screen for Investigation of Physical Neural-Guidance Cues

    Science.gov (United States)

    Li, Wei; Tang, Qing Yuan; Jadhav, Amol D.; Narang, Ankit; Qian, Wei Xian; Shi, Peng; Pang, Stella W.

    2015-03-01

    A combinatorial approach was used to present primary neurons with a large library of topographical features in the form of micropatterned substrate for high-throughput screening of physical neural-guidance cues that can effectively promote different aspects of neuronal development, including axon and dendritic outgrowth. Notably, the neuronal-guidance capability of specific features was automatically identified using a customized image processing software, thus significantly increasing the screening throughput with minimal subjective bias. Our results indicate that the anisotropic topographies promote axonal and in some cases dendritic extension relative to the isotropic topographies, while dendritic branching showed preference to plain substrates over the microscale features. The results from this work can be readily applied towards engineering novel biomaterials with precise surface topography that can serve as guidance conduits for neuro-regenerative applications. This novel topographical screening strategy combined with the automated processing capability can also be used for high-throughput screening of chemical or genetic regulatory factors in primary neurons.

  1. Large scale electrolysers

    International Nuclear Information System (INIS)

    B Bello; M Junker

    2006-01-01

    Hydrogen production by water electrolysis represents nearly 4 % of the world hydrogen production. Future development of hydrogen vehicles will require large quantities of hydrogen. Installation of large scale hydrogen production plants will be needed. In this context, development of low cost large scale electrolysers that could use 'clean power' seems necessary. ALPHEA HYDROGEN, an European network and center of expertise on hydrogen and fuel cells, has performed for its members a study in 2005 to evaluate the potential of large scale electrolysers to produce hydrogen in the future. The different electrolysis technologies were compared. Then, a state of art of the electrolysis modules currently available was made. A review of the large scale electrolysis plants that have been installed in the world was also realized. The main projects related to large scale electrolysis were also listed. Economy of large scale electrolysers has been discussed. The influence of energy prices on the hydrogen production cost by large scale electrolysis was evaluated. (authors)

  2. Organizational Influences on Interdisciplinary Interactions during Research and Design of Large-Scale Complex Engineered Systems

    Science.gov (United States)

    McGowan, Anna-Maria R.; Seifert, Colleen M.; Papalambros, Panos Y.

    2012-01-01

    The design of large-scale complex engineered systems (LaCES) such as an aircraft is inherently interdisciplinary. Multiple engineering disciplines, drawing from a team of hundreds to thousands of engineers and scientists, are woven together throughout the research, development, and systems engineering processes to realize one system. Though research and development (R&D) is typically focused in single disciplines, the interdependencies involved in LaCES require interdisciplinary R&D efforts. This study investigates the interdisciplinary interactions that take place during the R&D and early conceptual design phases in the design of LaCES. Our theoretical framework is informed by both engineering practices and social science research on complex organizations. This paper provides preliminary perspective on some of the organizational influences on interdisciplinary interactions based on organization theory (specifically sensemaking), data from a survey of LaCES experts, and the authors experience in the research and design. The analysis reveals couplings between the engineered system and the organization that creates it. Survey respondents noted the importance of interdisciplinary interactions and their significant benefit to the engineered system, such as innovation and problem mitigation. Substantial obstacles to interdisciplinarity are uncovered beyond engineering that include communication and organizational challenges. Addressing these challenges may ultimately foster greater efficiencies in the design and development of LaCES and improved system performance by assisting with the collective integration of interdependent knowledge bases early in the R&D effort. This research suggests that organizational and human dynamics heavily influence and even constrain the engineering effort for large-scale complex systems.

  3. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  4. Neural Computations in a Dynamical System with Multiple Time Scales.

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

  5. Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

    Science.gov (United States)

    Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

    2018-02-12

    Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

  6. Large-scale instability in interacting dark energy and dark matter fluids

    International Nuclear Information System (INIS)

    Väliviita, Jussi; Majerotto, Elisabetta; Maartens, Roy

    2008-01-01

    If dark energy interacts with dark matter, this gives a new approach to the coincidence problem. But interacting dark energy models can suffer from pathologies. We consider the case where the dark energy is modelled as a fluid with constant equation of state parameter w. Non-interacting constant-w models are well behaved in the background and in the perturbed universe. But the combination of constant w and a simple interaction with dark matter leads to an instability in the dark sector perturbations at early times: the curvature perturbation blows up on super-Hubble scales. Our results underline how important it is to carefully analyse the relativistic perturbations when considering models of coupled dark energy. The instability that we find has been missed in some previous work where the perturbations were not consistently treated. The unstable mode dominates even if adiabatic initial conditions are used. The instability also arises regardless of how weak the coupling is. This non-adiabatic instability is different from previously discovered adiabatic instabilities on small scales in the strong-coupling regime

  7. Accelerating large-scale phase-field simulations with GPU

    Directory of Open Access Journals (Sweden)

    Xiaoming Shi

    2017-10-01

    Full Text Available A new package for accelerating large-scale phase-field simulations was developed by using GPU based on the semi-implicit Fourier method. The package can solve a variety of equilibrium equations with different inhomogeneity including long-range elastic, magnetostatic, and electrostatic interactions. Through using specific algorithm in Compute Unified Device Architecture (CUDA, Fourier spectral iterative perturbation method was integrated in GPU package. The Allen-Cahn equation, Cahn-Hilliard equation, and phase-field model with long-range interaction were solved based on the algorithm running on GPU respectively to test the performance of the package. From the comparison of the calculation results between the solver executed in single CPU and the one on GPU, it was found that the speed on GPU is enormously elevated to 50 times faster. The present study therefore contributes to the acceleration of large-scale phase-field simulations and provides guidance for experiments to design large-scale functional devices.

  8. Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

    Science.gov (United States)

    Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

    2016-01-01

    Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. © 2015 Wiley Periodicals, Inc.

  9. Large-scale parallel configuration interaction. II. Two- and four-component double-group general active space implementation with application to BiH

    DEFF Research Database (Denmark)

    Knecht, Stefan; Jensen, Hans Jørgen Aagaard; Fleig, Timo

    2010-01-01

    We present a parallel implementation of a large-scale relativistic double-group configuration interaction CIprogram. It is applicable with a large variety of two- and four-component Hamiltonians. The parallel algorithm is based on a distributed data model in combination with a static load balanci...

  10. Interactive effects of large- and small-scale sources of feral honey-bees for sunflower in the Argentine Pampas.

    Directory of Open Access Journals (Sweden)

    Agustín Sáez

    Full Text Available Pollinators for animal pollinated crops can be provided by natural and semi-natural habitats, ranging from large vegetation remnants to small areas of non-crop land in an otherwise highly modified landscape. It is unknown, however, how different small- and large-scale habitat patches interact as pollinator sources. In the intensively managed Argentine Pampas, we studied the additive and interactive effects of large expanses (up to 2200 ha of natural habitat, represented by untilled isolated "sierras", and narrow (3-7 m wide strips of semi-natural habitat, represented by field margins, as pollinator sources for sunflower (Helianthus annus. We estimated visitation rates by feral honey-bees, Apis mellifera, and native flower visitors (as a group at 1, 5, 25, 50 and 100 m from a field margin in 17 sunflower fields 0-10 km distant from the nearest sierra. Honey-bees dominated the pollinator assemblage accounting for >90% of all visits to sunflower inflorescences. Honey-bee visitation was strongly affected by proximity to the sierras decreasing by about 70% in the most isolated fields. There was also a decline in honey-bee visitation with distance from the field margin, which was apparent with increasing field isolation, but undetected in fields nearby large expanses of natural habitat. The probability of observing a native visitor decreased with isolation from the sierras, but in other respects visitation by flower visitors other than honey-bees was mostly unaffected by the habitat factors assessed in this study. Overall, we found strong hierarchical and interactive effects between the study large and small-scale pollinator sources. These results emphasize the importance of preserving natural habitats and managing actively field verges in the absence of large remnants of natural habitat for improving pollinator services.

  11. Interactive effects of large- and small-scale sources of feral honey-bees for sunflower in the Argentine Pampas.

    Science.gov (United States)

    Sáez, Agustín; Sabatino, Malena; Aizen, Marcelo A

    2012-01-01

    Pollinators for animal pollinated crops can be provided by natural and semi-natural habitats, ranging from large vegetation remnants to small areas of non-crop land in an otherwise highly modified landscape. It is unknown, however, how different small- and large-scale habitat patches interact as pollinator sources. In the intensively managed Argentine Pampas, we studied the additive and interactive effects of large expanses (up to 2200 ha) of natural habitat, represented by untilled isolated "sierras", and narrow (3-7 m wide) strips of semi-natural habitat, represented by field margins, as pollinator sources for sunflower (Helianthus annus). We estimated visitation rates by feral honey-bees, Apis mellifera, and native flower visitors (as a group) at 1, 5, 25, 50 and 100 m from a field margin in 17 sunflower fields 0-10 km distant from the nearest sierra. Honey-bees dominated the pollinator assemblage accounting for >90% of all visits to sunflower inflorescences. Honey-bee visitation was strongly affected by proximity to the sierras decreasing by about 70% in the most isolated fields. There was also a decline in honey-bee visitation with distance from the field margin, which was apparent with increasing field isolation, but undetected in fields nearby large expanses of natural habitat. The probability of observing a native visitor decreased with isolation from the sierras, but in other respects visitation by flower visitors other than honey-bees was mostly unaffected by the habitat factors assessed in this study. Overall, we found strong hierarchical and interactive effects between the study large and small-scale pollinator sources. These results emphasize the importance of preserving natural habitats and managing actively field verges in the absence of large remnants of natural habitat for improving pollinator services.

  12. Evolution and interaction of large interplanetary streams

    International Nuclear Information System (INIS)

    Whang, Y.C.; Burlaga, L.F.

    1985-02-01

    A computer simulation for the evolution and interaction of large interplanetary streams based on multi-spacecraft observations and an unsteady, one-dimensional MHD model is presented. Two events, each observed by two or more spacecraft separated by a distance of the order of 10 AU, were studied. The first simulation is based on the plasma and magnetic field observations made by two radially-aligned spacecraft. The second simulation is based on an event observed first by Helios-1 in May 1980 near 0.6 AU and later by Voyager-1 in June 1980 at 8.1 AU. These examples show that the dynamical evolution of large-scale solar wind structures is dominated by the shock process, including the formation, collision, and merging of shocks. The interaction of shocks with stream structures also causes a drastic decrease in the amplitude of the solar wind speed variation with increasing heliocentric distance, and as a result of interactions there is a large variation of shock-strengths and shock-speeds. The simulation results shed light on the interpretation for the interaction and evolution of large interplanetary streams. Observations were made along a few limited trajectories, but simulation results can supplement these by providing the detailed evolution process for large-scale solar wind structures in the vast region not directly observed. The use of a quantitative nonlinear simulation model including shock merging process is crucial in the interpretation of data obtained in the outer heliosphere

  13. Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

    Science.gov (United States)

    Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

    2016-11-01

    Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

  14. Neural stem cells induce bone-marrow-derived mesenchymal stem cells to generate neural stem-like cells via juxtacrine and paracrine interactions

    International Nuclear Information System (INIS)

    Alexanian, Arshak R.

    2005-01-01

    Several recent reports suggest that there is far more plasticity that previously believed in the developmental potential of bone-marrow-derived cells (BMCs) that can be induced by extracellular developmental signals of other lineages whose nature is still largely unknown. In this study, we demonstrate that bone-marrow-derived mesenchymal stem cells (MSCs) co-cultured with mouse proliferating or fixed (by paraformaldehyde or methanol) neural stem cells (NSCs) generate neural stem cell-like cells with a higher expression of Sox-2 and nestin when grown in NS-A medium supplemented with N2, NSC conditioned medium (NSCcm) and bFGF. These neurally induced MSCs eventually differentiate into β-III-tubulin and GFAP expressing cells with neuronal and glial morphology when grown an additional week in Neurobasal/B27 without bFGF. We conclude that juxtacrine interaction between NSCs and MSCs combined with soluble factors released from NSCs are important for generation of neural-like cells from bone-marrow-derived adherent MSCs

  15. The role of large-scale, extratropical dynamics in climate change

    Energy Technology Data Exchange (ETDEWEB)

    Shepherd, T.G. [ed.

    1994-02-01

    The climate modeling community has focused recently on improving our understanding of certain processes, such as cloud feedbacks and ocean circulation, that are deemed critical to climate-change prediction. Although attention to such processes is warranted, emphasis on these areas has diminished a general appreciation of the role played by the large-scale dynamics of the extratropical atmosphere. Lack of interest in extratropical dynamics may reflect the assumption that these dynamical processes are a non-problem as far as climate modeling is concerned, since general circulation models (GCMs) calculate motions on this scale from first principles. Nevertheless, serious shortcomings in our ability to understand and simulate large-scale dynamics exist. Partly due to a paucity of standard GCM diagnostic calculations of large-scale motions and their transports of heat, momentum, potential vorticity, and moisture, a comprehensive understanding of the role of large-scale dynamics in GCM climate simulations has not been developed. Uncertainties remain in our understanding and simulation of large-scale extratropical dynamics and their interaction with other climatic processes, such as cloud feedbacks, large-scale ocean circulation, moist convection, air-sea interaction and land-surface processes. To address some of these issues, the 17th Stanstead Seminar was convened at Bishop`s University in Lennoxville, Quebec. The purpose of the Seminar was to promote discussion of the role of large-scale extratropical dynamics in global climate change. Abstracts of the talks are included in this volume. On the basis of these talks, several key issues emerged concerning large-scale extratropical dynamics and their climatic role. Individual records are indexed separately for the database.

  16. The role of large-scale, extratropical dynamics in climate change

    International Nuclear Information System (INIS)

    Shepherd, T.G.

    1994-02-01

    The climate modeling community has focused recently on improving our understanding of certain processes, such as cloud feedbacks and ocean circulation, that are deemed critical to climate-change prediction. Although attention to such processes is warranted, emphasis on these areas has diminished a general appreciation of the role played by the large-scale dynamics of the extratropical atmosphere. Lack of interest in extratropical dynamics may reflect the assumption that these dynamical processes are a non-problem as far as climate modeling is concerned, since general circulation models (GCMs) calculate motions on this scale from first principles. Nevertheless, serious shortcomings in our ability to understand and simulate large-scale dynamics exist. Partly due to a paucity of standard GCM diagnostic calculations of large-scale motions and their transports of heat, momentum, potential vorticity, and moisture, a comprehensive understanding of the role of large-scale dynamics in GCM climate simulations has not been developed. Uncertainties remain in our understanding and simulation of large-scale extratropical dynamics and their interaction with other climatic processes, such as cloud feedbacks, large-scale ocean circulation, moist convection, air-sea interaction and land-surface processes. To address some of these issues, the 17th Stanstead Seminar was convened at Bishop's University in Lennoxville, Quebec. The purpose of the Seminar was to promote discussion of the role of large-scale extratropical dynamics in global climate change. Abstracts of the talks are included in this volume. On the basis of these talks, several key issues emerged concerning large-scale extratropical dynamics and their climatic role. Individual records are indexed separately for the database

  17. Large deep neural networks for MS lesion segmentation

    Science.gov (United States)

    Prieto, Juan C.; Cavallari, Michele; Palotai, Miklos; Morales Pinzon, Alfredo; Egorova, Svetlana; Styner, Martin; Guttmann, Charles R. G.

    2017-02-01

    Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

  18. Geospatial Augmented Reality for the interactive exploitation of large-scale walkable orthoimage maps in museums

    Science.gov (United States)

    Wüest, Robert; Nebiker, Stephan

    2018-05-01

    In this paper we present an app framework for augmenting large-scale walkable maps and orthoimages in museums or public spaces using standard smartphones and tablets. We first introduce a novel approach for using huge orthoimage mosaic floor prints covering several hundred square meters as natural Augmented Reality (AR) markers. We then present a new app architecture and subsequent tests in the Swissarena of the Swiss National Transport Museum in Lucerne demonstrating the capabilities of accurately tracking and augmenting different map topics, including dynamic 3d data such as live air traffic. The resulting prototype was tested with everyday visitors of the museum to get feedback on the usability of the AR app and to identify pitfalls when using AR in the context of a potentially crowded museum. The prototype is to be rolled out to the public after successful testing and optimization of the app. We were able to show that AR apps on standard smartphone devices can dramatically enhance the interactive use of large-scale maps for different purposes such as education or serious gaming in a museum context.

  19. Discovery of Protein–lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jun-Hao; Liu, Shun; Zheng, Ling-Ling; Wu, Jie; Sun, Wen-Ju; Wang, Ze-Lin; Zhou, Hui; Qu, Liang-Hu, E-mail: lssqlh@mail.sysu.edu.cn; Yang, Jian-Hua, E-mail: lssqlh@mail.sysu.edu.cn [RNA Information Center, Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou (China)

    2015-01-14

    Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein–lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP–lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP–lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.

  20. Discovery of Protein–lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets

    International Nuclear Information System (INIS)

    Li, Jun-Hao; Liu, Shun; Zheng, Ling-Ling; Wu, Jie; Sun, Wen-Ju; Wang, Ze-Lin; Zhou, Hui; Qu, Liang-Hu; Yang, Jian-Hua

    2015-01-01

    Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein–lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP–lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP–lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.

  1. Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

    OpenAIRE

    Xerxes D. Arsiwalla; Riccardo eZucca; Alberto eBetella; Enrique eMartinez; David eDalmazzo; Pedro eOmedas; Gustavo eDeco; Gustavo eDeco; Paul F.M.J. Verschure; Paul F.M.J. Verschure

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

  2. Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

    OpenAIRE

    Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martínez, Enrique, 1961-; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimula...

  3. Large-scale structure of the Universe

    International Nuclear Information System (INIS)

    Doroshkevich, A.G.

    1978-01-01

    The problems, discussed at the ''Large-scale Structure of the Universe'' symposium are considered on a popular level. Described are the cell structure of galaxy distribution in the Universe, principles of mathematical galaxy distribution modelling. The images of cell structures, obtained after reprocessing with the computer are given. Discussed are three hypothesis - vortical, entropic, adiabatic, suggesting various processes of galaxy and galaxy clusters origin. A considerable advantage of the adiabatic hypothesis is recognized. The relict radiation, as a method of direct studying the processes taking place in the Universe is considered. The large-scale peculiarities and small-scale fluctuations of the relict radiation temperature enable one to estimate the turbance properties at the pre-galaxy stage. The discussion of problems, pertaining to studying the hot gas, contained in galaxy clusters, the interactions within galaxy clusters and with the inter-galaxy medium, is recognized to be a notable contribution into the development of theoretical and observational cosmology

  4. Large-scale networks in engineering and life sciences

    CERN Document Server

    Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai

    2014-01-01

    This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...

  5. Unraveling The Connectome: Visualizing and Abstracting Large-Scale Connectomics Data

    KAUST Repository

    Al-Awami, Ali K.

    2017-01-01

    -user system seamlessly integrates a diverse set of tools. Our system provides support for the management, provenance, accountability, and auditing of large-scale segmentations. Finally, we present a novel architecture to render very large volumes interactively

  6. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  7. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  8. Predators on private land: broad-scale socioeconomic interactions influence large predator management

    Directory of Open Access Journals (Sweden)

    Hayley S. Clements

    2016-06-01

    Full Text Available The proliferation of private land conservation areas (PLCAs is placing increasing pressure on conservation authorities to effectively regulate their ecological management. Many PLCAs depend on tourism for income, and charismatic large mammal species are considered important for attracting international visitors. Broad-scale socioeconomic factors therefore have the potential to drive fine-scale ecological management, creating a systemic scale mismatch that can reduce long-term sustainability in cases where economic and conservation objectives are not perfectly aligned. We assessed the socioeconomic drivers and outcomes of large predator management on 71 PLCAs in South Africa. Owners of PLCAs that are stocking free-roaming large predators identified revenue generation as influencing most or all of their management decisions, and rated profit generation as a more important objective than did the owners of PLCAs that did not stock large predators. Ecotourism revenue increased with increasing lion (Panthera leo density, which created a potential economic incentive for stocking lion at high densities. Despite this potential mismatch between economic and ecological objectives, lion densities were sustainable relative to available prey. Regional-scale policy guidelines for free-roaming lion management were ecologically sound. By contrast, policy guidelines underestimated the area required to sustain cheetah (Acinonyx jubatus, which occurred at unsustainable densities relative to available prey. Evidence of predator overstocking included predator diet supplementation and frequent reintroduction of game. We conclude that effective facilitation of conservation on private land requires consideration of the strong and not necessarily beneficial multiscale socioeconomic factors that influence private land management.

  9. Interactions between neural networks: a mechanism for tuning chaos and oscillations.

    Science.gov (United States)

    Wang, Lipo

    2007-06-01

    We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability.

  10. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

    Science.gov (United States)

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

  11. Convolutional neural networks for transient candidate vetting in large-scale surveys

    Science.gov (United States)

    Gieseke, Fabian; Bloemen, Steven; van den Bogaard, Cas; Heskes, Tom; Kindler, Jonas; Scalzo, Richard A.; Ribeiro, Valério A. R. M.; van Roestel, Jan; Groot, Paul J.; Yuan, Fang; Möller, Anais; Tucker, Brad E.

    2017-12-01

    Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional pre-processing steps - eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3 per cent of all 'real' and 99.7 per cent of all 'bogus' instances on a test set containing 1942 'bogus' and 227 'real' instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all.

  12. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    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.

  13. Information-geometric measures estimate neural interactions during oscillatory brain states

    Directory of Open Access Journals (Sweden)

    Yimin eNie

    2014-02-01

    Full Text Available The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG, a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

  14. Large transverse momenta in inclusive hadronic reactions and asymptotic scale invariance

    International Nuclear Information System (INIS)

    Miralles, F.; Sala, C.

    1976-01-01

    The inclusive reaction among scalar particles in considered, assuming that in the large-transverse momentum limit, scale invariance becomes important. Predictions are made of the asymptotic scale invariance for large four transverse momentum in hadron-hadron interactions, and they are compared with previous predictions. Photoproduction is also studied and the predictions that follow from different assumptions about the compositeness of hadrons are compared

  15. Large-scale changes in network interactions as a physiological signature of spatial neglect.

    Science.gov (United States)

    Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L; Callejas, Alicia; Astafiev, Serguei V; Metcalf, Nicholas V; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z; Carter, Alex R; Shulman, Gordon L; Corbetta, Maurizio

    2014-12-01

    networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. Large-scale solar purchasing

    International Nuclear Information System (INIS)

    1999-01-01

    The principal objective of the project was to participate in the definition of a new IEA task concerning solar procurement (''the Task'') and to assess whether involvement in the task would be in the interest of the UK active solar heating industry. The project also aimed to assess the importance of large scale solar purchasing to UK active solar heating market development and to evaluate the level of interest in large scale solar purchasing amongst potential large scale purchasers (in particular housing associations and housing developers). A further aim of the project was to consider means of stimulating large scale active solar heating purchasing activity within the UK. (author)

  17. Geospatial Optimization of Siting Large-Scale Solar Projects

    Energy Technology Data Exchange (ETDEWEB)

    Macknick, Jordan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Quinby, Ted [National Renewable Energy Lab. (NREL), Golden, CO (United States); Caulfield, Emmet [Stanford Univ., CA (United States); Gerritsen, Margot [Stanford Univ., CA (United States); Diffendorfer, Jay [U.S. Geological Survey, Boulder, CO (United States); Haines, Seth [U.S. Geological Survey, Boulder, CO (United States)

    2014-03-01

    Recent policy and economic conditions have encouraged a renewed interest in developing large-scale solar projects in the U.S. Southwest. However, siting large-scale solar projects is complex. In addition to the quality of the solar resource, solar developers must take into consideration many environmental, social, and economic factors when evaluating a potential site. This report describes a proof-of-concept, Web-based Geographical Information Systems (GIS) tool that evaluates multiple user-defined criteria in an optimization algorithm to inform discussions and decisions regarding the locations of utility-scale solar projects. Existing siting recommendations for large-scale solar projects from governmental and non-governmental organizations are not consistent with each other, are often not transparent in methods, and do not take into consideration the differing priorities of stakeholders. The siting assistance GIS tool we have developed improves upon the existing siting guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.

  18. Identifying gene-environment interactions in schizophrenia: contemporary challenges for integrated, large-scale investigations.

    Science.gov (United States)

    van Os, Jim; Rutten, Bart P; Myin-Germeys, Inez; Delespaul, Philippe; Viechtbauer, Wolfgang; van Zelst, Catherine; Bruggeman, Richard; Reininghaus, Ulrich; Morgan, Craig; Murray, Robin M; Di Forti, Marta; McGuire, Philip; Valmaggia, Lucia R; Kempton, Matthew J; Gayer-Anderson, Charlotte; Hubbard, Kathryn; Beards, Stephanie; Stilo, Simona A; Onyejiaka, Adanna; Bourque, Francois; Modinos, Gemma; Tognin, Stefania; Calem, Maria; O'Donovan, Michael C; Owen, Michael J; Holmans, Peter; Williams, Nigel; Craddock, Nicholas; Richards, Alexander; Humphreys, Isla; Meyer-Lindenberg, Andreas; Leweke, F Markus; Tost, Heike; Akdeniz, Ceren; Rohleder, Cathrin; Bumb, J Malte; Schwarz, Emanuel; Alptekin, Köksal; Üçok, Alp; Saka, Meram Can; Atbaşoğlu, E Cem; Gülöksüz, Sinan; Gumus-Akay, Guvem; Cihan, Burçin; Karadağ, Hasan; Soygür, Haldan; Cankurtaran, Eylem Şahin; Ulusoy, Semra; Akdede, Berna; Binbay, Tolga; Ayer, Ahmet; Noyan, Handan; Karadayı, Gülşah; Akturan, Elçin; Ulaş, Halis; Arango, Celso; Parellada, Mara; Bernardo, Miguel; Sanjuán, Julio; Bobes, Julio; Arrojo, Manuel; Santos, Jose Luis; Cuadrado, Pedro; Rodríguez Solano, José Juan; Carracedo, Angel; García Bernardo, Enrique; Roldán, Laura; López, Gonzalo; Cabrera, Bibiana; Cruz, Sabrina; Díaz Mesa, Eva Ma; Pouso, María; Jiménez, Estela; Sánchez, Teresa; Rapado, Marta; González, Emiliano; Martínez, Covadonga; Sánchez, Emilio; Olmeda, Ma Soledad; de Haan, Lieuwe; Velthorst, Eva; van der Gaag, Mark; Selten, Jean-Paul; van Dam, Daniella; van der Ven, Elsje; van der Meer, Floor; Messchaert, Elles; Kraan, Tamar; Burger, Nadine; Leboyer, Marion; Szoke, Andrei; Schürhoff, Franck; Llorca, Pierre-Michel; Jamain, Stéphane; Tortelli, Andrea; Frijda, Flora; Vilain, Jeanne; Galliot, Anne-Marie; Baudin, Grégoire; Ferchiou, Aziz; Richard, Jean-Romain; Bulzacka, Ewa; Charpeaud, Thomas; Tronche, Anne-Marie; De Hert, Marc; van Winkel, Ruud; Decoster, Jeroen; Derom, Catherine; Thiery, Evert; Stefanis, Nikos C; Sachs, Gabriele; Aschauer, Harald; Lasser, Iris; Winklbaur, Bernadette; Schlögelhofer, Monika; Riecher-Rössler, Anita; Borgwardt, Stefan; Walter, Anna; Harrisberger, Fabienne; Smieskova, Renata; Rapp, Charlotte; Ittig, Sarah; Soguel-dit-Piquard, Fabienne; Studerus, Erich; Klosterkötter, Joachim; Ruhrmann, Stephan; Paruch, Julia; Julkowski, Dominika; Hilboll, Desiree; Sham, Pak C; Cherny, Stacey S; Chen, Eric Y H; Campbell, Desmond D; Li, Miaoxin; Romeo-Casabona, Carlos María; Emaldi Cirión, Aitziber; Urruela Mora, Asier; Jones, Peter; Kirkbride, James; Cannon, Mary; Rujescu, Dan; Tarricone, Ilaria; Berardi, Domenico; Bonora, Elena; Seri, Marco; Marcacci, Thomas; Chiri, Luigi; Chierzi, Federico; Storbini, Viviana; Braca, Mauro; Minenna, Maria Gabriella; Donegani, Ivonne; Fioritti, Angelo; La Barbera, Daniele; La Cascia, Caterina Erika; Mulè, Alice; Sideli, Lucia; Sartorio, Rachele; Ferraro, Laura; Tripoli, Giada; Seminerio, Fabio; Marinaro, Anna Maria; McGorry, Patrick; Nelson, Barnaby; Amminger, G Paul; Pantelis, Christos; Menezes, Paulo R; Del-Ben, Cristina M; Gallo Tenan, Silvia H; Shuhama, Rosana; Ruggeri, Mirella; Tosato, Sarah; Lasalvia, Antonio; Bonetto, Chiara; Ira, Elisa; Nordentoft, Merete; Krebs, Marie-Odile; Barrantes-Vidal, Neus; Cristóbal, Paula; Kwapil, Thomas R; Brietzke, Elisa; Bressan, Rodrigo A; Gadelha, Ary; Maric, Nadja P; Andric, Sanja; Mihaljevic, Marina; Mirjanic, Tijana

    2014-07-01

    Recent years have seen considerable progress in epidemiological and molecular genetic research into environmental and genetic factors in schizophrenia, but methodological uncertainties remain with regard to validating environmental exposures, and the population risk conferred by individual molecular genetic variants is small. There are now also a limited number of studies that have investigated molecular genetic candidate gene-environment interactions (G × E), however, so far, thorough replication of findings is rare and G × E research still faces several conceptual and methodological challenges. In this article, we aim to review these recent developments and illustrate how integrated, large-scale investigations may overcome contemporary challenges in G × E research, drawing on the example of a large, international, multi-center study into the identification and translational application of G × E in schizophrenia. While such investigations are now well underway, new challenges emerge for G × E research from late-breaking evidence that genetic variation and environmental exposures are, to a significant degree, shared across a range of psychiatric disorders, with potential overlap in phenotype. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  19. Statistical physics of interacting neural networks

    Science.gov (United States)

    Kinzel, Wolfgang; Metzler, Richard; Kanter, Ido

    2001-12-01

    Recent results on the statistical physics of time series generation and prediction are presented. A neural network is trained on quasi-periodic and chaotic sequences and overlaps to the sequence generator as well as the prediction errors are calculated numerically. For each network there exists a sequence for which it completely fails to make predictions. Two interacting networks show a transition to perfect synchronization. A pool of interacting networks shows good coordination in the minority game-a model of competition in a closed market. Finally, as a demonstration, a perceptron predicts bit sequences produced by human beings.

  20. Neural correlate of human reciprocity in social interactions.

    Science.gov (United States)

    Sakaiya, Shiro; Shiraito, Yuki; Kato, Junko; Ide, Hiroko; Okada, Kensuke; Takano, Kouji; Kansaku, Kenji

    2013-01-01

    Reciprocity plays a key role maintaining cooperation in society. However, little is known about the neural process that underpins human reciprocity during social interactions. Our neuroimaging study manipulated partner identity (computer, human) and strategy (random, tit-for-tat) in repeated prisoner's dilemma games and investigated the neural correlate of reciprocal interaction with humans. Reciprocal cooperation with humans but exploitation of computers by defection was associated with activation in the left amygdala. Amygdala activation was also positively and negatively correlated with a preference change for human partners following tit-for-tat and random strategies, respectively. The correlated activation represented the intensity of positive feeling toward reciprocal and negative feeling toward non-reciprocal partners, and so reflected reciprocity in social interaction. Reciprocity in social interaction, however, might plausibly be misinterpreted and so we also examined the neural coding of insight into the reciprocity of partners. Those with and without insight revealed differential brain activation across the reward-related circuitry (i.e., the right middle dorsolateral prefrontal cortex and dorsal caudate) and theory of mind (ToM) regions [i.e., ventromedial prefrontal cortex (VMPFC) and precuneus]. Among differential activations, activation in the precuneus, which accompanied deactivation of the VMPFC, was specific to those without insight into human partners who were engaged in a tit-for-tat strategy. This asymmetric (de)activation might involve specific contributions of ToM regions to the human search for reciprocity. Consequently, the intensity of emotion attached to human reciprocity was represented in the amygdala, whereas insight into the reciprocity of others was reflected in activation across the reward-related and ToM regions. This suggests the critical role of mentalizing, which was not equated with reward expectation during social interactions.

  1. Neural correlate of human reciprocity in social interactions

    Directory of Open Access Journals (Sweden)

    Shiro eSakaiya

    2013-12-01

    Full Text Available Reciprocity plays a key role maintaining cooperation in society. However, little is known about the neural process that underpins human reciprocity during social interactions. Our neuroimaging study manipulated partner identity (computer, human and strategy (random, tit-for-tat in repeated prisoner’s dilemma games and investigated the neural correlate of reciprocal interaction with humans. Reciprocal cooperation with humans but exploitation of computers by defection was associated with activation in the left amygdala. Amygdala activation was also positively and negatively correlated with a preference change for human partners following tit-for-tat and random strategies, respectively. The correlated activation represented the intensity of positive feeling toward reciprocal and negative feeling toward non-reciprocal partners, and so reflected reciprocity in social interaction. Reciprocity in social interaction, however, might plausibly be misinterpreted and so we also examined the neural coding of insight into the reciprocity of partners. Those with and without insight revealed differential brain activation across the reward-related circuitry (i.e., the right middle dorsolateral prefrontal cortex and dorsal caudate and theory of mind (ToM regions (i.e., ventromedial prefrontal cortex [VMPFC] and precuneus. Among differential activations, activation in the precuneus, which accompanied deactivation of the VMPFC, was specific to those without insight into human partners who were engaged in a tit-for-tat strategy. This asymmetric (deactivation might involve specific contributions of ToM regions to the human search for reciprocity. Consequently, the intensity of emotion attached to human reciprocity was represented in the amygdala, whereas insight into the reciprocity of others was reflected in activation across the reward-related and ToM regions. This suggests the critical role of mentalizing, which was not equated with reward expectation during

  2. Multi-Scale Models for the Scale Interaction of Organized Tropical Convection

    Science.gov (United States)

    Yang, Qiu

    Assessing the upscale impact of organized tropical convection from small spatial and temporal scales is a research imperative, not only for having a better understanding of the multi-scale structures of dynamical and convective fields in the tropics, but also for eventually helping in the design of new parameterization strategies to improve the next-generation global climate models. Here self-consistent multi-scale models are derived systematically by following the multi-scale asymptotic methods and used to describe the hierarchical structures of tropical atmospheric flows. The advantages of using these multi-scale models lie in isolating the essential components of multi-scale interaction and providing assessment of the upscale impact of the small-scale fluctuations onto the large-scale mean flow through eddy flux divergences of momentum and temperature in a transparent fashion. Specifically, this thesis includes three research projects about multi-scale interaction of organized tropical convection, involving tropical flows at different scaling regimes and utilizing different multi-scale models correspondingly. Inspired by the observed variability of tropical convection on multiple temporal scales, including daily and intraseasonal time scales, the goal of the first project is to assess the intraseasonal impact of the diurnal cycle on the planetary-scale circulation such as the Hadley cell. As an extension of the first project, the goal of the second project is to assess the intraseasonal impact of the diurnal cycle over the Maritime Continent on the Madden-Julian Oscillation. In the third project, the goals are to simulate the baroclinic aspects of the ITCZ breakdown and assess its upscale impact on the planetary-scale circulation over the eastern Pacific. These simple multi-scale models should be useful to understand the scale interaction of organized tropical convection and help improve the parameterization of unresolved processes in global climate models.

  3. Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

    Directory of Open Access Journals (Sweden)

    Hongli Liu

    2009-01-01

    Full Text Available We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.

  4. The Dutch Social Interaction Anxiety Scale and the Social Phobia Scale: Reliability, Validity, and Clinical Utility

    OpenAIRE

    de Beurs, Edwin; Tielen, Deirdre; Wollmann, Lisa

    2014-01-01

    The social interaction anxiety scale (SIAS) and the social phobia scale (SPS) assess anxiety in social interactions and fear of scrutiny by others. This study examines the psychometric properties of the Dutch versions of the SIAS and SPS using data from a large group of patients with social phobia and a community-based sample. Confirmatory factor analysis revealed that the SIAS is unidimensional, whereas the SPS is comprised of three subscales. The internal consistency of the scales and subsc...

  5. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2016-01-01

    Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  6. Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

    Science.gov (United States)

    Santangelo, Valerio

    2018-01-01

    Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks

  7. Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

    Directory of Open Access Journals (Sweden)

    Valerio Santangelo

    2018-02-01

    Full Text Available Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010 to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory in one spatial location. The analysis of the independent components (ICs revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC. The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among

  8. Neural Darwinism and consciousness.

    Science.gov (United States)

    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.

  9. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  10. Fluid-structure interaction in non-rigid pipeline systems - large scale validation experiments

    International Nuclear Information System (INIS)

    Heinsbroek, A.G.T.J.; Kruisbrink, A.C.H.

    1993-01-01

    The fluid-structure interaction computer code FLUSTRIN, developed by DELFT HYDRAULICS, enables the user to determine dynamic fluid pressures, structural stresses and displacements in a liquid-filled pipeline system under transient conditions. As such, the code is a useful tool to process and mechanical engineers in the safe design and operation of pipeline systems in nuclear power plants. To validate FLUSTRIN, experiments have been performed in a large scale 3D test facility. The test facility consists of a flexible pipeline system which is suspended by wires, bearings and anchors. Pressure surges, which excite the system, are generated by a fast acting shut-off valve. Dynamic pressures, structural displacements and strains (in total 70 signals) have been measured under well determined initial and boundary conditions. The experiments have been simulated with FLUSTRIN, which solves the acoustic equations using the method of characteristics (fluid) and the finite element method (structure). The agreement between experiments and simulations is shown to be good: frequencies, amplitudes and wave phenomena are well predicted by the numerical simulations. It is demonstrated that an uncoupled water hammer computation would render unreliable and useless results. (author)

  11. Dynamics of neural cryptography.

    Science.gov (United States)

    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.

  12. 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

  13. Dynamics of neural cryptography

    Science.gov (United States)

    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.

  14. Unraveling The Connectome: Visualizing and Abstracting Large-Scale Connectomics Data

    KAUST Repository

    Al-Awami, Ali K.

    2017-04-30

    We explore visualization and abstraction approaches to represent neuronal data. Neuroscientists acquire electron microscopy volumes to reconstruct a complete wiring diagram of the neurons in the brain, called the connectome. This will be crucial to understanding brains and their development. However, the resulting data is complex and large, posing a big challenge to existing visualization techniques in terms of clarity and scalability. We describe solutions to tackle the problems of scalability and cluttered presentation. We first show how a query-guided interactive approach to visual exploration can reduce the clutter and help neuroscientists explore their data dynamically. We use a knowledge-based query algebra that facilitates the interactive creation of queries. This allows neuroscientists to pose domain-specific questions related to their research. Simple queries can be combined to form complex queries to answer more sophisticated questions. We then show how visual abstractions from 3D to 2D can significantly reduce the visual clutter and add clarity to the visualization so that scientists can focus more on the analysis. We abstract the topology of 3D neurons into a multi-scale, relative distance-preserving subway map visualization that allows scientists to interactively explore the morphological and connectivity features of neuronal cells. We then focus on the process of acquisition, where neuroscientists segment electron microscopy images to reconstruct neurons. The segmentation process of such data is tedious, time-intensive, and usually performed using a diverse set of tools. We present a novel web-based visualization system for tracking the state, progress, and evolution of segmentation data in neuroscience. Our multi-user system seamlessly integrates a diverse set of tools. Our system provides support for the management, provenance, accountability, and auditing of large-scale segmentations. Finally, we present a novel architecture to render very large

  15. Feed forward neural networks modeling for K-P interactions

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2003-01-01

    Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

  16. Large-Scale medical image analytics: Recent methodologies, applications and Future directions.

    Science.gov (United States)

    Zhang, Shaoting; Metaxas, Dimitris

    2016-10-01

    Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion. Copyright © 2016. Published by Elsevier B.V.

  17. Energy transfers in large-scale and small-scale dynamos

    Science.gov (United States)

    Samtaney, Ravi; Kumar, Rohit; Verma, Mahendra

    2015-11-01

    We present the energy transfers, mainly energy fluxes and shell-to-shell energy transfers in small-scale dynamo (SSD) and large-scale dynamo (LSD) using numerical simulations of MHD turbulence for Pm = 20 (SSD) and for Pm = 0.2 on 10243 grid. For SSD, we demonstrate that the magnetic energy growth is caused by nonlocal energy transfers from the large-scale or forcing-scale velocity field to small-scale magnetic field. The peak of these energy transfers move towards lower wavenumbers as dynamo evolves, which is the reason for the growth of the magnetic fields at the large scales. The energy transfers U2U (velocity to velocity) and B2B (magnetic to magnetic) are forward and local. For LSD, we show that the magnetic energy growth takes place via energy transfers from large-scale velocity field to large-scale magnetic field. We observe forward U2U and B2B energy flux, similar to SSD.

  18. Deep Feature Learning and Cascaded Classifier for Large Scale Data

    DEFF Research Database (Denmark)

    Prasoon, Adhish

    from data rather than having a predefined feature set. We explore deep learning approach of convolutional neural network (CNN) for segmenting three dimensional medical images. We propose a novel system integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D......This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge...... amount of training data to cover sufficient biological variability. Learning methods scaling badly with number of training data points cannot be used in such scenarios. This may restrict the usage of many powerful classifiers having excellent generalization ability. We propose a cascaded classifier which...

  19. Evolutionary leap in large-scale flood risk assessment needed

    OpenAIRE

    Vorogushyn, Sergiy; Bates, Paul D.; de Bruijn, Karin; Castellarin, Attilio; Kreibich, Heidi; Priest, Sally J.; Schröter, Kai; Bagli, Stefano; Blöschl, Günter; Domeneghetti, Alessio; Gouldby, Ben; Klijn, Frans; Lammersen, Rita; Neal, Jeffrey C.; Ridder, Nina

    2018-01-01

    Current approaches for assessing large-scale flood risks contravene the fundamental principles of the flood risk system functioning because they largely ignore basic interactions and feedbacks between atmosphere, catchments, river-floodplain systems and socio-economic processes. As a consequence, risk analyses are uncertain and might be biased. However, reliable risk estimates are required for prioritizing national investments in flood risk mitigation or for appraisal and management of insura...

  20. Postaccident heat removal: large-scale molten-fuel-sodium interaction experiments

    International Nuclear Information System (INIS)

    Johnson, T.R.; Pavlik, J.R.; Baker, L. Jr.

    1975-02-01

    Kilogram-scale interactions between molten UO 2 and sodium were performed in an unrestrained geometry to study the resulting energetics and fragmentation. The molten UO 2 was producted by the exothrmic reaction between uranium and MoO 3 powders. Under the conditions of the experiments completed to date, the short-rise-time pressure pulses created in the liquid phase had negligible work potential, and their magnitude did not increase with the amount of molten fuel. No significant gas-phase shock pressures were generated. The largest potential for mechanical work was the sodium vapor generated over a period of roughly 1 sec. About 20 percent of the heat was effective in generating vapor. The ex- perimental results show a marked tendency of molten UO 2 to form particulate after passage through only a few inches of sodium. Particle size distributions obtained under the conditions of the experiments were not significantly different from those obtained in prior small-scale tests and in TREAT tests. Also, the results indicate that the metallic component of the molten mixture formed larger particles than the oxide component. (U.S.)

  1. Interacting neural networks

    Science.gov (United States)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  2. Photorealistic large-scale urban city model reconstruction.

    Science.gov (United States)

    Poullis, Charalambos; You, Suya

    2009-01-01

    The rapid and efficient creation of virtual environments has become a crucial part of virtual reality applications. In particular, civil and defense applications often require and employ detailed models of operations areas for training, simulations of different scenarios, planning for natural or man-made events, monitoring, surveillance, games, and films. A realistic representation of the large-scale environments is therefore imperative for the success of such applications since it increases the immersive experience of its users and helps reduce the difference between physical and virtual reality. However, the task of creating such large-scale virtual environments still remains a time-consuming and manual work. In this work, we propose a novel method for the rapid reconstruction of photorealistic large-scale virtual environments. First, a novel, extendible, parameterized geometric primitive is presented for the automatic building identification and reconstruction of building structures. In addition, buildings with complex roofs containing complex linear and nonlinear surfaces are reconstructed interactively using a linear polygonal and a nonlinear primitive, respectively. Second, we present a rendering pipeline for the composition of photorealistic textures, which unlike existing techniques, can recover missing or occluded texture information by integrating multiple information captured from different optical sensors (ground, aerial, and satellite).

  3. Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1993-01-01

    A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

  4. State of the Art in Large-Scale Soil Moisture Monitoring

    Science.gov (United States)

    Ochsner, Tyson E.; Cosh, Michael Harold; Cuenca, Richard H.; Dorigo, Wouter; Draper, Clara S.; Hagimoto, Yutaka; Kerr, Yan H.; Larson, Kristine M.; Njoku, Eni Gerald; Small, Eric E.; hide

    2013-01-01

    Soil moisture is an essential climate variable influencing land atmosphere interactions, an essential hydrologic variable impacting rainfall runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large-scale soil moisture monitoring has advanced in recent years creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication. For some applications, the science required to utilize large-scale soil moisture data is poorly developed. In this review, we describe the state of the art in large-scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of 1) emerging in situ and proximal sensing techniques, 2) dedicated soil moisture remote sensing missions, 3) soil moisture monitoring networks, and 4) applications of large-scale soil moisture measurements. Significant near-term progress seems possible in the use of large-scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to model structures and model errors remain. Little progress has been made yet in the use of large-scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large-scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.

  5. Scaling of counter-current imbibition recovery curves using artificial neural networks

    Science.gov (United States)

    Jafari, Iman; Masihi, Mohsen; Nasiri Zarandi, Masoud

    2018-06-01

    Scaling imbibition curves are of great importance in the characterization and simulation of oil production from naturally fractured reservoirs. Different parameters such as matrix porosity and permeability, oil and water viscosities, matrix dimensions, and oil/water interfacial tensions have an effective on the imbibition process. Studies on the scaling imbibition curves along with the consideration of different assumptions have resulted in various scaling equations. In this work, using an artificial neural network (ANN) method, a novel technique is presented for scaling imbibition recovery curves, which can be used for scaling the experimental and field-scale imbibition cases. The imbibition recovery curves for training and testing the neural network were gathered through the simulation of different scenarios using a commercial reservoir simulator. In this ANN-based method, six parameters were assumed to have an effect on the imbibition process and were considered as the inputs for training the network. Using the ‘Bayesian regularization’ training algorithm, the network was trained and tested. Training and testing phases showed superior results in comparison with the other scaling methods. It is concluded that using the new technique is useful for scaling imbibition recovery curves, especially for complex cases, for which the common scaling methods are not designed.

  6. Creating Large Scale Database Servers

    International Nuclear Information System (INIS)

    Becla, Jacek

    2001-01-01

    The BaBar experiment at the Stanford Linear Accelerator Center (SLAC) is designed to perform a high precision investigation of the decays of the B-meson produced from electron-positron interactions. The experiment, started in May 1999, will generate approximately 300TB/year of data for 10 years. All of the data will reside in Objectivity databases accessible via the Advanced Multi-threaded Server (AMS). To date, over 70TB of data have been placed in Objectivity/DB, making it one of the largest databases in the world. Providing access to such a large quantity of data through a database server is a daunting task. A full-scale testbed environment had to be developed to tune various software parameters and a fundamental change had to occur in the AMS architecture to allow it to scale past several hundred terabytes of data. Additionally, several protocol extensions had to be implemented to provide practical access to large quantities of data. This paper will describe the design of the database and the changes that we needed to make in the AMS for scalability reasons and how the lessons we learned would be applicable to virtually any kind of database server seeking to operate in the Petabyte region

  7. Creating Large Scale Database Servers

    Energy Technology Data Exchange (ETDEWEB)

    Becla, Jacek

    2001-12-14

    The BaBar experiment at the Stanford Linear Accelerator Center (SLAC) is designed to perform a high precision investigation of the decays of the B-meson produced from electron-positron interactions. The experiment, started in May 1999, will generate approximately 300TB/year of data for 10 years. All of the data will reside in Objectivity databases accessible via the Advanced Multi-threaded Server (AMS). To date, over 70TB of data have been placed in Objectivity/DB, making it one of the largest databases in the world. Providing access to such a large quantity of data through a database server is a daunting task. A full-scale testbed environment had to be developed to tune various software parameters and a fundamental change had to occur in the AMS architecture to allow it to scale past several hundred terabytes of data. Additionally, several protocol extensions had to be implemented to provide practical access to large quantities of data. This paper will describe the design of the database and the changes that we needed to make in the AMS for scalability reasons and how the lessons we learned would be applicable to virtually any kind of database server seeking to operate in the Petabyte region.

  8. QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.

    Science.gov (United States)

    Zhao, Shanrong; Xi, Li; Quan, Jie; Xi, Hualin; Zhang, Ying; von Schack, David; Vincent, Michael; Zhang, Baohong

    2016-01-08

    RNA sequencing (RNA-seq), a next-generation sequencing technique for transcriptome profiling, is being increasingly used, in part driven by the decreasing cost of sequencing. Nevertheless, the analysis of the massive amounts of data generated by large-scale RNA-seq remains a challenge. Multiple algorithms pertinent to basic analyses have been developed, and there is an increasing need to automate the use of these tools so as to obtain results in an efficient and user friendly manner. Increased automation and improved visualization of the results will help make the results and findings of the analyses readily available to experimental scientists. By combing the best open source tools developed for RNA-seq data analyses and the most advanced web 2.0 technologies, we have implemented QuickRNASeq, a pipeline for large-scale RNA-seq data analyses and visualization. The QuickRNASeq workflow consists of three main steps. In Step #1, each individual sample is processed, including mapping RNA-seq reads to a reference genome, counting the numbers of mapped reads, quality control of the aligned reads, and SNP (single nucleotide polymorphism) calling. Step #1 is computationally intensive, and can be processed in parallel. In Step #2, the results from individual samples are merged, and an integrated and interactive project report is generated. All analyses results in the report are accessible via a single HTML entry webpage. Step #3 is the data interpretation and presentation step. The rich visualization features implemented here allow end users to interactively explore the results of RNA-seq data analyses, and to gain more insights into RNA-seq datasets. In addition, we used a real world dataset to demonstrate the simplicity and efficiency of QuickRNASeq in RNA-seq data analyses and interactive visualizations. The seamless integration of automated capabilites with interactive visualizations in QuickRNASeq is not available in other published RNA-seq pipelines. The high degree

  9. Time-scale effects in the interaction between a large and a small herbivore

    NARCIS (Netherlands)

    Kuijper, D. P. J.; Beek, P.; van Wieren, S.E.; Bakker, J. P.

    2008-01-01

    In the short term, grazing will mainly affect plant biomass and forage quality. However, grazing can affect plant species composition by accelerating or retarding succession at longer time-scales. Few studies concerning interactions among herbivores have taken the change in plant species composition

  10. Large-scale functional networks connect differently for processing words and symbol strings.

    Science.gov (United States)

    Liljeström, Mia; Vartiainen, Johanna; Kujala, Jan; Salmelin, Riitta

    2018-01-01

    Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

  11. Studies of Sub-Synchronous Oscillations in Large-Scale Wind Farm Integrated System

    Science.gov (United States)

    Yue, Liu; Hang, Mend

    2018-01-01

    With the rapid development and construction of large-scale wind farms and grid-connected operation, the series compensation wind power AC transmission is gradually becoming the main way of power usage and improvement of wind power availability and grid stability, but the integration of wind farm will change the SSO (Sub-Synchronous oscillation) damping characteristics of synchronous generator system. Regarding the above SSO problem caused by integration of large-scale wind farms, this paper focusing on doubly fed induction generator (DFIG) based wind farms, aim to summarize the SSO mechanism in large-scale wind power integrated system with series compensation, which can be classified as three types: sub-synchronous control interaction (SSCI), sub-synchronous torsional interaction (SSTI), sub-synchronous resonance (SSR). Then, SSO modelling and analysis methods are categorized and compared by its applicable areas. Furthermore, this paper summarizes the suppression measures of actual SSO projects based on different control objectives. Finally, the research prospect on this field is explored.

  12. Large-scale data analytics

    CERN Document Server

    Gkoulalas-Divanis, Aris

    2014-01-01

    Provides cutting-edge research in large-scale data analytics from diverse scientific areas Surveys varied subject areas and reports on individual results of research in the field Shares many tips and insights into large-scale data analytics from authors and editors with long-term experience and specialization in the field

  13. How CMB and large-scale structure constrain chameleon interacting dark energy

    International Nuclear Information System (INIS)

    Boriero, Daniel; Das, Subinoy; Wong, Yvonne Y.Y.

    2015-01-01

    We explore a chameleon type of interacting dark matter-dark energy scenario in which a scalar field adiabatically traces the minimum of an effective potential sourced by the dark matter density. We discuss extensively the effect of this coupling on cosmological observables, especially the parameter degeneracies expected to arise between the model parameters and other cosmological parameters, and then test the model against observations of the cosmic microwave background (CMB) anisotropies and other cosmological probes. We find that the chameleon parameters α and β, which determine respectively the slope of the scalar field potential and the dark matter-dark energy coupling strength, can be constrained to α < 0.17 and β < 0.19 using CMB data and measurements of baryon acoustic oscillations. The latter parameter in particular is constrained only by the late Integrated Sachs-Wolfe effect. Adding measurements of the local Hubble expansion rate H 0 tightens the bound on α by a factor of two, although this apparent improvement is arguably an artefact of the tension between the local measurement and the H 0 value inferred from Planck data in the minimal ΛCDM model. The same argument also precludes chameleon models from mimicking a dark radiation component, despite a passing similarity between the two scenarios in that they both delay the epoch of matter-radiation equality. Based on the derived parameter constraints, we discuss possible signatures of the model for ongoing and future large-scale structure surveys

  14. How CMB and large-scale structure constrain chameleon interacting dark energy

    Energy Technology Data Exchange (ETDEWEB)

    Boriero, Daniel [Fakultät für Physik, Universität Bielefeld, Universitätstr. 25, Bielefeld (Germany); Das, Subinoy [Indian Institute of Astrophisics, Bangalore, 560034 (India); Wong, Yvonne Y.Y., E-mail: boriero@physik.uni-bielefeld.de, E-mail: subinoy@iiap.res.in, E-mail: yvonne.y.wong@unsw.edu.au [School of Physics, The University of New South Wales, Sydney NSW 2052 (Australia)

    2015-07-01

    We explore a chameleon type of interacting dark matter-dark energy scenario in which a scalar field adiabatically traces the minimum of an effective potential sourced by the dark matter density. We discuss extensively the effect of this coupling on cosmological observables, especially the parameter degeneracies expected to arise between the model parameters and other cosmological parameters, and then test the model against observations of the cosmic microwave background (CMB) anisotropies and other cosmological probes. We find that the chameleon parameters α and β, which determine respectively the slope of the scalar field potential and the dark matter-dark energy coupling strength, can be constrained to α < 0.17 and β < 0.19 using CMB data and measurements of baryon acoustic oscillations. The latter parameter in particular is constrained only by the late Integrated Sachs-Wolfe effect. Adding measurements of the local Hubble expansion rate H{sub 0} tightens the bound on α by a factor of two, although this apparent improvement is arguably an artefact of the tension between the local measurement and the H{sub 0} value inferred from Planck data in the minimal ΛCDM model. The same argument also precludes chameleon models from mimicking a dark radiation component, despite a passing similarity between the two scenarios in that they both delay the epoch of matter-radiation equality. Based on the derived parameter constraints, we discuss possible signatures of the model for ongoing and future large-scale structure surveys.

  15. Direction of information flow in large-scale resting-state networks is frequency-dependent

    NARCIS (Netherlands)

    Hillebrand, Arjan; Tewarie, Prejaas; Van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W S; Douw, Linda; Gouw, Alida A.; Van Straaten, Elisabeth C W; Stam, Cornelis J.

    2016-01-01

    Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these

  16. Hadronic interactions at energies around 103 TeV inferred from the large-scale emulsion chamber experiment at Mt. Fuji

    International Nuclear Information System (INIS)

    Akashi, M.; Amenomori, M.; Konishi, E.

    1981-01-01

    Details of experimental results, obtained with the large-scale emulsion chambers at Mt. Fuji (3776 m above sea level), are presented to investigate the hadronic interactions at energies around 10 3 TeV, comparing with the Monte Carlo simulation based on various assumed interaction models and primary particles. It may be briefly summarized as 1) Scaling in the fragmentation region can be valid up to 10 3 TeV at least only if the primary particles are mixed, with significant amounts of heavy nuclei at energies over 10 14 eV, 2) Cross sections should continue to increase with energy up to 10 3 TeV, at least, at almost the same rate as that obtained at lower energies, 3) There exist some evidence to indicate remarkable production of particles or jets with high P sub(t) of several GeV/c at energies around 10 3 TeV, 4) Hadron to gamma-ray ratios in the observed family events remain almost within the scope of simulation prediction. Other interesting characters of family events are also discussed. (author)

  17. Large-scale grid management

    International Nuclear Information System (INIS)

    Langdal, Bjoern Inge; Eggen, Arnt Ove

    2003-01-01

    The network companies in the Norwegian electricity industry now have to establish a large-scale network management, a concept essentially characterized by (1) broader focus (Broad Band, Multi Utility,...) and (2) bigger units with large networks and more customers. Research done by SINTEF Energy Research shows so far that the approaches within large-scale network management may be structured according to three main challenges: centralization, decentralization and out sourcing. The article is part of a planned series

  18. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  19. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. Generation of large-scale vorticity in rotating stratified turbulence with inhomogeneous helicity: mean-field theory

    Science.gov (United States)

    Kleeorin, N.

    2018-06-01

    We discuss a mean-field theory of the generation of large-scale vorticity in a rotating density stratified developed turbulence with inhomogeneous kinetic helicity. We show that the large-scale non-uniform flow is produced due to either a combined action of a density stratified rotating turbulence and uniform kinetic helicity or a combined effect of a rotating incompressible turbulence and inhomogeneous kinetic helicity. These effects result in the formation of a large-scale shear, and in turn its interaction with the small-scale turbulence causes an excitation of the large-scale instability (known as a vorticity dynamo) due to a combined effect of the large-scale shear and Reynolds stress-induced generation of the mean vorticity. The latter is due to the effect of large-scale shear on the Reynolds stress. A fast rotation suppresses this large-scale instability.

  1. Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators

    International Nuclear Information System (INIS)

    Fonseca, R A; Vieira, J; Silva, L O; Fiuza, F; Davidson, A; Tsung, F S; Mori, W B

    2013-01-01

    A new generation of laser wakefield accelerators (LWFA), supported by the extreme accelerating fields generated in the interaction of PW-Class lasers and underdense targets, promises the production of high quality electron beams in short distances for multiple applications. Achieving this goal will rely heavily on numerical modelling to further understand the underlying physics and identify optimal regimes, but large scale modelling of these scenarios is computationally heavy and requires the efficient use of state-of-the-art petascale supercomputing systems. We discuss the main difficulties involved in running these simulations and the new developments implemented in the OSIRIS framework to address these issues, ranging from multi-dimensional dynamic load balancing and hybrid distributed/shared memory parallelism to the vectorization of the PIC algorithm. We present the results of the OASCR Joule Metric program on the issue of large scale modelling of LWFA, demonstrating speedups of over 1 order of magnitude on the same hardware. Finally, scalability to over ∼10 6 cores and sustained performance over ∼2 P Flops is demonstrated, opening the way for large scale modelling of LWFA scenarios. (paper)

  2. IR wireless cluster synapses of HYDRA very large neural networks

    Science.gov (United States)

    Jannson, Tomasz; Forrester, Thomas

    2008-04-01

    RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

  3. Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.

    Directory of Open Access Journals (Sweden)

    Leonid A Safonov

    Full Text Available A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.

  4. Projective synchronization of time-varying delayed neural network with adaptive scaling factors

    International Nuclear Information System (INIS)

    Ghosh, Dibakar; Banerjee, Santo

    2013-01-01

    Highlights: • Projective synchronization in coupled delayed neural chaotic systems with modulated delay time is introduced. • An adaptive rule for the scaling factors is introduced. • This scheme is highly applicable in secure communication. -- Abstract: In this work, the projective synchronization between two continuous time delayed neural systems with time varying delay is investigated. A sufficient condition for synchronization for the coupled systems with modulated delay is presented analytically with the help of the Krasovskii–Lyapunov approach. The effect of adaptive scaling factors on synchronization are also studied in details. Numerical simulations verify the effectiveness of the analytic results

  5. Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.

    Science.gov (United States)

    Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve

    2011-11-01

    Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

    International Nuclear Information System (INIS)

    Nasser, Hassan; Cessac, Bruno; Marre, Olivier

    2013-01-01

    Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles. (paper)

  7. Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

    Science.gov (United States)

    Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

    2015-05-01

    In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

  8. Large-scale pool fires

    Directory of Open Access Journals (Sweden)

    Steinhaus Thomas

    2007-01-01

    Full Text Available A review of research into the burning behavior of large pool fires and fuel spill fires is presented. The features which distinguish such fires from smaller pool fires are mainly associated with the fire dynamics at low source Froude numbers and the radiative interaction with the fire source. In hydrocarbon fires, higher soot levels at increased diameters result in radiation blockage effects around the perimeter of large fire plumes; this yields lower emissive powers and a drastic reduction in the radiative loss fraction; whilst there are simplifying factors with these phenomena, arising from the fact that soot yield can saturate, there are other complications deriving from the intermittency of the behavior, with luminous regions of efficient combustion appearing randomly in the outer surface of the fire according the turbulent fluctuations in the fire plume. Knowledge of the fluid flow instabilities, which lead to the formation of large eddies, is also key to understanding the behavior of large-scale fires. Here modeling tools can be effectively exploited in order to investigate the fluid flow phenomena, including RANS- and LES-based computational fluid dynamics codes. The latter are well-suited to representation of the turbulent motions, but a number of challenges remain with their practical application. Massively-parallel computational resources are likely to be necessary in order to be able to adequately address the complex coupled phenomena to the level of detail that is necessary.

  9. Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

    Directory of Open Access Journals (Sweden)

    Kun Qian

    2014-01-01

    Full Text Available Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.

  10. How the Internet Will Help Large-Scale Assessment Reinvent Itself

    Directory of Open Access Journals (Sweden)

    Randy Elliot Bennett

    2001-02-01

    Full Text Available Large-scale assessment in the United States is undergoing enormous pressure to change. That pressure stems from many causes. Depending upon the type of test, the issues precipitating change include an outmoded cognitive-scientific basis for test design; a mismatch with curriculum; the differential performance of population groups; a lack of information to help individuals improve; and inefficiency. These issues provide a strong motivation to reconceptualize both the substance and the business of large-scale assessment. At the same time, advances in technology, measurement, and cognitive science are providing the means to make that reconceptualization a reality. The thesis of this paper is that the largest facilitating factor will be technological, in particular the Internet. In the same way that it is already helping to revolutionize commerce, education, and even social interaction, the Internet will help revolutionize the business and substance of large-scale assessment.

  11. Ethics of large-scale change

    OpenAIRE

    Arler, Finn

    2006-01-01

      The subject of this paper is long-term large-scale changes in human society. Some very significant examples of large-scale change are presented: human population growth, human appropriation of land and primary production, the human use of fossil fuels, and climate change. The question is posed, which kind of attitude is appropriate when dealing with large-scale changes like these from an ethical point of view. Three kinds of approaches are discussed: Aldo Leopold's mountain thinking, th...

  12. Formation of outflow channels on Mars: Testing the origin of Reull Vallis in Hesperia Planum by large-scale lava-ice interactions and top-down melting

    Science.gov (United States)

    Cassanelli, James P.; Head, James W.

    2018-05-01

    The Reull Vallis outflow channel is a segmented system of fluvial valleys which originates from the volcanic plains of the Hesperia Planum region of Mars. Explanation of the formation of the Reull Vallis outflow channel by canonical catastrophic groundwater release models faces difficulties with generating sufficient hydraulic head, requiring unreasonably high aquifer permeability, and from limited recharge sources. Recent work has proposed that large-scale lava-ice interactions could serve as an alternative mechanism for outflow channel formation on the basis of predictions of regional ice sheet formation in areas that also underwent extensive contemporaneous volcanic resurfacing. Here we assess in detail the potential formation of outflow channels by large-scale lava-ice interactions through an applied case study of the Reull Vallis outflow channel system, selected for its close association with the effusive volcanic plains of the Hesperia Planum region. We first review the geomorphology of the Reull Vallis system to outline criteria that must be met by the proposed formation mechanism. We then assess local and regional lava heating and loading conditions and generate model predictions for the formation of Reull Vallis to test against the outlined geomorphic criteria. We find that successive events of large-scale lava-ice interactions that melt ice deposits, which then undergo re-deposition due to climatic mechanisms, best explains the observed geomorphic criteria, offering improvements over previously proposed formation models, particularly in the ability to supply adequate volumes of water.

  13. Cardiorespiratory interactions in neural circulatory control in humans.

    Science.gov (United States)

    Shamsuzzaman, A S; Somers, V K

    2001-06-01

    The reflex mechanisms and interactions described in this overview provide some explanation for the range of neural circulatory responses evident during changes in breathing. The effects described represent the integrated responses to activation of several reflex mechanisms, including peripheral and central chemoreflexes, arterial baroreflexes, pulmonary stretch receptors, and ventricular mechanoreceptors. These interactions occur on a dynamic basis and the transfer characteristics of any single interaction are, in all likelihood, also highly dynamic. Nevertheless, it is only by attempting to understand individual reflexes and their modulating influences that a more thorough understanding of the responses to complex phenomena such as hyperventilation, apnea, and obstructive sleep apnea can be better understood.

  14. Progress in Long Scale Length Laser-Plasma Interactions

    International Nuclear Information System (INIS)

    Glenzer, S H; Arnold, P; Bardsley, G; Berger, R L; Bonanno, G; Borger, T; Bower, D E; Bowers, M; Bryant, R; Buckman, S.; Burkhart, S C; Campbell, K; Chrisp, M P; Cohen, B I; Constantin, G; Cooper, F; Cox, J; Dewald, E; Divol, L; Dixit, S; Duncan, J; Eder, D; Edwards, J; Erbert, G; Felker, B; Fornes, J; Frieders, G; Froula, D H; Gardner, S D; Gates, C; Gonzalez, M; Grace, S; Gregori, G; Greenwood, A; Griffith, R; Hall, T; Hammel, B A; Haynam, C; Heestand, G; Henesian, M; Hermes, G; Hinkel, D; Holder, J; Holdner, F; Holtmeier, G; Hsing, W; Huber, S; James, T; Johnson, S; Jones, O S; Kalantar, D; Kamperschroer, J H; Kauffman, R; Kelleher, T; Knight, J; Kirkwood, R K; Kruer, W L; Labiak, W; Landen, O L; Langdon, A B; Langer, S; Latray, D; Lee, A; Lee, F D; Lund, D; MacGowan, B; Marshall, S; McBride, J; McCarville, T; McGrew, L; Mackinnon, A J; Mahavandi, S; Manes, K; Marshall, C; Mertens, E; Meezan, N; Miller, G; Montelongo, S; Moody, J D; Moses, E; Munro, D; Murray, J; Neumann, J; Newton, M; Ng, E; Niemann, C; Nikitin, A; Opsahl, P; Padilla, E; Parham, T; Parrish, G; Petty, C; Polk, M; Powell, C; Reinbachs, I; Rekow, V; Rinnert, R; Riordan, B; Rhodes, M.

    2003-01-01

    The first experiments on the National Ignition Facility (NIF) have employed the first four beams to measure propagation and laser backscattering losses in large ignition-size plasmas. Gas-filled targets between 2 mm and 7 mm length have been heated from one side by overlapping the focal spots of the four beams from one quad operated at 351 nm (3ω) with a total intensity of 2 x 10 15 W cm -2 . The targets were filled with 1 atm of CO 2 producing of up to 7 mm long homogeneously heated plasmas with densities of n e = 6 x 10 20 cm -3 and temperatures of T e = 2 keV. The high energy in a NIF quad of beams of 16kJ, illuminating the target from one direction, creates unique conditions for the study of laser plasma interactions at scale lengths not previously accessible. The propagation through the large-scale plasma was measured with a gated x-ray imager that was filtered for 3.5 keV x rays. These data indicate that the beams interact with the full length of this ignition-scale plasma during the last ∼1 ns of the experiment. During that time, the full aperture measurements of the stimulated Brillouin scattering and stimulated Raman scattering show scattering into the four focusing lenses of 6% for the smallest length (∼2 mm). increasing to 12% for ∼7 mm. These results demonstrate the NIF experimental capabilities and further provide a benchmark for three-dimensional modeling of the laser-plasma interactions at ignition-size scale lengths

  15. Grey relational and neural network approach for multi-objective optimization in small scale resistance spot welding of titanium alloy

    Energy Technology Data Exchange (ETDEWEB)

    Wan, Xiaodong; Wang, Yuanxun; Zhao, Dawei [Huazhong University of Science and Technology, Wuhan (China)

    2016-06-15

    The prediction and optimization of weld quality characteristics in small scale resistance spot welding of TC2 titanium alloy were investigated. Grey relational analysis, neural network and genetic algorithm were applied separately. Quality characteristics were selected as nugget diameter, failure load, failure displacement and failure energy. Welding parameters to be optimized were set as electrode force, welding current and welding time. Grey relational analysis was conducted for a rough estimation of the optimum welding parameters. Results showed that welding current played a key role in weld quality improvement. Different back propagation neural network architectures were then arranged to predict multiple quality characteristics. Interaction effects of welding parameters were analyzed with the proposed neural network. Failure load was found more sensitive to the change of welding parameters than nugget diameter. Optimum welding parameters were determined by genetic algorithm. The predicted responses showed good agreement with confirmation experiments.

  16. Large-scale Intelligent Transporation Systems simulation

    Energy Technology Data Exchange (ETDEWEB)

    Ewing, T.; Canfield, T.; Hannebutte, U.; Levine, D.; Tentner, A.

    1995-06-01

    A prototype computer system has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS) capable of running on massively parallel computers and distributed (networked) computer systems. The prototype includes the modelling of instrumented ``smart`` vehicles with in-vehicle navigation units capable of optimal route planning and Traffic Management Centers (TMC). The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces to support human-factors studies. The prototype has been developed on a distributed system of networked UNIX computers but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of our design is that vehicles will be represented by autonomus computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.

  17. Foundational perspectives on causality in large-scale brain networks

    Science.gov (United States)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

    likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

  18. The statistical power to detect cross-scale interactions at macroscales

    Science.gov (United States)

    Wagner, Tyler; Fergus, C. Emi; Stow, Craig A.; Cheruvelil, Kendra S.; Soranno, Patricia A.

    2016-01-01

    Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets.

  19. Optimization of large-scale heterogeneous system-of-systems models.

    Energy Technology Data Exchange (ETDEWEB)

    Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)

    2012-01-01

    Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

  20. Intranasal oxytocin modulates neural functional connectivity during human social interaction.

    Science.gov (United States)

    Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

    2018-02-10

    Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

  1. WebViz:A Web-based Collaborative Interactive Visualization System for large-Scale Data Sets

    Science.gov (United States)

    Yuen, D. A.; McArthur, E.; Weiss, R. M.; Zhou, J.; Yao, B.

    2010-12-01

    WebViz is a web-based application designed to conduct collaborative, interactive visualizations of large data sets for multiple users, allowing researchers situated all over the world to utilize the visualization services offered by the University of Minnesota’s Laboratory for Computational Sciences and Engineering (LCSE). This ongoing project has been built upon over the last 3 1/2 years .The motivation behind WebViz lies primarily with the need to parse through an increasing amount of data produced by the scientific community as a result of larger and faster multicore and massively parallel computers coming to the market, including the use of general purpose GPU computing. WebViz allows these large data sets to be visualized online by anyone with an account. The application allows users to save time and resources by visualizing data ‘on the fly’, wherever he or she may be located. By leveraging AJAX via the Google Web Toolkit (http://code.google.com/webtoolkit/), we are able to provide users with a remote, web portal to LCSE's (http://www.lcse.umn.edu) large-scale interactive visualization system already in place at the University of Minnesota. LCSE’s custom hierarchical volume rendering software provides high resolution visualizations on the order of 15 million pixels and has been employed for visualizing data primarily from simulations in astrophysics to geophysical fluid dynamics . In the current version of WebViz, we have implemented a highly extensible back-end framework built around HTTP "server push" technology. The web application is accessible via a variety of devices including netbooks, iPhones, and other web and javascript-enabled cell phones. Features in the current version include the ability for users to (1) securely login (2) launch multiple visualizations (3) conduct collaborative visualization sessions (4) delegate control aspects of a visualization to others and (5) engage in collaborative chats with other users within the user interface

  2. Biogrid--a microfluidic device for large-scale enzyme-free dissociation of stem cell aggregates.

    Science.gov (United States)

    Wallman, Lars; Åkesson, Elisabet; Ceric, Dario; Andersson, Per Henrik; Day, Kelly; Hovatta, Outi; Falci, Scott; Laurell, Thomas; Sundström, Erik

    2011-10-07

    Culturing stem cells as free-floating aggregates in suspension facilitates large-scale production of cells in closed systems, for clinical use. To comply with GMP standards, the use of substances such as proteolytic enzymes should be avoided. Instead of enzymatic dissociation, the growing cell aggregates may be mechanically cut at passage, but available methods are not compatible with large-scale cell production and hence translation into the clinic becomes a severe bottle-neck. We have developed the Biogrid device, which consists of an array of micrometerscale knife edges, micro-fabricated in silicon, and a manifold in which the microgrid is placed across the central fluid channel. By connecting one side of the Biogrid to a syringe or a pump and the other side to the cell culture, the culture medium with suspended cell aggregates can be aspirated, forcing the aggregates through the microgrid, and ejected back to the cell culture container. Large aggregates are thereby dissociated into smaller fragments while small aggregates pass through the microgrid unaffected. As proof-of-concept, we demonstrate that the Biogrid device can be successfully used for repeated passage of human neural stem/progenitor cells cultured as so-called neurospheres, as well as for passage of suspension cultures of human embryonic stem cells. We also show that human neural stem/progenitor cells tolerate transient pressure changes far exceeding those that will occur in a fluidic system incorporating the Biogrid microgrids. Thus, by using the Biogrid device it is possible to mechanically passage large quantities of cells in suspension cultures in closed fluidic systems, without the use of proteolytic enzymes.

  3. Simple concentration-dependent pair interaction model for large-scale simulations of Fe-Cr alloys

    International Nuclear Information System (INIS)

    Levesque, Maximilien; Martinez, Enrique; Fu, Chu-Chun; Nastar, Maylise; Soisson, Frederic

    2011-01-01

    This work is motivated by the need for large-scale simulations to extract physical information on the iron-chromium system that is a binary model alloy for ferritic steels used or proposed in many nuclear applications. From first-principles calculations and the experimental critical temperature we build a new energetic rigid lattice model based on pair interactions with concentration and temperature dependence. Density functional theory calculations in both norm-conserving and projector augmented-wave approaches have been performed. A thorough comparison of these two different ab initio techniques leads to a robust parametrization of the Fe-Cr Hamiltonian. Mean-field approximations and Monte Carlo calculations are then used to account for temperature effects. The predictions of the model are in agreement with the most recent phase diagram at all temperatures and compositions. The solubility of Cr in Fe below 700 K remains in the range of about 6 to 12%. It reproduces the transition between the ordering and demixing tendency and the spinodal decomposition limits are also in agreement with the values given in the literature.

  4. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

    Directory of Open Access Journals (Sweden)

    Jan Hahne

    2017-05-01

    Full Text Available Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

  5. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

    Science.gov (United States)

    Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

    2017-01-01

    Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

  6. Analysis of ground response data at Lotung large-scale soil- structure interaction experiment site

    International Nuclear Information System (INIS)

    Chang, C.Y.; Mok, C.M.; Power, M.S.

    1991-12-01

    The Electric Power Research Institute (EPRI), in cooperation with the Taiwan Power Company (TPC), constructed two models (1/4-scale and 1/2-scale) of a nuclear plant containment structure at a site in Lotung (Tang, 1987), a seismically active region in northeast Taiwan. The models were constructed to gather data for the evaluation and validation of soil-structure interaction (SSI) analysis methodologies. Extensive instrumentation was deployed to record both structural and ground responses at the site during earthquakes. The experiment is generally referred to as the Lotung Large-Scale Seismic Test (LSST). As part of the LSST, two downhole arrays were installed at the site to record ground motions at depths as well as at the ground surface. Structural response and ground response have been recorded for a number of earthquakes (i.e. a total of 18 earthquakes in the period of October 1985 through November 1986) at the LSST site since the completion of the installation of the downhole instruments in October 1985. These data include those from earthquakes having magnitudes ranging from M L 4.5 to M L 7.0 and epicentral distances range from 4.7 km to 77.7 km. Peak ground surface accelerations range from 0.03 g to 0.21 g for the horizontal component and from 0.01 g to 0.20 g for the vertical component. The objectives of the study were: (1) to obtain empirical data on variations of earthquake ground motion with depth; (2) to examine field evidence of nonlinear soil response due to earthquake shaking and to determine the degree of soil nonlinearity; (3) to assess the ability of ground response analysis techniques including techniques to approximate nonlinear soil response to estimate ground motions due to earthquake shaking; and (4) to analyze earth pressures recorded beneath the basemat and on the side wall of the 1/4 scale model structure during selected earthquakes

  7. Unparticle self-interactions at the Large Hadron Collider

    International Nuclear Information System (INIS)

    Bergstroem, Johannes; Ohlsson, Tommy

    2009-01-01

    We investigate the effect of unparticle self-interactions at the Large Hadron Collider (LHC). Especially, we discuss the three-point correlation function, which is determined by conformal symmetry up to a constant, and study its relation to processes with four-particle final states. These processes could be used as a favorable way to look for unparticle physics, and for weak enough couplings to the standard model, even the only way. We find updated upper bounds on the cross sections for unparticle-mediated 4γ final states at the LHC and novel upper bounds for the corresponding 2γ2l and 4l final states. The size of the allowed cross sections obtained are comparably large for large values of the scaling dimension of the unparticle sector, but they decrease with decreasing values of this parameter. In addition, we present relevant distributions for the different final states, enabling the possible identification of the unparticle scaling dimension if there was to be a large number of events of such final states at the LHC.

  8. A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

    Science.gov (United States)

    Milne, Roger L; Herranz, Jesús; Michailidou, Kyriaki; Dennis, Joe; Tyrer, Jonathan P; Zamora, M Pilar; Arias-Perez, José Ignacio; González-Neira, Anna; Pita, Guillermo; Alonso, M Rosario; Wang, Qin; Bolla, Manjeet K; Czene, Kamila; Eriksson, Mikael; Humphreys, Keith; Darabi, Hatef; Li, Jingmei; Anton-Culver, Hoda; Neuhausen, Susan L; Ziogas, Argyrios; Clarke, Christina A; Hopper, John L; Dite, Gillian S; Apicella, Carmel; Southey, Melissa C; Chenevix-Trench, Georgia; Swerdlow, Anthony; Ashworth, Alan; Orr, Nicholas; Schoemaker, Minouk; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Mulligan, Anna Marie; Bojesen, Stig E; Nordestgaard, Børge G; Flyger, Henrik; Nevanlinna, Heli; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Wang, Xianshu; Olson, Janet E; Vachon, Celine; Purrington, Kristen; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Dunning, Alison M; Shah, Mitul; Guénel, Pascal; Truong, Thérèse; Sanchez, Marie; Mulot, Claire; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Lindblom, Annika; Margolin, Sara; Hooning, Maartje J; Hollestelle, Antoinette; Collée, J Margriet; Jager, Agnes; Cox, Angela; Brock, Ian W; Reed, Malcolm W R; Devilee, Peter; Tollenaar, Robert A E M; Seynaeve, Caroline; Haiman, Christopher A; Henderson, Brian E; Schumacher, Fredrick; Le Marchand, Loic; Simard, Jacques; Dumont, Martine; Soucy, Penny; Dörk, Thilo; Bogdanova, Natalia V; Hamann, Ute; Försti, Asta; Rüdiger, Thomas; Ulmer, Hans-Ulrich; Fasching, Peter A; Häberle, Lothar; Ekici, Arif B; Beckmann, Matthias W; Fletcher, Olivia; Johnson, Nichola; dos Santos Silva, Isabel; Peto, Julian; Radice, Paolo; Peterlongo, Paolo; Peissel, Bernard; Mariani, Paolo; Giles, Graham G; Severi, Gianluca; Baglietto, Laura; Sawyer, Elinor; Tomlinson, Ian; Kerin, Michael; Miller, Nicola; Marme, Federik; Burwinkel, Barbara; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Lambrechts, Diether; Yesilyurt, Betul T; Floris, Giuseppe; Leunen, Karin; Alnæs, Grethe Grenaker; Kristensen, Vessela; Børresen-Dale, Anne-Lise; García-Closas, Montserrat; Chanock, Stephen J; Lissowska, Jolanta; Figueroa, Jonine D; Schmidt, Marjanka K; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Couch, Fergus J; Toland, Amanda E; Yannoukakos, Drakoulis; Pharoah, Paul D P; Hall, Per; Benítez, Javier; Malats, Núria; Easton, Douglas F

    2014-04-01

    Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70,917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46,450 breast cancer cases and 42,461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P 10(-10)). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

  9. On the elementary neural forms of interaction rituals

    DEFF Research Database (Denmark)

    Heinskou, Marie Bruvik; Liebst, Lasse Suonperä

    Randall Collins’ interaction ritual (IR) theory suggests solidarity as neurologically hardwired in the capacity for rhythmic entrainment. Yet, this article suggests that IR theory may benefit from being tied more firmly to recent neurological research, specifically Stephen W. Porges......’ neurophysiological polyvagal theory. IR theory does not sufficiently acknowledge the autonomic nervous system as a system involving a phylogenetically ordered response hierarchy, of which only one subsystem supports prosocial behavior. The ritual ingredients of shared attention and mood may be clarified as part...... of a social engagement system, neurally regulating attention and arousal via brain-face-heart circuits. This allows rhythmic entrainment to be specified as a neural epiphenomenon of the social engagement system. The polyvagal perspective, moreover, challenges IR theory to reconsider the importance...

  10. Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

    Directory of Open Access Journals (Sweden)

    Yongkun Li

    2009-01-01

    Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.

  11. The dynamic brain: from spiking neurons to neural masses and cortical fields.

    Directory of Open Access Journals (Sweden)

    Gustavo Deco

    2008-08-01

    Full Text Available The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space-time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI, electroencephalogram (EEG, and magnetoencephalogram (MEG. Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the

  12. Political consultation and large-scale research

    International Nuclear Information System (INIS)

    Bechmann, G.; Folkers, H.

    1977-01-01

    Large-scale research and policy consulting have an intermediary position between sociological sub-systems. While large-scale research coordinates science, policy, and production, policy consulting coordinates science, policy and political spheres. In this very position, large-scale research and policy consulting lack of institutional guarantees and rational back-ground guarantee which are characteristic for their sociological environment. This large-scale research can neither deal with the production of innovative goods under consideration of rentability, nor can it hope for full recognition by the basis-oriented scientific community. Policy consulting knows neither the competence assignment of the political system to make decisions nor can it judge succesfully by the critical standards of the established social science, at least as far as the present situation is concerned. This intermediary position of large-scale research and policy consulting has, in three points, a consequence supporting the thesis which states that this is a new form of institutionalization of science: These are: 1) external control, 2) the organization form, 3) the theoretical conception of large-scale research and policy consulting. (orig.) [de

  13. Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs

    Science.gov (United States)

    Wang, Limin; Guo, Sheng; Huang, Weilin; Xiong, Yuanjun; Qiao, Yu

    2017-04-01

    Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity. (i) We exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category. (ii) We utilize the knowledge of extra networks to produce a soft label for each image. Then the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7\\%) and SUN397 (72.0\\%). We release the code and models at~\\url{https://github.com/wanglimin/MRCNN-Scene-Recognition}.

  14. Large-scale multimedia modeling applications

    International Nuclear Information System (INIS)

    Droppo, J.G. Jr.; Buck, J.W.; Whelan, G.; Strenge, D.L.; Castleton, K.J.; Gelston, G.M.

    1995-08-01

    Over the past decade, the US Department of Energy (DOE) and other agencies have faced increasing scrutiny for a wide range of environmental issues related to past and current practices. A number of large-scale applications have been undertaken that required analysis of large numbers of potential environmental issues over a wide range of environmental conditions and contaminants. Several of these applications, referred to here as large-scale applications, have addressed long-term public health risks using a holistic approach for assessing impacts from potential waterborne and airborne transport pathways. Multimedia models such as the Multimedia Environmental Pollutant Assessment System (MEPAS) were designed for use in such applications. MEPAS integrates radioactive and hazardous contaminants impact computations for major exposure routes via air, surface water, ground water, and overland flow transport. A number of large-scale applications of MEPAS have been conducted to assess various endpoints for environmental and human health impacts. These applications are described in terms of lessons learned in the development of an effective approach for large-scale applications

  15. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Pedersen, Benjamin Pjedsted; Larsen, Jan

    2009-01-01

    Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction...

  16. Phase transitions in scale-free neural networks: Departure from the standard mean-field universality class

    International Nuclear Information System (INIS)

    Aldana, Maximino; Larralde, Hernan

    2004-01-01

    We investigate the nature of the phase transition from an ordered to a disordered state that occurs in a family of neural network models with noise. These models are closely related to the majority voter model, where a ferromagneticlike interaction between the elements prevails. Each member of the family is distinguished by the network topology, which is determined by the probability distribution of the number of incoming links. We show that for homogeneous random topologies, the phase transition belongs to the standard mean-field universality class, characterized by the order parameter exponent β=1/2. However, for scale-free networks we obtain phase transition exponents ranging from 1/2 to infinity. Furthermore, we show the existence of a phase transition even for values of the scale-free exponent in the interval (1.5,2], where the average network connectivity diverges

  17. The neural basis of deception in strategic interactions.

    Science.gov (United States)

    Volz, Kirsten G; Vogeley, Kai; Tittgemeyer, Marc; von Cramon, D Yves; Sutter, Matthias

    2015-01-01

    Communication based on informational asymmetries abounds in politics, business, and almost any other form of social interaction. Informational asymmetries may create incentives for the better-informed party to exploit her advantage by misrepresenting information. Using a game-theoretic setting, we investigate the neural basis of deception in human interaction. Unlike in most previous fMRI research on deception, the participants decide themselves whether to lie or not. We find activation within the right temporo-parietal junction (rTPJ), the dorsal anterior cingulate cortex (ACC), the (pre)cuneus (CUN), and the anterior frontal gyrus (aFG) when contrasting lying with truth telling. Notably, our design also allows for an investigation of the neural foundations of sophisticated deception through telling the truth-when the sender does not expect the receiver to believe her (true) message. Sophisticated deception triggers activation within the same network as plain lies, i.e., we find activity within the rTPJ, the CUN, and aFG. We take this result to show that brain activation can reveal the sender's veridical intention to deceive others, irrespective of whether in fact the sender utters the factual truth or not.

  18. The Neural Basis of Deception in Strategic Interactions

    Directory of Open Access Journals (Sweden)

    Kirsten G Volz

    2015-02-01

    Full Text Available Communication based on informational asymmetries abounds in politics, business, and almost any other form of social interaction. Informational asymmetries may create incentives for the better-informed party to exploit her advantage by misrepresenting information. Using a game-theoretic setting, we investigate the neural basis of deception in human interaction. Unlike in most previous fMRI research on deception, the participants decide themselves whether to lie or not. We find activation within the right temporo-parietal junction (rTPJ, the dorsal anterior cingulate cortex (ACC, the (precuneus (CUN, and the anterior frontal gyrus (aFG when contrasting lying with truth telling. Notably, our design also allows for an investigation of the neural foundations of sophisticated deception through telling the truth—when the sender does not expect the receiver to believe her (true message. Sophisticated deception triggers activation within the same network as plain lies, i.e., we find activity within the rTPJ, the CUN, and aFG. We take this result to show that brain activation can reveal the sender’s veridical intention to deceive others, irrespective of whether in fact the sender utters the factual truth or not.

  19. NASA's Information Power Grid: Large Scale Distributed Computing and Data Management

    Science.gov (United States)

    Johnston, William E.; Vaziri, Arsi; Hinke, Tom; Tanner, Leigh Ann; Feiereisen, William J.; Thigpen, William; Tang, Harry (Technical Monitor)

    2001-01-01

    Large-scale science and engineering are done through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organizationally dispersed. The overall motivation for Grids is to facilitate the routine interactions of these resources in order to support large-scale science and engineering. Multi-disciplinary simulations provide a good example of a class of applications that are very likely to require aggregation of widely distributed computing, data, and intellectual resources. Such simulations - e.g. whole system aircraft simulation and whole system living cell simulation - require integrating applications and data that are developed by different teams of researchers frequently in different locations. The research team's are the only ones that have the expertise to maintain and improve the simulation code and/or the body of experimental data that drives the simulations. This results in an inherently distributed computing and data management environment.

  20. The networks scale and coupling parameter in synchronization of neural networks with diluted synapses

    International Nuclear Information System (INIS)

    Li Yanlong; Ma Jun; Chen Yuhong; Xu Wenke; Wang Yinghai

    2008-01-01

    In this paper the influence of the networks scale on the coupling parameter in the synchronization of neural networks with diluted synapses is investigated. Using numerical simulations, an exponential decay form is observed in the extreme case of global coupling among networks and full connection in each network; the larger linked degree becomes, the larger critical coupling intensity becomes; and the oscillation phenomena in the relationship of critical coupling intensity and the number of neural networks layers in the case of small-scale networks are found

  1. Decentralized Large-Scale Power Balancing

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad

    2013-01-01

    problem is formulated as a centralized large-scale optimization problem but is then decomposed into smaller subproblems that are solved locally by each unit connected to an aggregator. For large-scale systems the method is faster than solving the full problem and can be distributed to include an arbitrary...

  2. Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

    Science.gov (United States)

    Si, Wenjie; Dong, Xunde; Yang, Feifei

    2018-03-01

    This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Automating large-scale reactor systems

    International Nuclear Information System (INIS)

    Kisner, R.A.

    1985-01-01

    This paper conveys a philosophy for developing automated large-scale control systems that behave in an integrated, intelligent, flexible manner. Methods for operating large-scale systems under varying degrees of equipment degradation are discussed, and a design approach that separates the effort into phases is suggested. 5 refs., 1 fig

  4. Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

    Science.gov (United States)

    McClelland, James L

    2013-01-01

    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

  5. Investigation of wake interaction using full-scale lidar measurements and large eddy simulation

    DEFF Research Database (Denmark)

    Machefaux, Ewan; Larsen, Gunner Chr.; Troldborg, Niels

    2016-01-01

    dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation...

  6. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Science.gov (United States)

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  7. Real-world-time simulation of memory consolidation in a large-scale cerebellar model

    Directory of Open Access Journals (Sweden)

    Masato eGosui

    2016-03-01

    Full Text Available We report development of a large-scale spiking network model of thecerebellum composed of more than 1 million neurons. The model isimplemented on graphics processing units (GPUs, which are dedicatedhardware for parallel computing. Using 4 GPUs simultaneously, we achieve realtime simulation, in which computer simulation ofcerebellar activity for 1 sec completes within 1 sec in thereal-world time, with temporal resolution of 1 msec.This allows us to carry out a very long-term computer simulationof cerebellar activity in a practical time with millisecond temporalresolution. Using the model, we carry out computer simulationof long-term gain adaptation of optokinetic response (OKR eye movementsfor 5 days aimed to study the neural mechanisms of posttraining memoryconsolidation. The simulation results are consistent with animal experimentsand our theory of posttraining memory consolidation. These resultssuggest that realtime computing provides a useful means to studya very slow neural process such as memory consolidation in the brain.

  8. Comparison of vibration test results for Atucha II NPP and large scale concrete block models

    International Nuclear Information System (INIS)

    Iizuka, S.; Konno, T.; Prato, C.A.

    2001-01-01

    In order to study the soil structure interaction of reactor building that could be constructed on a Quaternary soil, a comparison study of the soil structure interaction springs was performed between full scale vibration test results of Atucha II NPP and vibration test results of large scale concrete block models constructed on Quaternary soil. This comparison study provides a case data of soil structure interaction springs on Quaternary soil with different foundation size and stiffness. (author)

  9. GPU-based large-scale visualization

    KAUST Repository

    Hadwiger, Markus

    2013-11-19

    Recent advances in image and volume acquisition as well as computational advances in simulation have led to an explosion of the amount of data that must be visualized and analyzed. Modern techniques combine the parallel processing power of GPUs with out-of-core methods and data streaming to enable the interactive visualization of giga- and terabytes of image and volume data. A major enabler for interactivity is making both the computational and the visualization effort proportional to the amount of data that is actually visible on screen, decoupling it from the full data size. This leads to powerful display-aware multi-resolution techniques that enable the visualization of data of almost arbitrary size. The course consists of two major parts: An introductory part that progresses from fundamentals to modern techniques, and a more advanced part that discusses details of ray-guided volume rendering, novel data structures for display-aware visualization and processing, and the remote visualization of large online data collections. You will learn how to develop efficient GPU data structures and large-scale visualizations, implement out-of-core strategies and concepts such as virtual texturing that have only been employed recently, as well as how to use modern multi-resolution representations. These approaches reduce the GPU memory requirements of extremely large data to a working set size that fits into current GPUs. You will learn how to perform ray-casting of volume data of almost arbitrary size and how to render and process gigapixel images using scalable, display-aware techniques. We will describe custom virtual texturing architectures as well as recent hardware developments in this area. We will also describe client/server systems for distributed visualization, on-demand data processing and streaming, and remote visualization. We will describe implementations using OpenGL as well as CUDA, exploiting parallelism on GPUs combined with additional asynchronous

  10. The Long Tail Issue in Large Scale Deployment of Personal Informatics

    DEFF Research Database (Denmark)

    Cuttone, Andrea; Larsen, Jakob Eg

    2014-01-01

    We describe the challenges and the open questions arising during the design and deployment of SensibleJournal, a mobile personal informatics system with interactive visualizations of mobility and social interactions based on data acquired from embedded smartphone sensors. The SensibleJournal system...... was evaluated in a large scale (N=136) mobile sensing field study. We report issues in deployment, limitations in user engagement and uptake, and the challenges in measuring the effect of the system....

  11. Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

    Directory of Open Access Journals (Sweden)

    Richard P Mann

    2012-01-01

    Full Text Available Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis. We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.

  12. Efficient algorithms for collaborative decision making for large scale settings

    DEFF Research Database (Denmark)

    Assent, Ira

    2011-01-01

    to bring about more effective and more efficient retrieval systems that support the users' decision making process. We sketch promising research directions for more efficient algorithms for collaborative decision making, especially for large scale systems.......Collaborative decision making is a successful approach in settings where data analysis and querying can be done interactively. In large scale systems with huge data volumes or many users, collaboration is often hindered by impractical runtimes. Existing work on improving collaboration focuses...... on avoiding redundancy for users working on the same task. While this improves the effectiveness of the user work process, the underlying query processing engine is typically considered a "black box" and left unchanged. Research in multiple query processing, on the other hand, ignores the application...

  13. Sequence memory based on coherent spin-interaction neural networks.

    Science.gov (United States)

    Xia, Min; Wong, W K; Wang, Zhijie

    2014-12-01

    Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

  14. The Software Reliability of Large Scale Integration Circuit and Very Large Scale Integration Circuit

    OpenAIRE

    Artem Ganiyev; Jan Vitasek

    2010-01-01

    This article describes evaluation method of faultless function of large scale integration circuits (LSI) and very large scale integration circuits (VLSI). In the article there is a comparative analysis of factors which determine faultless of integrated circuits, analysis of already existing methods and model of faultless function evaluation of LSI and VLSI. The main part describes a proposed algorithm and program for analysis of fault rate in LSI and VLSI circuits.

  15. Blackthorn: Large-Scale Interactive Multimodal Learning

    DEFF Research Database (Denmark)

    Zahálka, Jan; Rudinac, Stevan; Jónsson, Björn Thór

    2018-01-01

    learning process. The Ratio-64 data representation introduced in this work only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64- compressed data directly, greatly reducing...... outperforming the baseline with respect to the relevance of results: it vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset...

  16. Endogenous and Exogenous Attention Shifts are Mediated by the Same Large-Scale Neural Network.

    NARCIS (Netherlands)

    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

  17. Large-scale neural networks and the lateralization of motivation and emotion.

    Science.gov (United States)

    Tops, Mattie; Quirin, Markus; Boksem, Maarten A S; Koole, Sander L

    2017-09-01

    Several lines of research in animals and humans converge on the distinction between two basic large-scale brain networks of self-regulation, giving rise to predictive and reactive control systems (PARCS). Predictive (internally-driven) and reactive (externally-guided) control are supported by dorsal versus ventral corticolimbic systems, respectively. Based on extant empirical evidence, we demonstrate how the PARCS produce frontal laterality effects in emotion and motivation. In addition, we explain how this framework gives rise to individual differences in appraising and coping with challenges. PARCS theory integrates separate fields of research, such as research on the motivational correlates of affect, EEG frontal alpha power asymmetry and implicit affective priming effects on cardiovascular indicators of effort during cognitive task performance. Across these different paradigms, converging evidence points to a qualitative motivational division between, on the one hand, angry and happy emotions, and, on the other hand, sad and fearful emotions. PARCS suggests that those two pairs of emotions are associated with predictive and reactive control, respectively. PARCS theory may thus generate important new insights on the motivational and emotional dynamics that drive autonomic and homeostatic control processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Large-scale exact diagonalizations reveal low-momentum scales of nuclei

    Science.gov (United States)

    Forssén, C.; Carlsson, B. D.; Johansson, H. T.; Sääf, D.; Bansal, A.; Hagen, G.; Papenbrock, T.

    2018-03-01

    Ab initio methods aim to solve the nuclear many-body problem with controlled approximations. Virtually exact numerical solutions for realistic interactions can only be obtained for certain special cases such as few-nucleon systems. Here we extend the reach of exact diagonalization methods to handle model spaces with dimension exceeding 1010 on a single compute node. This allows us to perform no-core shell model (NCSM) calculations for 6Li in model spaces up to Nmax=22 and to reveal the 4He+d halo structure of this nucleus. Still, the use of a finite harmonic-oscillator basis implies truncations in both infrared (IR) and ultraviolet (UV) length scales. These truncations impose finite-size corrections on observables computed in this basis. We perform IR extrapolations of energies and radii computed in the NCSM and with the coupled-cluster method at several fixed UV cutoffs. It is shown that this strategy enables information gain also from data that is not fully UV converged. IR extrapolations improve the accuracy of relevant bound-state observables for a range of UV cutoffs, thus making them profitable tools. We relate the momentum scale that governs the exponential IR convergence to the threshold energy for the first open decay channel. Using large-scale NCSM calculations we numerically verify this small-momentum scale of finite nuclei.

  19. A Web-based Multi-user Interactive Visualization System For Large-Scale Computing Using Google Web Toolkit Technology

    Science.gov (United States)

    Weiss, R. M.; McLane, J. C.; Yuen, D. A.; Wang, S.

    2009-12-01

    We have created a web-based, interactive system for multi-user collaborative visualization of large data sets (on the order of terabytes) that allows users in geographically disparate locations to simultaneous and collectively visualize large data sets over the Internet. By leveraging asynchronous java and XML (AJAX) web development paradigms via the Google Web Toolkit (http://code.google.com/webtoolkit/), we are able to provide remote, web-based users a web portal to LCSE's (http://www.lcse.umn.edu) large-scale interactive visualization system already in place at the University of Minnesota that provides high resolution visualizations to the order of 15 million pixels by Megan Damon. In the current version of our software, we have implemented a new, highly extensible back-end framework built around HTTP "server push" technology to provide a rich collaborative environment and a smooth end-user experience. Furthermore, the web application is accessible via a variety of devices including netbooks, iPhones, and other web- and javascript-enabled cell phones. New features in the current version include: the ability for (1) users to launch multiple visualizations, (2) a user to invite one or more other users to view their visualization in real-time (multiple observers), (3) users to delegate control aspects of the visualization to others (multiple controllers) , and (4) engage in collaborative chat and instant messaging with other users within the user interface of the web application. We will explain choices made regarding implementation, overall system architecture and method of operation, and the benefits of an extensible, modular design. We will also discuss future goals, features, and our plans for increasing scalability of the system which includes a discussion of the benefits potentially afforded us by a migration of server-side components to the Google Application Engine (http://code.google.com/appengine/).

  20. Phylogenetic distribution of large-scale genome patchiness

    Directory of Open Access Journals (Sweden)

    Hackenberg Michael

    2008-04-01

    Full Text Available Abstract Background The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. Results The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris, birds (Gallus gallus, fishes (Danio rerio, invertebrates (Drosophila melanogaster and Caenorhabditis elegans, plants (Arabidopsis thaliana and yeasts (Saccharomyces cerevisiae. We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. Conclusion Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level.

  1. Managing large-scale models: DBS

    International Nuclear Information System (INIS)

    1981-05-01

    A set of fundamental management tools for developing and operating a large scale model and data base system is presented. Based on experience in operating and developing a large scale computerized system, the only reasonable way to gain strong management control of such a system is to implement appropriate controls and procedures. Chapter I discusses the purpose of the book. Chapter II classifies a broad range of generic management problems into three groups: documentation, operations, and maintenance. First, system problems are identified then solutions for gaining management control are disucssed. Chapters III, IV, and V present practical methods for dealing with these problems. These methods were developed for managing SEAS but have general application for large scale models and data bases

  2. Large Scale Self-Organizing Information Distribution System

    National Research Council Canada - National Science Library

    Low, Steven

    2005-01-01

    This project investigates issues in "large-scale" networks. Here "large-scale" refers to networks with large number of high capacity nodes and transmission links, and shared by a large number of users...

  3. High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

    Science.gov (United States)

    Andras, Peter

    2018-02-01

    Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

  4. Large scale structure and baryogenesis

    International Nuclear Information System (INIS)

    Kirilova, D.P.; Chizhov, M.V.

    2001-08-01

    We discuss a possible connection between the large scale structure formation and the baryogenesis in the universe. An update review of the observational indications for the presence of a very large scale 120h -1 Mpc in the distribution of the visible matter of the universe is provided. The possibility to generate a periodic distribution with the characteristic scale 120h -1 Mpc through a mechanism producing quasi-periodic baryon density perturbations during inflationary stage, is discussed. The evolution of the baryon charge density distribution is explored in the framework of a low temperature boson condensate baryogenesis scenario. Both the observed very large scale of a the visible matter distribution in the universe and the observed baryon asymmetry value could naturally appear as a result of the evolution of a complex scalar field condensate, formed at the inflationary stage. Moreover, for some model's parameters a natural separation of matter superclusters from antimatter ones can be achieved. (author)

  5. Automatic management software for large-scale cluster system

    International Nuclear Information System (INIS)

    Weng Yunjian; Chinese Academy of Sciences, Beijing; Sun Gongxing

    2007-01-01

    At present, the large-scale cluster system faces to the difficult management. For example the manager has large work load. It needs to cost much time on the management and the maintenance of large-scale cluster system. The nodes in large-scale cluster system are very easy to be chaotic. Thousands of nodes are put in big rooms so that some managers are very easy to make the confusion with machines. How do effectively carry on accurate management under the large-scale cluster system? The article introduces ELFms in the large-scale cluster system. Furthermore, it is proposed to realize the large-scale cluster system automatic management. (authors)

  6. A Large-Scale Study of Surrogate Physicality and Gesturing on Human–Surrogate Interactions in a Public Space

    Directory of Open Access Journals (Sweden)

    Kangsoo Kim

    2017-07-01

    Full Text Available Technological human surrogates, including robotic and virtual humans, have been popularly used in various scenarios, including training, education, and entertainment. Prior research has investigated the effects of the surrogate’s physicality and gesturing in human perceptions and social influence of the surrogate. However, those studies have been carried out in research laboratories, where the participants were aware that it was an experiment, and the participant demographics are typically relatively narrow—e.g., college students. In this paper, we describe and share results from a large-scale exploratory user study involving 7,685 people in a public space, where they were unaware of the experimental nature of the setting, to investigate the effects of surrogate physicality and gesturing on their behavior during human–surrogate interactions. We evaluate human behaviors using several variables, such as proactivity and reactivity, and proximity. We have identified several interesting phenomena that could lead to hypotheses developed as part of future hypothesis-based studies. Based on the measurements of the variables, we believe people are more likely to be engaged in a human–surrogate interaction when the surrogate is physically present, but movements and gesturing with its body parts have not shown the expected benefits for the interaction engagement. Regarding the demographics of the people in the study, we found higher overall engagement for females than males, and higher reactivity for younger than older people. We discuss implications for practitioners aiming to design a technological surrogate that will directly interact with real humans.

  7. Wnt/Yes-Associated Protein Interactions During Neural Tissue Patterning of Human Induced Pluripotent Stem Cells.

    Science.gov (United States)

    Bejoy, Julie; Song, Liqing; Zhou, Yi; Li, Yan

    2018-04-01

    Human induced pluripotent stem cells (hiPSCs) have special ability to self-assemble into neural spheroids or mini-brain-like structures. During the self-assembly process, Wnt signaling plays an important role in regional patterning and establishing positional identity of hiPSC-derived neural progenitors. Recently, the role of Wnt signaling in regulating Yes-associated protein (YAP) expression (nuclear or cytoplasmic), the pivotal regulator during organ growth and tissue generation, has attracted increasing interests. However, the interactions between Wnt and YAP expression for neural lineage commitment of hiPSCs remain poorly explored. The objective of this study is to investigate the effects of Wnt signaling and YAP expression on the cellular population in three-dimensional (3D) neural spheroids derived from hiPSCs. In this study, Wnt signaling was activated using CHIR99021 for 3D neural spheroids derived from human iPSK3 cells through embryoid body formation. Our results indicate that Wnt activation induces nuclear localization of YAP and upregulates the expression of HOXB4, the marker for hindbrain/spinal cord. By contrast, the cells exhibit more rostral forebrain neural identity (expression of TBR1) without Wnt activation. Cytochalasin D was then used to induce cytoplasmic YAP and the results showed the decreased HOXB4 expression. In addition, the incorporation of microparticles in the neural spheroids was investigated for the perturbation of neural patterning. This study may indicate the bidirectional interactions of Wnt signaling and YAP expression during neural tissue patterning, which have the significance in neurological disease modeling, drug screening, and neural tissue regeneration.

  8. The dutch social interaction anxiety scale and the social phobia scale: reliability, validity, and clinical utility.

    Science.gov (United States)

    de Beurs, Edwin; Tielen, Deirdre; Wollmann, Lisa

    2014-01-01

    The social interaction anxiety scale (SIAS) and the social phobia scale (SPS) assess anxiety in social interactions and fear of scrutiny by others. This study examines the psychometric properties of the Dutch versions of the SIAS and SPS using data from a large group of patients with social phobia and a community-based sample. Confirmatory factor analysis revealed that the SIAS is unidimensional, whereas the SPS is comprised of three subscales. The internal consistency of the scales and subscales was good. The concurrent and discriminant validity was supported and the scales were well able to discriminate between patients and community-based respondents. Cut-off values with excellent sensitivity and specificity are presented. Of all self-report measures included, the SPS was the most sensitive for treatment effects. Normative data are provided which can be used to assess whether clinically significant change has occurred in individual patients.

  9. The Dutch Social Interaction Anxiety Scale and the Social Phobia Scale: Reliability, Validity, and Clinical Utility

    Directory of Open Access Journals (Sweden)

    Edwin de Beurs

    2014-01-01

    Full Text Available The social interaction anxiety scale (SIAS and the social phobia scale (SPS assess anxiety in social interactions and fear of scrutiny by others. This study examines the psychometric properties of the Dutch versions of the SIAS and SPS using data from a large group of patients with social phobia and a community-based sample. Confirmatory factor analysis revealed that the SIAS is unidimensional, whereas the SPS is comprised of three subscales. The internal consistency of the scales and subscales was good. The concurrent and discriminant validity was supported and the scales were well able to discriminate between patients and community-based respondents. Cut-off values with excellent sensitivity and specificity are presented. Of all self-report measures included, the SPS was the most sensitive for treatment effects. Normative data are provided which can be used to assess whether clinically significant change has occurred in individual patients.

  10. Scale interaction and arrangement in a turbulent boundary layer perturbed by a wall-mounted cylindrical element

    Science.gov (United States)

    Tang, Zhanqi; Jiang, Nan

    2018-05-01

    This study reports the modifications of scale interaction and arrangement in a turbulent boundary layer perturbed by a wall-mounted circular cylinder. Hot-wire measurements were executed at multiple streamwise and wall-normal wise locations downstream of the cylindrical element. The streamwise fluctuating signals were decomposed into large-, small-, and dissipative-scale signatures by corresponding cutoff filters. The scale interaction under the cylindrical perturbation was elaborated by comparing the small- and dissipative-scale amplitude/frequency modulation effects downstream of the cylinder element with the results observed in the unperturbed case. It was obtained that the large-scale fluctuations perform a stronger amplitude modulation on both the small and dissipative scales in the near-wall region. At the wall-normal positions of the cylinder height, the small-scale amplitude modulation coefficients are redistributed by the cylinder wake. The similar observation was noted in small-scale frequency modulation; however, the dissipative-scale frequency modulation seems to be independent of the cylindrical perturbation. The phase-relationship observation indicated that the cylindrical perturbation shortens the time shifts between both the small- and dissipative-scale variations (amplitude and frequency) and large-scale fluctuations. Then, the integral time scale dependence of the phase-relationship between the small/dissipative scales and large scales was also discussed. Furthermore, the discrepancy of small- and dissipative-scale time shifts relative to the large-scale motions was examined, which indicates that the small-scale amplitude/frequency leads the dissipative scales.

  11. Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

    International Nuclear Information System (INIS)

    Wang Rubin; Yu Wei

    2005-01-01

    In this paper, we investigate how the population of neuronal oscillators deals with information and the dynamic evolution of neural coding when the external stimulation acts on it. Numerically computing method is used to describe the evolution process of neural coding in three-dimensioned space. The numerical result proves that only the suitable stimulation can change the coupling structure and plasticity of neurons

  12. Neural electrical activity and neural network growth.

    Science.gov (United States)

    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.

  13. Structure of exotic nuclei by large-scale shell model calculations

    International Nuclear Information System (INIS)

    Utsuno, Yutaka; Otsuka, Takaharu; Mizusaki, Takahiro; Honma, Michio

    2006-01-01

    An extensive large-scale shell-model study is conducted for unstable nuclei around N = 20 and N = 28, aiming to investigate how the shell structure evolves from stable to unstable nuclei and affects the nuclear structure. The structure around N = 20 including the disappearance of the magic number is reproduced systematically, exemplified in the systematics of the electromagnetic moments in the Na isotope chain. As a key ingredient dominating the structure/shell evolution in the exotic nuclei from a general viewpoint, we pay attention to the tensor force. Including a proper strength of the tensor force in the effective interaction, we successfully reproduce the proton shell evolution ranging from N = 20 to 28 without any arbitrary modifications in the interaction and predict the ground state of 42Si to contain a large deformed component

  14. Topographically Engineered Large Scale Nanostructures for Plasmonic Biosensing

    Science.gov (United States)

    Xiao, Bo; Pradhan, Sangram K.; Santiago, Kevin C.; Rutherford, Gugu N.; Pradhan, Aswini K.

    2016-04-01

    We demonstrate that a nanostructured metal thin film can achieve enhanced transmission efficiency and sharp resonances and use a large-scale and high-throughput nanofabrication technique for the plasmonic structures. The fabrication technique combines the features of nanoimprint and soft lithography to topographically construct metal thin films with nanoscale patterns. Metal nanogratings developed using this method show significantly enhanced optical transmission (up to a one-order-of-magnitude enhancement) and sharp resonances with full width at half maximum (FWHM) of ~15nm in the zero-order transmission using an incoherent white light source. These nanostructures are sensitive to the surrounding environment, and the resonance can shift as the refractive index changes. We derive an analytical method using a spatial Fourier transformation to understand the enhancement phenomenon and the sensing mechanism. The use of real-time monitoring of protein-protein interactions in microfluidic cells integrated with these nanostructures is demonstrated to be effective for biosensing. The perpendicular transmission configuration and large-scale structures provide a feasible platform without sophisticated optical instrumentation to realize label-free surface plasmon resonance (SPR) sensing.

  15. Fuel pin integrity assessment under large scale transients

    International Nuclear Information System (INIS)

    Dutta, B.K.

    2006-01-01

    The integrity of fuel rods under normal, abnormal and accident conditions is an important consideration during fuel design of advanced nuclear reactors. The fuel matrix and the sheath form the first barrier to prevent the release of radioactive materials into the primary coolant. An understanding of the fuel and clad behaviour under different reactor conditions, particularly under the beyond-design-basis accident scenario leading to large scale transients, is always desirable to assess the inherent safety margins in fuel pin design and to plan for the mitigation the consequences of accidents, if any. The severe accident conditions are typically characterized by the energy deposition rates far exceeding the heat removal capability of the reactor coolant system. This may lead to the clad failure due to fission gas pressure at high temperature, large- scale pellet-clad interaction and clad melting. The fuel rod performance is affected by many interdependent complex phenomena involving extremely complex material behaviour. The versatile experimental database available in this area has led to the development of powerful analytical tools to characterize fuel under extreme scenarios

  16. Large scale network-centric distributed systems

    CERN Document Server

    Sarbazi-Azad, Hamid

    2014-01-01

    A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

  17. The Hualien Large-Scale Seismic Test for soil-structure interaction research

    International Nuclear Information System (INIS)

    Tang, H.T.; Stepp, J.C.; Cheng, Y.H.

    1991-01-01

    A Large-Scale Seismic Test (LSST) Program at Hualien, Taiwan, has been initiated with the primary objective of obtaining earthquake-induced SSI data at a stiff soil site having similar prototypical nuclear power plant soil conditions. Preliminary soil boring, geophysical testing and ambient and earthquake-induced ground motion monitoring have been conducted to understand the experiment site conditions. More refined field and laboratory tests will be conducted such as the state-of-the-art freezing sampling technique and the large penetration test (LPT) method to characterize the soil constitutive behavior. The test model to be constructed will be similar to the Lotung model. The instrumentation layout will be designed to provide data for studies of SSI, spatial incoherence, soil stability, foundation uplifting, ground motion wave field and structural response. A consortium consisting of EPRI, Taipower, CRIEPI, TEPCO, CEA, EdF and Framatome has been established to carry out the project. It is envisaged that the Hualien SSI array will be ready to record earthquakes by the middle of 1992. The duration of the recording scheduled for five years. (author)

  18. Large-Scale Outflows in Seyfert Galaxies

    Science.gov (United States)

    Colbert, E. J. M.; Baum, S. A.

    1995-12-01

    \\catcode`\\@=11 \\ialign{m @th#1hfil ##hfil \\crcr#2\\crcr\\sim\\crcr}}} \\catcode`\\@=12 Highly collimated outflows extend out to Mpc scales in many radio-loud active galaxies. In Seyfert galaxies, which are radio-quiet, the outflows extend out to kpc scales and do not appear to be as highly collimated. In order to study the nature of large-scale (>~1 kpc) outflows in Seyferts, we have conducted optical, radio and X-ray surveys of a distance-limited sample of 22 edge-on Seyfert galaxies. Results of the optical emission-line imaging and spectroscopic survey imply that large-scale outflows are present in >~{{1} /{4}} of all Seyferts. The radio (VLA) and X-ray (ROSAT) surveys show that large-scale radio and X-ray emission is present at about the same frequency. Kinetic luminosities of the outflows in Seyferts are comparable to those in starburst-driven superwinds. Large-scale radio sources in Seyferts appear diffuse, but do not resemble radio halos found in some edge-on starburst galaxies (e.g. M82). We discuss the feasibility of the outflows being powered by the active nucleus (e.g. a jet) or a circumnuclear starburst.

  19. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    Full Text Available Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS and Internet of Things (IoT, transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  20. Sinup, a novel Siaz-interacting nuclear protein, modulates neural plate formation in the zebrafish embryos

    International Nuclear Information System (INIS)

    Ro, Hyunju; Won, Minho; Lee, Su-Ui; Kim, Kyoon E.; Huh, Tae-Lin; Kim, Cheol-Hee; Rhee, Myungchull

    2005-01-01

    Siah, the vertebrate homologue of the Drosophila seven in absentia (sina) gene, is well conserved from Drosophila to mammal and involved in ubiquitination and proteasome-dependent degradation of various target proteins. To identify cellular proteins interacting with Siah, we screened a zebrafish cDNA library with zebrafish Siah (Siaz) as bait in a yeast two-hybrid assay. We identified a cDNA encoding a novel protein composed of 145 amino acids and termed it as Sinup (Siaz-interacting-nuclear-protein). Sinup is a novel nuclear protein that binds to the highly conserved C-terminal protein-interacting domain of Siaz both in vivo and in vitro. During development, sinup transcripts are abundant from the one-cell stage to the early blastula and then markedly diminished, suggesting sinup largely exists as maternal transcripts. sinup overexpression induced lateral expansion of the neural plate and in consequence caused ectopic expression of otx-2 and hoxb1b during the late gastrula stage. In addition, the lateral/paraxial expression of wnt8 at the onset of gastrulation is suppressed by the forced expression of sinup while the expression levels of various dorso-ventral markers are unaffected. In contrast, interfering with sinup functions using sinup morpholino oligonucleotides gradually diminished the anterior neuroectoderm from the posterior region, and resulted in compete loss of hindbrain at the 3-somites stage. Our report suggests that sinup expression should be tightly regulated during early embryonic development for the proper neural plate formation

  1. Large scale nuclear structure studies

    International Nuclear Information System (INIS)

    Faessler, A.

    1985-01-01

    Results of large scale nuclear structure studies are reported. The starting point is the Hartree-Fock-Bogoliubov solution with angular momentum and proton and neutron number projection after variation. This model for number and spin projected two-quasiparticle excitations with realistic forces yields in sd-shell nuclei similar good results as the 'exact' shell-model calculations. Here the authors present results for a pf-shell nucleus 46 Ti and results for the A=130 mass region where they studied 58 different nuclei with the same single-particle energies and the same effective force derived from a meson exchange potential. They carried out a Hartree-Fock-Bogoliubov variation after mean field projection in realistic model spaces. In this way, they determine for each yrast state the optimal mean Hartree-Fock-Bogoliubov field. They apply this method to 130 Ce and 128 Ba using the same effective nucleon-nucleon interaction. (Auth.)

  2. HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

    Science.gov (United States)

    Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

    2017-02-09

    In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

  3. Impact of ultralight axion self-interactions on the large scale structure of the Universe

    Science.gov (United States)

    Desjacques, Vincent; Kehagias, Alex; Riotto, Antonio

    2018-01-01

    Ultralight axions have sparked attention because their tiny mass m ˜10-22 eV , which leads to a kiloparsec-scale de Broglie wavelength comparable to the size of a dwarf galaxy, could alleviate the so-called small-scale crisis of massive cold dark matter (CDM) candidates. However, recent analyses of the Lyman-α forest power spectrum set a tight lower bound on their mass of m ≳10-21 eV which makes them much less relevant from an astrophysical point of view. An important caveat to these numerical studies is that they do not take into account self-interactions among ultralight axions. Furthermore, for axions which acquired a mass through nonperturbative effects, this self-interaction is attractive and, therefore, could counteract the quantum "pressure" induced by the strong delocalization of the particles. In this work, we show that even a tiny attractive interaction among ultralight axions can have a significant impact on the stability of cosmic structures at low redshift. After a brief review of known results about solitons in the absence of gravity, we discuss the stability of filamentary and pancakelike solutions when quantum pressure, attractive interactions and gravity are present. The analysis based on 1 degree of freedom, namely the breathing mode, reveals that pancakes are stable, while filaments are unstable if the mass per unit length is larger than a critical value. However, we show that pancakes are unstable against transverse perturbations. We expect this to be true for halos and filaments as well. Instabilities driven by the breathing mode will not be seen in the low column density Lyman-α forest unless the axion decay constant is extremely small, f ≲1013 GeV . Notwithstanding, axion solitonic cores could leave a detectable signature in the Lyman-α forest if the normalization of the unknown axion core—filament mass relation is ˜100 larger than it is for spherical halos. We hope our work motivates future numerical studies of the impact of axion

  4. IP over optical multicasting for large-scale video delivery

    Science.gov (United States)

    Jin, Yaohui; Hu, Weisheng; Sun, Weiqiang; Guo, Wei

    2007-11-01

    In the IPTV systems, multicasting will play a crucial role in the delivery of high-quality video services, which can significantly improve bandwidth efficiency. However, the scalability and the signal quality of current IPTV can barely compete with the existing broadcast digital TV systems since it is difficult to implement large-scale multicasting with end-to-end guaranteed quality of service (QoS) in packet-switched IP network. China 3TNet project aimed to build a high performance broadband trial network to support large-scale concurrent streaming media and interactive multimedia services. The innovative idea of 3TNet is that an automatic switched optical networks (ASON) with the capability of dynamic point-to-multipoint (P2MP) connections replaces the conventional IP multicasting network in the transport core, while the edge remains an IP multicasting network. In this paper, we will introduce the network architecture and discuss challenges in such IP over Optical multicasting for video delivery.

  5. Parallel Index and Query for Large Scale Data Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chou, Jerry; Wu, Kesheng; Ruebel, Oliver; Howison, Mark; Qiang, Ji; Prabhat,; Austin, Brian; Bethel, E. Wes; Ryne, Rob D.; Shoshani, Arie

    2011-07-18

    Modern scientific datasets present numerous data management and analysis challenges. State-of-the-art index and query technologies are critical for facilitating interactive exploration of large datasets, but numerous challenges remain in terms of designing a system for process- ing general scientific datasets. The system needs to be able to run on distributed multi-core platforms, efficiently utilize underlying I/O infrastructure, and scale to massive datasets. We present FastQuery, a novel software framework that address these challenges. FastQuery utilizes a state-of-the-art index and query technology (FastBit) and is designed to process mas- sive datasets on modern supercomputing platforms. We apply FastQuery to processing of a massive 50TB dataset generated by a large scale accelerator modeling code. We demonstrate the scalability of the tool to 11,520 cores. Motivated by the scientific need to search for inter- esting particles in this dataset, we use our framework to reduce search time from hours to tens of seconds.

  6. The diurnal interaction between convection and peninsular-scale forcing over South Florida

    Science.gov (United States)

    Cooper, H. J.; Simpson, J.; Garstang, M.

    1982-01-01

    One of the outstanding problems in modern meterology is that of describing in detail the manner in which larger scales of motion interact with, influence and are influenced by successively smaller scales of motion. The present investigation is concerned with a study of the diurnal evolution of convection, the interaction between the peninsular-scale convergence and convection, and the role of the feedback produced by the cloud-scale downdrafts in the maintenance of the convection. Attention is given to the analysis, the diurnal cycle of the network area-averaged divergence, convective-scale divergence, convective mass transports, and the peninsular scale divergence. The links established in the investigation between the large scale (peninsular), the mesoscale (network), and the convective scale (cloud) are found to be of fundamental importance to the understanding of the initiation, maintenance, and decay of deep precipitating convection and to its theoretical parameterization.

  7. Dark matter self-interactions and small scale structure

    Science.gov (United States)

    Tulin, Sean; Yu, Hai-Bo

    2018-02-01

    We review theories of dark matter (DM) beyond the collisionless paradigm, known as self-interacting dark matter (SIDM), and their observable implications for astrophysical structure in the Universe. Self-interactions are motivated, in part, due to the potential to explain long-standing (and more recent) small scale structure observations that are in tension with collisionless cold DM (CDM) predictions. Simple particle physics models for SIDM can provide a universal explanation for these observations across a wide range of mass scales spanning dwarf galaxies, low and high surface brightness spiral galaxies, and clusters of galaxies. At the same time, SIDM leaves intact the success of ΛCDM cosmology on large scales. This report covers the following topics: (1) small scale structure issues, including the core-cusp problem, the diversity problem for rotation curves, the missing satellites problem, and the too-big-to-fail problem, as well as recent progress in hydrodynamical simulations of galaxy formation; (2) N-body simulations for SIDM, including implications for density profiles, halo shapes, substructure, and the interplay between baryons and self-interactions; (3) semi-analytic Jeans-based methods that provide a complementary approach for connecting particle models with observations; (4) merging systems, such as cluster mergers (e.g., the Bullet Cluster) and minor infalls, along with recent simulation results for mergers; (5) particle physics models, including light mediator models and composite DM models; and (6) complementary probes for SIDM, including indirect and direct detection experiments, particle collider searches, and cosmological observations. We provide a summary and critical look for all current constraints on DM self-interactions and an outline for future directions.

  8. Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

    Directory of Open Access Journals (Sweden)

    Richard P Mann

    Full Text Available Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis. We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture the observed locality of interactions. Traditional self-propelled particle models fail to capture the fine scale dynamics of the system. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics, while maintaining a biologically plausible perceptual range. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.

  9. Neural fields theory and applications

    CERN Document Server

    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 ...

  10. Large Scale Monte Carlo Simulation of Neutrino Interactions Using the Open Science Grid and Commercial Clouds

    International Nuclear Information System (INIS)

    Norman, A.; Boyd, J.; Davies, G.; Flumerfelt, E.; Herner, K.; Mayer, N.; Mhashilhar, P.; Tamsett, M.; Timm, S.

    2015-01-01

    Modern long baseline neutrino experiments like the NOvA experiment at Fermilab, require large scale, compute intensive simulations of their neutrino beam fluxes and backgrounds induced by cosmic rays. The amount of simulation required to keep the systematic uncertainties in the simulation from dominating the final physics results is often 10x to 100x that of the actual detector exposure. For the first physics results from NOvA this has meant the simulation of more than 2 billion cosmic ray events in the far detector and more than 200 million NuMI beam spill simulations. Performing these high statistics levels of simulation have been made possible for NOvA through the use of the Open Science Grid and through large scale runs on commercial clouds like Amazon EC2. We details the challenges in performing large scale simulation in these environments and how the computing infrastructure for the NOvA experiment has been adapted to seamlessly support the running of different simulation and data processing tasks on these resources. (paper)

  11. The intertidal community in West Greenland: Large-scale patterns and small-scale variation on ecosystem dynamics along a climate gradient

    DEFF Research Database (Denmark)

    Thyrring, Jakob; Blicher, Martin; Sejr, Mikael Kristian

    are largely unknown. The West Greenland coast is north - south orientated. This provides an ideal setting to study the impact of climate change on marine species population dynamics and distribution. We investigated the latitudinal changes in the rocky intertidal community along 18° latitudes (59-77°N......) in West Greenland. Using cleared quadrats we quantified patterns in abundance, biomass and species richness in the intertidal zone. We use this data to disentangle patterns in Arctic intertidal communities at different scales. We describe the effects of different environmental drivers and species...... interactions on distribution and dynamics of intertidal species. Our results indicate that changes in distribution and abundance of foundation species can have large effects on the ecosystem. We also show that the importance of small-scale variation may be of same magnitude as large- scale variation. Only...

  12. Large-scale perspective as a challenge

    NARCIS (Netherlands)

    Plomp, M.G.A.

    2012-01-01

    1. Scale forms a challenge for chain researchers: when exactly is something ‘large-scale’? What are the underlying factors (e.g. number of parties, data, objects in the chain, complexity) that determine this? It appears to be a continuum between small- and large-scale, where positioning on that

  13. Neural ensemble communities: Open-source approaches to hardware for large-scale electrophysiology

    Science.gov (United States)

    Siegle, Joshua H.; Hale, Gregory J.; Newman, Jonathan P.; Voigts, Jakob

    2014-01-01

    One often-overlooked factor when selecting a platform for large-scale electrophysiology is whether or not a particular data acquisition system is “open” or “closed”: that is, whether or not the system’s schematics and source code are available to end users. Open systems have a reputation for being difficult to acquire, poorly documented, and hard to maintain. With the arrival of more powerful and compact integrated circuits, rapid prototyping services, and web-based tools for collaborative development, these stereotypes must be reconsidered. We discuss some of the reasons why multichannel extracellular electrophysiology could benefit from open-source approaches and describe examples of successful community-driven tool development within this field. In order to promote the adoption of open-source hardware and to reduce the need for redundant development efforts, we advocate a move toward standardized interfaces that connect each element of the data processing pipeline. This will give researchers the flexibility to modify their tools when necessary, while allowing them to continue to benefit from the high-quality products and expertise provided by commercial vendors. PMID:25528614

  14. Algorithm 896: LSA: Algorithms for Large-Scale Optimization

    Czech Academy of Sciences Publication Activity Database

    Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan

    2009-01-01

    Roč. 36, č. 3 (2009), 16-1-16-29 ISSN 0098-3500 R&D Pro jects: GA AV ČR IAA1030405; GA ČR GP201/06/P397 Institutional research plan: CEZ:AV0Z10300504 Keywords : algorithms * design * large-scale optimization * large-scale nonsmooth optimization * large-scale nonlinear least squares * large-scale nonlinear minimax * large-scale systems of nonlinear equations * sparse pro blems * partially separable pro blems * limited-memory methods * discrete Newton methods * quasi-Newton methods * primal interior-point methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.904, year: 2009

  15. Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

    Science.gov (United States)

    Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

    2017-01-01

    In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

  16. Large-scale matrix-handling subroutines 'ATLAS'

    International Nuclear Information System (INIS)

    Tsunematsu, Toshihide; Takeda, Tatsuoki; Fujita, Keiichi; Matsuura, Toshihiko; Tahara, Nobuo

    1978-03-01

    Subroutine package ''ATLAS'' has been developed for handling large-scale matrices. The package is composed of four kinds of subroutines, i.e., basic arithmetic routines, routines for solving linear simultaneous equations and for solving general eigenvalue problems and utility routines. The subroutines are useful in large scale plasma-fluid simulations. (auth.)

  17. Large scale analysis of signal reachability.

    Science.gov (United States)

    Todor, Andrei; Gabr, Haitham; Dobra, Alin; Kahveci, Tamer

    2014-06-15

    Major disorders, such as leukemia, have been shown to alter the transcription of genes. Understanding how gene regulation is affected by such aberrations is of utmost importance. One promising strategy toward this objective is to compute whether signals can reach to the transcription factors through the transcription regulatory network (TRN). Due to the uncertainty of the regulatory interactions, this is a #P-complete problem and thus solving it for very large TRNs remains to be a challenge. We develop a novel and scalable method to compute the probability that a signal originating at any given set of source genes can arrive at any given set of target genes (i.e., transcription factors) when the topology of the underlying signaling network is uncertain. Our method tackles this problem for large networks while providing a provably accurate result. Our method follows a divide-and-conquer strategy. We break down the given network into a sequence of non-overlapping subnetworks such that reachability can be computed autonomously and sequentially on each subnetwork. We represent each interaction using a small polynomial. The product of these polynomials express different scenarios when a signal can or cannot reach to target genes from the source genes. We introduce polynomial collapsing operators for each subnetwork. These operators reduce the size of the resulting polynomial and thus the computational complexity dramatically. We show that our method scales to entire human regulatory networks in only seconds, while the existing methods fail beyond a few tens of genes and interactions. We demonstrate that our method can successfully characterize key reachability characteristics of the entire transcriptions regulatory networks of patients affected by eight different subtypes of leukemia, as well as those from healthy control samples. All the datasets and code used in this article are available at bioinformatics.cise.ufl.edu/PReach/scalable.htm. © The Author 2014

  18. Large-scale solar heat

    Energy Technology Data Exchange (ETDEWEB)

    Tolonen, J.; Konttinen, P.; Lund, P. [Helsinki Univ. of Technology, Otaniemi (Finland). Dept. of Engineering Physics and Mathematics

    1998-12-31

    In this project a large domestic solar heating system was built and a solar district heating system was modelled and simulated. Objectives were to improve the performance and reduce costs of a large-scale solar heating system. As a result of the project the benefit/cost ratio can be increased by 40 % through dimensioning and optimising the system at the designing stage. (orig.)

  19. Development and analysis of prognostic equations for mesoscale kinetic energy and mesoscale (subgrid scale) fluxes for large-scale atmospheric models

    Science.gov (United States)

    Avissar, Roni; Chen, Fei

    1993-01-01

    Generated by landscape discontinuities (e.g., sea breezes) mesoscale circulation processes are not represented in large-scale atmospheric models (e.g., general circulation models), which have an inappropiate grid-scale resolution. With the assumption that atmospheric variables can be separated into large scale, mesoscale, and turbulent scale, a set of prognostic equations applicable in large-scale atmospheric models for momentum, temperature, moisture, and any other gaseous or aerosol material, which includes both mesoscale and turbulent fluxes is developed. Prognostic equations are also developed for these mesoscale fluxes, which indicate a closure problem and, therefore, require a parameterization. For this purpose, the mean mesoscale kinetic energy (MKE) per unit of mass is used, defined as E-tilde = 0.5 (the mean value of u'(sub i exp 2), where u'(sub i) represents the three Cartesian components of a mesoscale circulation (the angle bracket symbol is the grid-scale, horizontal averaging operator in the large-scale model, and a tilde indicates a corresponding large-scale mean value). A prognostic equation is developed for E-tilde, and an analysis of the different terms of this equation indicates that the mesoscale vertical heat flux, the mesoscale pressure correlation, and the interaction between turbulence and mesoscale perturbations are the major terms that affect the time tendency of E-tilde. A-state-of-the-art mesoscale atmospheric model is used to investigate the relationship between MKE, landscape discontinuities (as characterized by the spatial distribution of heat fluxes at the earth's surface), and mesoscale sensible and latent heat fluxes in the atmosphere. MKE is compared with turbulence kinetic energy to illustrate the importance of mesoscale processes as compared to turbulent processes. This analysis emphasizes the potential use of MKE to bridge between landscape discontinuities and mesoscale fluxes and, therefore, to parameterize mesoscale fluxes

  20. Large-scale urban point cloud labeling and reconstruction

    Science.gov (United States)

    Zhang, Liqiang; Li, Zhuqiang; Li, Anjian; Liu, Fangyu

    2018-04-01

    The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. In this paper, a novel framework is proposed for classification and reconstruction of airborne laser scanning point cloud data. To label point clouds, we present a rectified linear units neural network named ReLu-NN where the rectified linear units (ReLu) instead of the traditional sigmoid are taken as the activation function in order to speed up the convergence. Since the features of the point cloud are sparse, we reduce the number of neurons by the dropout to avoid over-fitting of the training process. The set of feature descriptors for each 3D point is encoded through self-taught learning, and forms a discriminative feature representation which is taken as the input of the ReLu-NN. The segmented building points are consolidated through an edge-aware point set resampling algorithm, and then they are reconstructed into 3D lightweight models using the 2.5D contouring method (Zhou and Neumann, 2010). Compared with deep learning approaches, the ReLu-NN introduced can easily classify unorganized point clouds without rasterizing the data, and it does not need a large number of training samples. Most of the parameters in the network are learned, and thus the intensive parameter tuning cost is significantly reduced. Experimental results on various datasets demonstrate that the proposed framework achieves better performance than other related algorithms in terms of classification accuracy and reconstruction quality.

  1. The genetic etiology of Tourette Syndrome: Large-scale collaborative efforts on the precipice of discovery

    Directory of Open Access Journals (Sweden)

    Marianthi Georgitsi

    2016-08-01

    Full Text Available Gilles de la Tourette Syndrome (TS is a childhood-onset neurodevelopmental disorder that is characterized by multiple motor and phonic tics. It has a complex etiology with multiple genes likely interacting with environmental factors to lead to the onset of symptoms. The genetic basis of the disorder remains elusive;however, multiple resources and large-scale projects are coming together, launching a new era in the field and bringing us on the verge of discovery. The large-scale efforts outlined in this report, are complementary and represent a range of different approaches to the study of disorders with complex inheritance. The Tourette Syndrome Association International Consortium for Genetics (TSAICG has focused on large families, parent-proband trios and cases for large case-control designs such as genomewide association studies (GWAS, copy number variation (CNV scans and exome/genome sequencing. TIC Genetics targets rare, large effect size mutations in simplex trios and multigenerational families. The European Multicentre Tics in Children Study (EMTICS seeks to elucidate gene-environment interactions including the involvement of infection and immune mechanisms in TS etiology. Finally, TS-EUROTRAIN, a Marie Curie Initial Training Network, aims to act as a platform to unify large-scale projects in the field and to educate the next generation of experts. Importantly, these complementary large-scale efforts are joining forces to uncover the full range of genetic variation and environmental risk factors for TS, holding great promise for indentifying definitive TS susceptibility genes and shedding light into the complex pathophysiology of this disorder.

  2. The Genetic Etiology of Tourette Syndrome: Large-Scale Collaborative Efforts on the Precipice of Discovery

    Science.gov (United States)

    Georgitsi, Marianthi; Willsey, A. Jeremy; Mathews, Carol A.; State, Matthew; Scharf, Jeremiah M.; Paschou, Peristera

    2016-01-01

    Gilles de la Tourette Syndrome (TS) is a childhood-onset neurodevelopmental disorder that is characterized by multiple motor and phonic tics. It has a complex etiology with multiple genes likely interacting with environmental factors to lead to the onset of symptoms. The genetic basis of the disorder remains elusive. However, multiple resources and large-scale projects are coming together, launching a new era in the field and bringing us on the verge of discovery. The large-scale efforts outlined in this report are complementary and represent a range of different approaches to the study of disorders with complex inheritance. The Tourette Syndrome Association International Consortium for Genetics (TSAICG) has focused on large families, parent-proband trios and cases for large case-control designs such as genomewide association studies (GWAS), copy number variation (CNV) scans, and exome/genome sequencing. TIC Genetics targets rare, large effect size mutations in simplex trios, and multigenerational families. The European Multicentre Tics in Children Study (EMTICS) seeks to elucidate gene-environment interactions including the involvement of infection and immune mechanisms in TS etiology. Finally, TS-EUROTRAIN, a Marie Curie Initial Training Network, aims to act as a platform to unify large-scale projects in the field and to educate the next generation of experts. Importantly, these complementary large-scale efforts are joining forces to uncover the full range of genetic variation and environmental risk factors for TS, holding great promise for identifying definitive TS susceptibility genes and shedding light into the complex pathophysiology of this disorder. PMID:27536211

  3. Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data.

    Science.gov (United States)

    Brinkmann, Benjamin H; Bower, Mark R; Stengel, Keith A; Worrell, Gregory A; Stead, Matt

    2009-05-30

    The use of large-scale electrophysiology to obtain high spatiotemporal resolution brain recordings (>100 channels) capable of probing the range of neural activity from local field potential oscillations to single-neuron action potentials presents new challenges for data acquisition, storage, and analysis. Our group is currently performing continuous, long-term electrophysiological recordings in human subjects undergoing evaluation for epilepsy surgery using hybrid intracranial electrodes composed of up to 320 micro- and clinical macroelectrode arrays. DC-capable amplifiers, sampling at 32kHz per channel with 18-bits of A/D resolution are capable of resolving extracellular voltages spanning single-neuron action potentials, high frequency oscillations, and high amplitude ultra-slow activity, but this approach generates 3 terabytes of data per day (at 4 bytes per sample) using current data formats. Data compression can provide several practical benefits, but only if data can be compressed and appended to files in real-time in a format that allows random access to data segments of varying size. Here we describe a state-of-the-art, scalable, electrophysiology platform designed for acquisition, compression, encryption, and storage of large-scale data. Data are stored in a file format that incorporates lossless data compression using range-encoded differences, a 32-bit cyclically redundant checksum to ensure data integrity, and 128-bit encryption for protection of patient information.

  4. Probes of large-scale structure in the Universe

    International Nuclear Information System (INIS)

    Suto, Yasushi; Gorski, K.; Juszkiewicz, R.; Silk, J.

    1988-01-01

    Recent progress in observational techniques has made it possible to confront quantitatively various models for the large-scale structure of the Universe with detailed observational data. We develop a general formalism to show that the gravitational instability theory for the origin of large-scale structure is now capable of critically confronting observational results on cosmic microwave background radiation angular anisotropies, large-scale bulk motions and large-scale clumpiness in the galaxy counts. (author)

  5. Large-scale grid management; Storskala Nettforvaltning

    Energy Technology Data Exchange (ETDEWEB)

    Langdal, Bjoern Inge; Eggen, Arnt Ove

    2003-07-01

    The network companies in the Norwegian electricity industry now have to establish a large-scale network management, a concept essentially characterized by (1) broader focus (Broad Band, Multi Utility,...) and (2) bigger units with large networks and more customers. Research done by SINTEF Energy Research shows so far that the approaches within large-scale network management may be structured according to three main challenges: centralization, decentralization and out sourcing. The article is part of a planned series.

  6. Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

    Science.gov (United States)

    Dong, Xianlei; Bollen, Johan

    2015-01-01

    Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

  7. Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

    Directory of Open Access Journals (Sweden)

    Xianlei Dong

    Full Text Available Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

  8. Punishment sustains large-scale cooperation in prestate warfare

    Science.gov (United States)

    Mathew, Sarah; Boyd, Robert

    2011-01-01

    Understanding cooperation and punishment in small-scale societies is crucial for explaining the origins of human cooperation. We studied warfare among the Turkana, a politically uncentralized, egalitarian, nomadic pastoral society in East Africa. Based on a representative sample of 88 recent raids, we show that the Turkana sustain costly cooperation in combat at a remarkably large scale, at least in part, through punishment of free-riders. Raiding parties comprised several hundred warriors and participants are not kin or day-to-day interactants. Warriors incur substantial risk of death and produce collective benefits. Cowardice and desertions occur, and are punished by community-imposed sanctions, including collective corporal punishment and fines. Furthermore, Turkana norms governing warfare benefit the ethnolinguistic group, a population of a half-million people, at the expense of smaller social groupings. These results challenge current views that punishment is unimportant in small-scale societies and that human cooperation evolved in small groups of kin and familiar individuals. Instead, these results suggest that cooperation at the larger scale of ethnolinguistic units enforced by third-party sanctions could have a deep evolutionary history in the human species. PMID:21670285

  9. Japanese large-scale interferometers

    CERN Document Server

    Kuroda, K; Miyoki, S; Ishizuka, H; Taylor, C T; Yamamoto, K; Miyakawa, O; Fujimoto, M K; Kawamura, S; Takahashi, R; Yamazaki, T; Arai, K; Tatsumi, D; Ueda, A; Fukushima, M; Sato, S; Shintomi, T; Yamamoto, A; Suzuki, T; Saitô, Y; Haruyama, T; Sato, N; Higashi, Y; Uchiyama, T; Tomaru, T; Tsubono, K; Ando, M; Takamori, A; Numata, K; Ueda, K I; Yoneda, H; Nakagawa, K; Musha, M; Mio, N; Moriwaki, S; Somiya, K; Araya, A; Kanda, N; Telada, S; Sasaki, M; Tagoshi, H; Nakamura, T; Tanaka, T; Ohara, K

    2002-01-01

    The objective of the TAMA 300 interferometer was to develop advanced technologies for kilometre scale interferometers and to observe gravitational wave events in nearby galaxies. It was designed as a power-recycled Fabry-Perot-Michelson interferometer and was intended as a step towards a final interferometer in Japan. The present successful status of TAMA is presented. TAMA forms a basis for LCGT (large-scale cryogenic gravitational wave telescope), a 3 km scale cryogenic interferometer to be built in the Kamioka mine in Japan, implementing cryogenic mirror techniques. The plan of LCGT is schematically described along with its associated R and D.

  10. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  11. Statistical mechanics of neocortical interactions: A scaling paradigm applied to electroencephalography

    Science.gov (United States)

    Ingber, Lester

    1991-09-01

    A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions. While not useful to yield insights at the single-neuron level, SMNI has demonstrated its capability in describing large-scale properties of short-term memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. In this paper, a more stringent test is placed on SMNI: The algebraic and numerical algorithms previously developed in this and similar systems are brought to bear to fit large sets of EEG and evoked-potential data being collected to investigate genetic predispositions to alcoholism and to extract brain ``signatures'' of short-term memory. Using the numerical algorithm of very fast simulated reannealing, it is demonstrated that SMNI can indeed fit these data within experimentally observed ranges of its underlying neuronal-synaptic parameters, and the quantitative modeling results are used to examine physical neocortical mechanisms to discriminate high-risk and low-risk populations genetically predisposed to alcoholism. Since this study is a control to span relatively long time epochs, similar to earlier attempts to establish such correlations, this discrimination is inconclusive because of other neuronal activity which can mask such effects. However, the SMNI model is shown to be consistent with EEG data during selective attention tasks and with neocortical mechanisms describing short-term memory previously published using this approach. This paper explicitly identifies similar nonlinear stochastic mechanisms of interaction at the microscopic-neuronal, mesoscopic-columnar, and macroscopic-regional scales of neocortical interactions. These results give strong quantitative support for an accurate intuitive picture, portraying

  12. Spatial fingerprints of community structure in human interaction network for an extensive set of large-scale regions.

    Science.gov (United States)

    Kallus, Zsófia; Barankai, Norbert; Szüle, János; Vattay, Gábor

    2015-01-01

    Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization.

  13. Orphan nuclear receptor TLX recruits histone deacetylases to repress transcription and regulate neural stem cell proliferation

    OpenAIRE

    Sun, GuoQiang; Yu, Ruth T.; Evans, Ronald M.; Shi, Yanhong

    2007-01-01

    TLX is a transcription factor that is essential for neural stem cell proliferation and self-renewal. However, the molecular mechanism of TLX-mediated neural stem cell proliferation and self-renewal is largely unknown. We show here that TLX recruits histone deacetylases (HDACs) to its downstream target genes to repress their transcription, which in turn regulates neural stem cell proliferation. TLX interacts with HDAC3 and HDAC5 in neural stem cells. The HDAC5-interaction domain was mapped to ...

  14. A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data.

    Science.gov (United States)

    Venkat, A; Christensen, C; Gyulassy, A; Summa, B; Federer, F; Angelucci, A; Pascucci, V

    2016-08-01

    The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data.

  15. Evidence for non-Abelian dark matter from large scale structure?

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    If dark matter multiplicity arises from a weakly coupled non-Abelian dark gauge group the corresponding "dark gluons" can have interesting signatures in cosmology which I will review: 1. the "dark gluons" contribute to the radiation content of the universe and 2. gluon interactions with the dark matter may explain the >3 sigma discrepancy between precision fits to the CMB from Planck and direct measurements of large scale structure in the universe.

  16. Changes in the interaction of resting-state neural networks from adolescence to adulthood.

    Science.gov (United States)

    Stevens, Michael C; Pearlson, Godfrey D; Calhoun, Vince D

    2009-08-01

    This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks. (c) 2009 Wiley-Liss, Inc.

  17. Large scale model testing

    International Nuclear Information System (INIS)

    Brumovsky, M.; Filip, R.; Polachova, H.; Stepanek, S.

    1989-01-01

    Fracture mechanics and fatigue calculations for WWER reactor pressure vessels were checked by large scale model testing performed using large testing machine ZZ 8000 (with a maximum load of 80 MN) at the SKODA WORKS. The results are described from testing the material resistance to fracture (non-ductile). The testing included the base materials and welded joints. The rated specimen thickness was 150 mm with defects of a depth between 15 and 100 mm. The results are also presented of nozzles of 850 mm inner diameter in a scale of 1:3; static, cyclic, and dynamic tests were performed without and with surface defects (15, 30 and 45 mm deep). During cyclic tests the crack growth rate in the elastic-plastic region was also determined. (author). 6 figs., 2 tabs., 5 refs

  18. Why small-scale cannabis growers stay small: five mechanisms that prevent small-scale growers from going large scale.

    Science.gov (United States)

    Hammersvik, Eirik; Sandberg, Sveinung; Pedersen, Willy

    2012-11-01

    Over the past 15-20 years, domestic cultivation of cannabis has been established in a number of European countries. New techniques have made such cultivation easier; however, the bulk of growers remain small-scale. In this study, we explore the factors that prevent small-scale growers from increasing their production. The study is based on 1 year of ethnographic fieldwork and qualitative interviews conducted with 45 Norwegian cannabis growers, 10 of whom were growing on a large-scale and 35 on a small-scale. The study identifies five mechanisms that prevent small-scale indoor growers from going large-scale. First, large-scale operations involve a number of people, large sums of money, a high work-load and a high risk of detection, and thus demand a higher level of organizational skills than for small growing operations. Second, financial assets are needed to start a large 'grow-site'. Housing rent, electricity, equipment and nutrients are expensive. Third, to be able to sell large quantities of cannabis, growers need access to an illegal distribution network and knowledge of how to act according to black market norms and structures. Fourth, large-scale operations require advanced horticultural skills to maximize yield and quality, which demands greater skills and knowledge than does small-scale cultivation. Fifth, small-scale growers are often embedded in the 'cannabis culture', which emphasizes anti-commercialism, anti-violence and ecological and community values. Hence, starting up large-scale production will imply having to renegotiate or abandon these values. Going from small- to large-scale cannabis production is a demanding task-ideologically, technically, economically and personally. The many obstacles that small-scale growers face and the lack of interest and motivation for going large-scale suggest that the risk of a 'slippery slope' from small-scale to large-scale growing is limited. Possible political implications of the findings are discussed. Copyright

  19. Distributed large-scale dimensional metrology new insights

    CERN Document Server

    Franceschini, Fiorenzo; Maisano, Domenico

    2011-01-01

    Focuses on the latest insights into and challenges of distributed large scale dimensional metrology Enables practitioners to study distributed large scale dimensional metrology independently Includes specific examples of the development of new system prototypes

  20. Large-Scale Traveling Weather Systems in Mars’ Southern Extratropics

    Science.gov (United States)

    Hollingsworth, Jeffery L.; Kahre, Melinda A.

    2017-10-01

    Between late fall and early spring, Mars’ middle- and high-latitude atmosphere supports strong mean equator-to-pole temperature contrasts and an accompanying mean westerly polar vortex. Observations from both the MGS Thermal Emission Spectrometer (TES) and the MRO Mars Climate Sounder (MCS) indicate that a mean baroclinicity-barotropicity supports intense, large-scale eastward traveling weather systems (i.e., transient synoptic-period waves). Such extratropical weather disturbances are critical components of the global circulation as they serve as agents in the transport of heat and momentum, and generalized scalar/tracer quantities (e.g., atmospheric dust, water-vapor and ice clouds). The character of such traveling extratropical synoptic disturbances in Mars' southern hemisphere during late winter through early spring is investigated using a moderately high-resolution Mars global climate model (Mars GCM). This Mars GCM imposes interactively-lifted and radiatively-active dust based on a threshold value of the surface stress. The model exhibits a reasonable "dust cycle" (i.e., globally averaged, a dustier atmosphere during southern spring and summer occurs). Compared to the northern-hemisphere counterparts, the southern synoptic-period weather disturbances and accompanying frontal waves have smaller meridional and zonal scales, and are far less intense. Influences of the zonally asymmetric (i.e., east-west varying) topography on southern large-scale weather are investigated, in addition to large-scale up-slope/down-slope flows and the diurnal cycle. A southern storm zone in late winter and early spring presents in the western hemisphere via orographic influences from the Tharsis highlands, and the Argyre and Hellas impact basins. Geographically localized transient-wave activity diagnostics are constructed that illuminate dynamical differences amongst the simulations and these are presented.

  1. Large-Scale Traveling Weather Systems in Mars Southern Extratropics

    Science.gov (United States)

    Hollingsworth, Jeffery L.; Kahre, Melinda A.

    2017-01-01

    Between late fall and early spring, Mars' middle- and high-latitude atmosphere supports strong mean equator-to-pole temperature contrasts and an accompanying mean westerly polar vortex. Observations from both the MGS Thermal Emission Spectrometer (TES) and the MRO Mars Climate Sounder (MCS) indicate that a mean baroclinicity-barotropicity supports intense, large-scale eastward traveling weather systems (i.e., transient synoptic-period waves). Such extratropical weather disturbances are critical components of the global circulation as they serve as agents in the transport of heat and momentum, and generalized scalar/tracer quantities (e.g., atmospheric dust, water-vapor and ice clouds). The character of such traveling extratropical synoptic disturbances in Mars' southern hemisphere during late winter through early spring is investigated using a moderately high-resolution Mars global climate model (Mars GCM). This Mars GCM imposes interactively-lifted and radiatively-active dust based on a threshold value of the surface stress. The model exhibits a reasonable "dust cycle" (i.e., globally averaged, a dustier atmosphere during southern spring and summer occurs). Compared to the northern-hemisphere counterparts, the southern synoptic-period weather disturbances and accompanying frontal waves have smaller meridional and zonal scales, and are far less intense. Influences of the zonally asymmetric (i.e., east-west varying) topography on southern large-scale weather are investigated, in addition to large-scale up-slope/down-slope flows and the diurnal cycle. A southern storm zone in late winter and early spring presents in the western hemisphere via orographic influences from the Tharsis highlands, and the Argyre and Hellas impact basins. Geographically localized transient-wave activity diagnostics are constructed that illuminate dynamical differences amongst the simulations and these are presented.

  2. Separating monocular and binocular neural mechanisms mediating chromatic contextual interactions.

    Science.gov (United States)

    D'Antona, Anthony D; Christiansen, Jens H; Shevell, Steven K

    2014-04-17

    When seen in isolation, a light that varies in chromaticity over time is perceived to oscillate in color. Perception of that same time-varying light may be altered by a surrounding light that is also temporally varying in chromaticity. The neural mechanisms that mediate these contextual interactions are the focus of this article. Observers viewed a central test stimulus that varied in chromaticity over time within a larger surround that also varied in chromaticity at the same temporal frequency. Center and surround were presented either to the same eye (monocular condition) or to opposite eyes (dichoptic condition) at the same frequency (3.125, 6.25, or 9.375 Hz). Relative phase between center and surround modulation was varied. In both the monocular and dichoptic conditions, the perceived modulation depth of the central light depended on the relative phase of the surround. A simple model implementing a linear combination of center and surround modulation fit the measurements well. At the lowest temporal frequency (3.125 Hz), the surround's influence was virtually identical for monocular and dichoptic conditions, suggesting that at this frequency, the surround's influence is mediated primarily by a binocular neural mechanism. At higher frequencies, the surround's influence was greater for the monocular condition than for the dichoptic condition, and this difference increased with temporal frequency. Our findings show that two separate neural mechanisms mediate chromatic contextual interactions: one binocular and dominant at lower temporal frequencies and the other monocular and dominant at higher frequencies (6-10 Hz).

  3. Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

    Science.gov (United States)

    Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

    2018-01-01

    It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

  4. Thermal activation of dislocations in large scale obstacle bypass

    Science.gov (United States)

    Sobie, Cameron; Capolungo, Laurent; McDowell, David L.; Martinez, Enrique

    2017-08-01

    Dislocation dynamics simulations have been used extensively to predict hardening caused by dislocation-obstacle interactions, including irradiation defect hardening in the athermal case. Incorporating the role of thermal energy on these interactions is possible with a framework provided by harmonic transition state theory (HTST) enabling direct access to thermally activated reaction rates using the Arrhenius equation, including rates of dislocation-obstacle bypass processes. Moving beyond unit dislocation-defect reactions to a representative environment containing a large number of defects requires coarse-graining the activation energy barriers of a population of obstacles into an effective energy barrier that accurately represents the large scale collective process. The work presented here investigates the relationship between unit dislocation-defect bypass processes and the distribution of activation energy barriers calculated for ensemble bypass processes. A significant difference between these cases is observed, which is attributed to the inherent cooperative nature of dislocation bypass processes. In addition to the dislocation-defect interaction, the morphology of the dislocation segments pinned to the defects play an important role on the activation energies for bypass. A phenomenological model for activation energy stress dependence is shown to describe well the effect of a distribution of activation energies, and a probabilistic activation energy model incorporating the stress distribution in a material is presented.

  5. Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

    OpenAIRE

    Zhao, Liang; Liao, Siyu; Wang, Yanzhi; Li, Zhe; Tang, Jian; Pan, Victor; Yuan, Bo

    2017-01-01

    Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. First, we prove the universal approximation property of LDR neural networks with a ...

  6. Characterization of human neural differentiation from pluripotent stem cells using proteomics/PTMomics

    DEFF Research Database (Denmark)

    Braga, Marcella Nunes de Melo; Meyer, Morten; Zeng, Xianmin

    2015-01-01

    Stem cells are unspecialized cells capable of self-renewal and to differentiate into the large variety of cells in the body. The possibility to differentiate these cells into neural precursors and neural cells in vitro provides the opportunity to study neural development, nerve cell biology, neur...... differentiation from pluripotent stem cells. Moreover, some of the challenges in stem cell biology, differentiation, and proteomics/PTMomics that are not exclusive to neural development will be discussed.......Stem cells are unspecialized cells capable of self-renewal and to differentiate into the large variety of cells in the body. The possibility to differentiate these cells into neural precursors and neural cells in vitro provides the opportunity to study neural development, nerve cell biology...... the understanding of molecular processes in cells. Substantial advances in PTM enrichment methods and mass spectrometry has allowed the characterization of a subset of PTMs in large-scale studies. This review focuses on the current state-of-the-art of proteomic, as well as PTMomic studies related to human neural...

  7. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

    Science.gov (United States)

    Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

    2018-03-06

    Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

  8. Large scale genotype comparison of human papillomavirus E2-host interaction networks provides new insights for e2 molecular functions.

    Science.gov (United States)

    Muller, Mandy; Jacob, Yves; Jones, Louis; Weiss, Amélie; Brino, Laurent; Chantier, Thibault; Lotteau, Vincent; Favre, Michel; Demeret, Caroline

    2012-01-01

    Human Papillomaviruses (HPV) cause widespread infections in humans, resulting in latent infections or diseases ranging from benign hyperplasia to cancers. HPV-induced pathologies result from complex interplays between viral proteins and the host proteome. Given the major public health concern due to HPV-associated cancers, most studies have focused on the early proteins expressed by HPV genotypes with high oncogenic potential (designated high-risk HPV or HR-HPV). To advance the global understanding of HPV pathogenesis, we mapped the virus/host interaction networks of the E2 regulatory protein from 12 genotypes representative of the range of HPV pathogenicity. Large-scale identification of E2-interaction partners was performed by yeast two-hybrid screenings of a HaCaT cDNA library. Based on a high-confidence scoring scheme, a subset of these partners was then validated for pair-wise interaction in mammalian cells with the whole range of the 12 E2 proteins, allowing a comparative interaction analysis. Hierarchical clustering of E2-host interaction profiles mostly recapitulated HPV phylogeny and provides clues to the involvement of E2 in HPV infection. A set of cellular proteins could thus be identified discriminating, among the mucosal HPV, E2 proteins of HR-HPV 16 or 18 from the non-oncogenic genital HPV. The study of the interaction networks revealed a preferential hijacking of highly connected cellular proteins and the targeting of several functional families. These include transcription regulation, regulation of apoptosis, RNA processing, ubiquitination and intracellular trafficking. The present work provides an overview of E2 biological functions across multiple HPV genotypes.

  9. Trends in large-scale testing of reactor structures

    International Nuclear Information System (INIS)

    Blejwas, T.E.

    2003-01-01

    Large-scale tests of reactor structures have been conducted at Sandia National Laboratories since the late 1970s. This paper describes a number of different large-scale impact tests, pressurization tests of models of containment structures, and thermal-pressure tests of models of reactor pressure vessels. The advantages of large-scale testing are evident, but cost, in particular limits its use. As computer models have grown in size, such as number of degrees of freedom, the advent of computer graphics has made possible very realistic representation of results - results that may not accurately represent reality. A necessary condition to avoiding this pitfall is the validation of the analytical methods and underlying physical representations. Ironically, the immensely larger computer models sometimes increase the need for large-scale testing, because the modeling is applied to increasing more complex structural systems and/or more complex physical phenomena. Unfortunately, the cost of large-scale tests is a disadvantage that will likely severely limit similar testing in the future. International collaborations may provide the best mechanism for funding future programs with large-scale tests. (author)

  10. On-line transient stability assessment of large-scale power systems by using ball vector machines

    International Nuclear Information System (INIS)

    Mohammadi, M.; Gharehpetian, G.B.

    2010-01-01

    In this paper ball vector machine (BVM) has been used for on-line transient stability assessment of large-scale power systems. To classify the system transient security status, a BVM has been trained for all contingencies. The proposed BVM based security assessment algorithm has very small training time and space in comparison with artificial neural networks (ANN), support vector machines (SVM) and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors (SV) and therefore is faster than existing algorithms for on-line applications. One of the main points, to apply a machine learning method is feature selection. In this paper, a new Decision Tree (DT) based feature selection technique has been presented. The proposed BVM based algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line transient stability assessment procedure of large-scale power system. The proposed feature selection algorithm has been compared with different feature selection algorithms. The simulation results demonstrate the effectiveness of the proposed feature algorithm.

  11. Direction of information flow in large-scale resting-state networks is frequency-dependent.

    Science.gov (United States)

    Hillebrand, Arjan; Tewarie, Prejaas; van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W S; Douw, Linda; Gouw, Alida A; van Straaten, Elisabeth C W; Stam, Cornelis J

    2016-04-05

    Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

  12. What Will the Neighbors Think? Building Large-Scale Science Projects Around the World

    International Nuclear Information System (INIS)

    Jones, Craig; Mrotzek, Christian; Toge, Nobu; Sarno, Doug

    2007-01-01

    Public participation is an essential ingredient for turning the International Linear Collider into a reality. Wherever the proposed particle accelerator is sited in the world, its neighbors -- in any country -- will have something to say about hosting a 35-kilometer-long collider in their backyards. When it comes to building large-scale physics projects, almost every laboratory has a story to tell. Three case studies from Japan, Germany and the US will be presented to examine how community relations are handled in different parts of the world. How do particle physics laboratories interact with their local communities? How do neighbors react to building large-scale projects in each region? How can the lessons learned from past experiences help in building the next big project? These and other questions will be discussed to engage the audience in an active dialogue about how a large-scale project like the ILC can be a good neighbor.

  13. Integration and segregation of large-scale brain networks during short-term task automatization.

    Science.gov (United States)

    Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

    2016-11-03

    The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.

  14. Multirelational organization of large-scale social networks in an online world.

    Science.gov (United States)

    Szell, Michael; Lambiotte, Renaud; Thurner, Stefan

    2010-08-03

    The capacity to collect fingerprints of individuals in online media has revolutionized the way researchers explore human society. Social systems can be seen as a nonlinear superposition of a multitude of complex social networks, where nodes represent individuals and links capture a variety of different social relations. Much emphasis has been put on the network topology of social interactions, however, the multidimensional nature of these interactions has largely been ignored, mostly because of lack of data. Here, for the first time, we analyze a complete, multirelational, large social network of a society consisting of the 300,000 odd players of a massive multiplayer online game. We extract networks of six different types of one-to-one interactions between the players. Three of them carry a positive connotation (friendship, communication, trade), three a negative (enmity, armed aggression, punishment). We first analyze these types of networks as separate entities and find that negative interactions differ from positive interactions by their lower reciprocity, weaker clustering, and fatter-tail degree distribution. We then explore how the interdependence of different network types determines the organization of the social system. In particular, we study correlations and overlap between different types of links and demonstrate the tendency of individuals to play different roles in different networks. As a demonstration of the power of the approach, we present the first empirical large-scale verification of the long-standing structural balance theory, by focusing on the specific multiplex network of friendship and enmity relations.

  15. Large scale molecular simulations of nanotoxicity.

    Science.gov (United States)

    Jimenez-Cruz, Camilo A; Kang, Seung-gu; Zhou, Ruhong

    2014-01-01

    The widespread use of nanomaterials in biomedical applications has been accompanied by an increasing interest in understanding their interactions with tissues, cells, and biomolecules, and in particular, on how they might affect the integrity of cell membranes and proteins. In this mini-review, we present a summary of some of the recent studies on this important subject, especially from the point of view of large scale molecular simulations. The carbon-based nanomaterials and noble metal nanoparticles are the main focus, with additional discussions on quantum dots and other nanoparticles as well. The driving forces for adsorption of fullerenes, carbon nanotubes, and graphene nanosheets onto proteins or cell membranes are found to be mainly hydrophobic interactions and the so-called π-π stacking (between aromatic rings), while for the noble metal nanoparticles the long-range electrostatic interactions play a bigger role. More interestingly, there are also growing evidences showing that nanotoxicity can have implications in de novo design of nanomedicine. For example, the endohedral metallofullerenol Gd@C₈₂(OH)₂₂ is shown to inhibit tumor growth and metastasis by inhibiting enzyme MMP-9, and graphene is illustrated to disrupt bacteria cell membranes by insertion/cutting as well as destructive extraction of lipid molecules. These recent findings have provided a better understanding of nanotoxicity at the molecular level and also suggested therapeutic potential by using the cytotoxicity of nanoparticles against cancer or bacteria cells. © 2014 Wiley Periodicals, Inc.

  16. Renormalization-group flow of the effective action of cosmological large-scale structures

    CERN Document Server

    Floerchinger, Stefan

    2017-01-01

    Following an approach of Matarrese and Pietroni, we derive the functional renormalization group (RG) flow of the effective action of cosmological large-scale structures. Perturbative solutions of this RG flow equation are shown to be consistent with standard cosmological perturbation theory. Non-perturbative approximate solutions can be obtained by truncating the a priori infinite set of possible effective actions to a finite subspace. Using for the truncated effective action a form dictated by dissipative fluid dynamics, we derive RG flow equations for the scale dependence of the effective viscosity and sound velocity of non-interacting dark matter, and we solve them numerically. Physically, the effective viscosity and sound velocity account for the interactions of long-wavelength fluctuations with the spectrum of smaller-scale perturbations. We find that the RG flow exhibits an attractor behaviour in the IR that significantly reduces the dependence of the effective viscosity and sound velocity on the input ...

  17. HiQuant: Rapid Postquantification Analysis of Large-Scale MS-Generated Proteomics Data.

    Science.gov (United States)

    Bryan, Kenneth; Jarboui, Mohamed-Ali; Raso, Cinzia; Bernal-Llinares, Manuel; McCann, Brendan; Rauch, Jens; Boldt, Karsten; Lynn, David J

    2016-06-03

    Recent advances in mass-spectrometry-based proteomics are now facilitating ambitious large-scale investigations of the spatial and temporal dynamics of the proteome; however, the increasing size and complexity of these data sets is overwhelming current downstream computational methods, specifically those that support the postquantification analysis pipeline. Here we present HiQuant, a novel application that enables the design and execution of a postquantification workflow, including common data-processing steps, such as assay normalization and grouping, and experimental replicate quality control and statistical analysis. HiQuant also enables the interpretation of results generated from large-scale data sets by supporting interactive heatmap analysis and also the direct export to Cytoscape and Gephi, two leading network analysis platforms. HiQuant may be run via a user-friendly graphical interface and also supports complete one-touch automation via a command-line mode. We evaluate HiQuant's performance by analyzing a large-scale, complex interactome mapping data set and demonstrate a 200-fold improvement in the execution time over current methods. We also demonstrate HiQuant's general utility by analyzing proteome-wide quantification data generated from both a large-scale public tyrosine kinase siRNA knock-down study and an in-house investigation into the temporal dynamics of the KSR1 and KSR2 interactomes. Download HiQuant, sample data sets, and supporting documentation at http://hiquant.primesdb.eu .

  18. Speech comprehension aided by multiple modalities: behavioural and neural interactions

    Science.gov (United States)

    McGettigan, Carolyn; Faulkner, Andrew; Altarelli, Irene; Obleser, Jonas; Baverstock, Harriet; Scott, Sophie K.

    2014-01-01

    Speech comprehension is a complex human skill, the performance of which requires the perceiver to combine information from several sources – e.g. voice, face, gesture, linguistic context – to achieve an intelligible and interpretable percept. We describe a functional imaging investigation of how auditory, visual and linguistic information interact to facilitate comprehension. Our specific aims were to investigate the neural responses to these different information sources, alone and in interaction, and further to use behavioural speech comprehension scores to address sites of intelligibility-related activation in multifactorial speech comprehension. In fMRI, participants passively watched videos of spoken sentences, in which we varied Auditory Clarity (with noise-vocoding), Visual Clarity (with Gaussian blurring) and Linguistic Predictability. Main effects of enhanced signal with increased auditory and visual clarity were observed in overlapping regions of posterior STS. Two-way interactions of the factors (auditory × visual, auditory × predictability) in the neural data were observed outside temporal cortex, where positive signal change in response to clearer facial information and greater semantic predictability was greatest at intermediate levels of auditory clarity. Overall changes in stimulus intelligibility by condition (as determined using an independent behavioural experiment) were reflected in the neural data by increased activation predominantly in bilateral dorsolateral temporal cortex, as well as inferior frontal cortex and left fusiform gyrus. Specific investigation of intelligibility changes at intermediate auditory clarity revealed a set of regions, including posterior STS and fusiform gyrus, showing enhanced responses to both visual and linguistic information. Finally, an individual differences analysis showed that greater comprehension performance in the scanning participants (measured in a post-scan behavioural test) were associated with

  19. Large scale FCI experiments in subassembly geometry. Test facility and model experiments

    International Nuclear Information System (INIS)

    Beutel, H.; Gast, K.

    A program is outlined for the study of fuel/coolant interaction under SNR conditions. The program consists of a) under water explosion experiments with full size models of the SNR-core, in which the fuel/coolant system is simulated by a pyrotechnic mixture. b) large scale fuel/coolant interaction experiments with up to 5kg of molten UO 2 interacting with liquid sodium at 300 deg C to 600 deg C in a highly instrumented test facility simulating an SNR subassembly. The experimental results will be compared to theoretical models under development at Karlsruhe. Commencement of the experiments is expected for the beginning of 1975

  20. Large Scale Computations in Air Pollution Modelling

    DEFF Research Database (Denmark)

    Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  1. Application of neural networks to software quality modeling of a very large telecommunications system.

    Science.gov (United States)

    Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

    1997-01-01

    Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

  2. Large-Scale 3D Printing: The Way Forward

    Science.gov (United States)

    Jassmi, Hamad Al; Najjar, Fady Al; Ismail Mourad, Abdel-Hamid

    2018-03-01

    Research on small-scale 3D printing has rapidly evolved, where numerous industrial products have been tested and successfully applied. Nonetheless, research on large-scale 3D printing, directed to large-scale applications such as construction and automotive manufacturing, yet demands a great a great deal of efforts. Large-scale 3D printing is considered an interdisciplinary topic and requires establishing a blended knowledge base from numerous research fields including structural engineering, materials science, mechatronics, software engineering, artificial intelligence and architectural engineering. This review article summarizes key topics of relevance to new research trends on large-scale 3D printing, particularly pertaining (1) technological solutions of additive construction (i.e. the 3D printers themselves), (2) materials science challenges, and (3) new design opportunities.

  3. Software Tools For Large Scale Interactive Hydrodynamic Modeling

    NARCIS (Netherlands)

    Donchyts, G.; Baart, F.; van Dam, A; Jagers, B; van der Pijl, S.; Piasecki, M.

    2014-01-01

    Developing easy-to-use software that combines components for simultaneous visualization, simulation and interaction is a great challenge. Mainly, because it involves a number of disciplines, like computational fluid dynamics, computer graphics, high-performance computing. One of the main

  4. Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

    Science.gov (United States)

    Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

    2016-01-01

    Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

  5. Growth Limits in Large Scale Networks

    DEFF Research Database (Denmark)

    Knudsen, Thomas Phillip

    limitations. The rising complexity of network management with the convergence of communications platforms is shown as problematic for both automatic management feasibility and for manpower resource management. In the fourth step the scope is extended to include the present society with the DDN project as its......The Subject of large scale networks is approached from the perspective of the network planner. An analysis of the long term planning problems is presented with the main focus on the changing requirements for large scale networks and the potential problems in meeting these requirements. The problems...... the fundamental technological resources in network technologies are analysed for scalability. Here several technological limits to continued growth are presented. The third step involves a survey of major problems in managing large scale networks given the growth of user requirements and the technological...

  6. Accelerating sustainability in large-scale facilities

    CERN Multimedia

    Marina Giampietro

    2011-01-01

    Scientific research centres and large-scale facilities are intrinsically energy intensive, but how can big science improve its energy management and eventually contribute to the environmental cause with new cleantech? CERN’s commitment to providing tangible answers to these questions was sealed in the first workshop on energy management for large scale scientific infrastructures held in Lund, Sweden, on the 13-14 October.   Participants at the energy management for large scale scientific infrastructures workshop. The workshop, co-organised with the European Spallation Source (ESS) and  the European Association of National Research Facilities (ERF), tackled a recognised need for addressing energy issues in relation with science and technology policies. It brought together more than 150 representatives of Research Infrastrutures (RIs) and energy experts from Europe and North America. “Without compromising our scientific projects, we can ...

  7. Large scale reflood test

    International Nuclear Information System (INIS)

    Hirano, Kemmei; Murao, Yoshio

    1980-01-01

    The large-scale reflood test with a view to ensuring the safety of light water reactors was started in fiscal 1976 based on the special account act for power source development promotion measures by the entrustment from the Science and Technology Agency. Thereafter, to establish the safety of PWRs in loss-of-coolant accidents by joint international efforts, the Japan-West Germany-U.S. research cooperation program was started in April, 1980. Thereupon, the large-scale reflood test is now included in this program. It consists of two tests using a cylindrical core testing apparatus for examining the overall system effect and a plate core testing apparatus for testing individual effects. Each apparatus is composed of the mock-ups of pressure vessel, primary loop, containment vessel and ECCS. The testing method, the test results and the research cooperation program are described. (J.P.N.)

  8. Driving force for hydrophobic interaction at different length scales.

    Science.gov (United States)

    Zangi, Ronen

    2011-03-17

    We study by molecular dynamics simulations the driving force for the hydrophobic interaction between graphene sheets of different sizes down to the atomic scale. Similar to the prediction by Lum, Chandler, and Weeks for hard-sphere solvation [J. Phys. Chem. B 1999, 103, 4570-4577], we find the driving force to be length-scale dependent, despite the fact that our model systems do not exhibit dewetting. For small hydrophobic solutes, the association is purely entropic, while enthalpy favors dissociation. The latter is demonstrated to arise from the enhancement of hydrogen bonding between the water molecules around small hydrophobes. On the other hand, the attraction between large graphene sheets is dominated by enthalpy which mainly originates from direct solute-solute interactions. The crossover length is found to be inside the range of 0.3-1.5 nm(2) of the surface area of the hydrophobe that is eliminated in the association process. In the large-scale regime, different thermodynamic properties are scalable with this change of surface area. In particular, upon dimerization, a total and a water-induced stabilization of approximately 65 and 12 kJ/mol/nm(2) are obtained, respectively, and on average around one hydrogen bond is gained per 1 nm(2) of graphene sheet association. Furthermore, the potential of mean force between the sheets is also scalable except for interplate distances smaller than 0.64 nm which corresponds to the region around the barrier for removing the last layer of water. It turns out that, as the surface area increases, the relative height of the barrier for association decreases and the range of attraction increases. It is also shown that, around small hydrophobic solutes, the lifetime of the hydrogen bonds is longer than in the bulk, while around large hydrophobes it is the same. Nevertheless, the rearrangement of the hydrogen-bond network for both length-scale regimes is slower than in bulk water. © 2011 American Chemical Society

  9. High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

    Science.gov (United States)

    Maly, K.

    1998-01-01

    Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated

  10. Measuring large-scale social networks with high resolution.

    Directory of Open Access Journals (Sweden)

    Arkadiusz Stopczynski

    Full Text Available This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

  11. Temporal scale dependent interactions between multiple environmental disturbances in microcosm ecosystems.

    Science.gov (United States)

    Garnier, Aurélie; Pennekamp, Frank; Lemoine, Mélissa; Petchey, Owen L

    2017-12-01

    Global environmental change has negative impacts on ecological systems, impacting the stable provision of functions, goods, and services. Whereas effects of individual environmental changes (e.g. temperature change or change in resource availability) are reasonably well understood, we lack information about if and how multiple changes interact. We examined interactions among four types of environmental disturbance (temperature, nutrient ratio, carbon enrichment, and light) in a fully factorial design using a microbial aquatic ecosystem and observed responses of dissolved oxygen saturation at three temporal scales (resistance, resilience, and return time). We tested whether multiple disturbances combine in a dominant, additive, or interactive fashion, and compared the predictability of dissolved oxygen across scales. Carbon enrichment and shading reduced oxygen concentration in the short term (i.e. resistance); although no other effects or interactions were statistically significant, resistance decreased as the number of disturbances increased. In the medium term, only enrichment accelerated recovery, but none of the other effects (including interactions) were significant. In the long term, enrichment and shading lengthened return times, and we found significant two-way synergistic interactions between disturbances. The best performing model (dominant, additive, or interactive) depended on the temporal scale of response. In the short term (i.e. for resistance), the dominance model predicted resistance of dissolved oxygen best, due to a large effect of carbon enrichment, whereas none of the models could predict the medium term (i.e. resilience). The long-term response was best predicted by models including interactions among disturbances. Our results indicate the importance of accounting for the temporal scale of responses when researching the effects of environmental disturbances on ecosystems. © 2017 The Authors. Global Change Biology Published by John Wiley

  12. Large Scale Cosmological Anomalies and Inhomogeneous Dark Energy

    Directory of Open Access Journals (Sweden)

    Leandros Perivolaropoulos

    2014-01-01

    Full Text Available A wide range of large scale observations hint towards possible modifications on the standard cosmological model which is based on a homogeneous and isotropic universe with a small cosmological constant and matter. These observations, also known as “cosmic anomalies” include unexpected Cosmic Microwave Background perturbations on large angular scales, large dipolar peculiar velocity flows of galaxies (“bulk flows”, the measurement of inhomogenous values of the fine structure constant on cosmological scales (“alpha dipole” and other effects. The presence of the observational anomalies could either be a large statistical fluctuation in the context of ΛCDM or it could indicate a non-trivial departure from the cosmological principle on Hubble scales. Such a departure is very much constrained by cosmological observations for matter. For dark energy however there are no significant observational constraints for Hubble scale inhomogeneities. In this brief review I discuss some of the theoretical models that can naturally lead to inhomogeneous dark energy, their observational constraints and their potential to explain the large scale cosmic anomalies.

  13. Large-scale patterns in Rayleigh-Benard convection

    International Nuclear Information System (INIS)

    Hardenberg, J. von; Parodi, A.; Passoni, G.; Provenzale, A.; Spiegel, E.A.

    2008-01-01

    Rayleigh-Benard convection at large Rayleigh number is characterized by the presence of intense, vertically moving plumes. Both laboratory and numerical experiments reveal that the rising and descending plumes aggregate into separate clusters so as to produce large-scale updrafts and downdrafts. The horizontal scales of the aggregates reported so far have been comparable to the horizontal extent of the containers, but it has not been clear whether that represents a limitation imposed by domain size. In this work, we present numerical simulations of convection at sufficiently large aspect ratio to ascertain whether there is an intrinsic saturation scale for the clustering process when that ratio is large enough. From a series of simulations of Rayleigh-Benard convection with Rayleigh numbers between 10 5 and 10 8 and with aspect ratios up to 12π, we conclude that the clustering process has a finite horizontal saturation scale with at most a weak dependence on Rayleigh number in the range studied

  14. Spatial fingerprints of community structure in human interaction network for an extensive set of large-scale regions.

    Directory of Open Access Journals (Sweden)

    Zsófia Kallus

    Full Text Available Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization.

  15. Artificial neural network modelling of a large-scale wastewater treatment plant operation.

    Science.gov (United States)

    Güçlü, Dünyamin; Dursun, Sükrü

    2010-11-01

    Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

  16. How do the multiple large-scale climate oscillations trigger extreme precipitation?

    Science.gov (United States)

    Shi, Pengfei; Yang, Tao; Xu, Chong-Yu; Yong, Bin; Shao, Quanxi; Li, Zhenya; Wang, Xiaoyan; Zhou, Xudong; Li, Shu

    2017-10-01

    Identifying the links between variations in large-scale climate patterns and precipitation is of tremendous assistance in characterizing surplus or deficit of precipitation, which is especially important for evaluation of local water resources and ecosystems in semi-humid and semi-arid regions. Restricted by current limited knowledge on underlying mechanisms, statistical correlation methods are often used rather than physical based model to characterize the connections. Nevertheless, available correlation methods are generally unable to reveal the interactions among a wide range of climate oscillations and associated effects on precipitation, especially on extreme precipitation. In this work, a probabilistic analysis approach by means of a state-of-the-art Copula-based joint probability distribution is developed to characterize the aggregated behaviors for large-scale climate patterns and their connections to precipitation. This method is employed to identify the complex connections between climate patterns (Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and seasonal precipitation over a typical semi-humid and semi-arid region, the Haihe River Basin in China. Results show that the interactions among multiple climate oscillations are non-uniform in most seasons and phases. Certain joint extreme phases can significantly trigger extreme precipitation (flood and drought) owing to the amplification effect among climate oscillations.

  17. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  18. Large scale genotype comparison of human papillomavirus E2-host interaction networks provides new insights for e2 molecular functions.

    Directory of Open Access Journals (Sweden)

    Mandy Muller

    Full Text Available Human Papillomaviruses (HPV cause widespread infections in humans, resulting in latent infections or diseases ranging from benign hyperplasia to cancers. HPV-induced pathologies result from complex interplays between viral proteins and the host proteome. Given the major public health concern due to HPV-associated cancers, most studies have focused on the early proteins expressed by HPV genotypes with high oncogenic potential (designated high-risk HPV or HR-HPV. To advance the global understanding of HPV pathogenesis, we mapped the virus/host interaction networks of the E2 regulatory protein from 12 genotypes representative of the range of HPV pathogenicity. Large-scale identification of E2-interaction partners was performed by yeast two-hybrid screenings of a HaCaT cDNA library. Based on a high-confidence scoring scheme, a subset of these partners was then validated for pair-wise interaction in mammalian cells with the whole range of the 12 E2 proteins, allowing a comparative interaction analysis. Hierarchical clustering of E2-host interaction profiles mostly recapitulated HPV phylogeny and provides clues to the involvement of E2 in HPV infection. A set of cellular proteins could thus be identified discriminating, among the mucosal HPV, E2 proteins of HR-HPV 16 or 18 from the non-oncogenic genital HPV. The study of the interaction networks revealed a preferential hijacking of highly connected cellular proteins and the targeting of several functional families. These include transcription regulation, regulation of apoptosis, RNA processing, ubiquitination and intracellular trafficking. The present work provides an overview of E2 biological functions across multiple HPV genotypes.

  19. Manufacturing test of large scale hollow capsule and long length cladding in the large scale oxide dispersion strengthened (ODS) martensitic steel

    International Nuclear Information System (INIS)

    Narita, Takeshi; Ukai, Shigeharu; Kaito, Takeji; Ohtsuka, Satoshi; Fujiwara, Masayuki

    2004-04-01

    Mass production capability of oxide dispersion strengthened (ODS) martensitic steel cladding (9Cr) has being evaluated in the Phase II of the Feasibility Studies on Commercialized Fast Reactor Cycle System. The cost for manufacturing mother tube (raw materials powder production, mechanical alloying (MA) by ball mill, canning, hot extrusion, and machining) is a dominant factor in the total cost for manufacturing ODS ferritic steel cladding. In this study, the large-sale 9Cr-ODS martensitic steel mother tube which is made with a large-scale hollow capsule, and long length claddings were manufactured, and the applicability of these processes was evaluated. Following results were obtained in this study. (1) Manufacturing the large scale mother tube in the dimension of 32 mm OD, 21 mm ID, and 2 m length has been successfully carried out using large scale hollow capsule. This mother tube has a high degree of accuracy in size. (2) The chemical composition and the micro structure of the manufactured mother tube are similar to the existing mother tube manufactured by a small scale can. And the remarkable difference between the bottom and top sides in the manufactured mother tube has not been observed. (3) The long length cladding has been successfully manufactured from the large scale mother tube which was made using a large scale hollow capsule. (4) For reducing the manufacturing cost of the ODS steel claddings, manufacturing process of the mother tubes using a large scale hollow capsules is promising. (author)

  20. Large-Scale Analysis of Network Bistability for Human Cancers

    Science.gov (United States)

    Shiraishi, Tetsuya; Matsuyama, Shinako; Kitano, Hiroaki

    2010-01-01

    Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers. PMID:20628618

  1. Amplification of large-scale magnetic field in nonhelical magnetohydrodynamics

    KAUST Repository

    Kumar, Rohit

    2017-08-11

    It is typically assumed that the kinetic and magnetic helicities play a crucial role in the growth of large-scale dynamo. In this paper, we demonstrate that helicity is not essential for the amplification of large-scale magnetic field. For this purpose, we perform nonhelical magnetohydrodynamic (MHD) simulation, and show that the large-scale magnetic field can grow in nonhelical MHD when random external forcing is employed at scale 1/10 the box size. The energy fluxes and shell-to-shell transfer rates computed using the numerical data show that the large-scale magnetic energy grows due to the energy transfers from the velocity field at the forcing scales.

  2. Relationship between neural rhythm generation disorders and physical disabilities in Parkinson's disease patients' walking.

    Science.gov (United States)

    Ota, Leo; Uchitomi, Hirotaka; Ogawa, Ken-ichiro; Orimo, Satoshi; Miyake, Yoshihiro

    2014-01-01

    Walking is generated by the interaction between neural rhythmic and physical activities. In fact, Parkinson's disease (PD), which is an example of disease, causes not only neural rhythm generation disorders but also physical disabilities. However, the relationship between neural rhythm generation disorders and physical disabilities has not been determined. The aim of this study was to identify the mechanism of gait rhythm generation. In former research, neural rhythm generation disorders in PD patients' walking were characterized by stride intervals, which are more variable and fluctuate randomly. The variability and fluctuation property were quantified using the coefficient of variation (CV) and scaling exponent α. Conversely, because walking is a dynamic process, postural reflex disorder (PRD) is considered the best way to estimate physical disabilities in walking. Therefore, we classified the severity of PRD using CV and α. Specifically, PD patients and healthy elderly were classified into three groups: no-PRD, mild-PRD, and obvious-PRD. We compared the contributions of CV and α to the accuracy of this classification. In this study, 45 PD patients and 17 healthy elderly people walked 200 m. The severity of PRD was determined using the modified Hoehn-Yahr scale (mH-Y). People with mH-Y scores of 2.5 and 3 had mild-PRD and obvious-PRD, respectively. As a result, CV differentiated no-PRD from PRD, indicating the correlation between CV and PRD. Considering that PRD is independent of neural rhythm generation, this result suggests the existence of feedback process from physical activities to neural rhythmic activities. Moreover, α differentiated obvious-PRD from mild-PRD. Considering α reflects the intensity of interaction between factors, this result suggests the change of the interaction. Therefore, the interaction between neural rhythmic and physical activities is thought to plays an important role for gait rhythm generation. These characteristics have

  3. Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    ZHENG Zhuo

    2018-05-01

    Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

  4. Hydrometeorological variability on a large french catchment and its relation to large-scale circulation across temporal scales

    Science.gov (United States)

    Massei, Nicolas; Dieppois, Bastien; Fritier, Nicolas; Laignel, Benoit; Debret, Maxime; Lavers, David; Hannah, David

    2015-04-01

    In the present context of global changes, considerable efforts have been deployed by the hydrological scientific community to improve our understanding of the impacts of climate fluctuations on water resources. Both observational and modeling studies have been extensively employed to characterize hydrological changes and trends, assess the impact of climate variability or provide future scenarios of water resources. In the aim of a better understanding of hydrological changes, it is of crucial importance to determine how and to what extent trends and long-term oscillations detectable in hydrological variables are linked to global climate oscillations. In this work, we develop an approach associating large-scale/local-scale correlation, enmpirical statistical downscaling and wavelet multiresolution decomposition of monthly precipitation and streamflow over the Seine river watershed, and the North Atlantic sea level pressure (SLP) in order to gain additional insights on the atmospheric patterns associated with the regional hydrology. We hypothesized that: i) atmospheric patterns may change according to the different temporal wavelengths defining the variability of the signals; and ii) definition of those hydrological/circulation relationships for each temporal wavelength may improve the determination of large-scale predictors of local variations. The results showed that the large-scale/local-scale links were not necessarily constant according to time-scale (i.e. for the different frequencies characterizing the signals), resulting in changing spatial patterns across scales. This was then taken into account by developing an empirical statistical downscaling (ESD) modeling approach which integrated discrete wavelet multiresolution analysis for reconstructing local hydrometeorological processes (predictand : precipitation and streamflow on the Seine river catchment) based on a large-scale predictor (SLP over the Euro-Atlantic sector) on a monthly time-step. This approach

  5. Superconducting materials for large scale applications

    International Nuclear Information System (INIS)

    Dew-Hughes, D.

    1975-01-01

    Applications of superconductors capable of carrying large current densities in large-scale electrical devices are examined. Discussions are included on critical current density, superconducting materials available, and future prospects for improved superconducting materials. (JRD)

  6. Large-scale influences in near-wall turbulence.

    Science.gov (United States)

    Hutchins, Nicholas; Marusic, Ivan

    2007-03-15

    Hot-wire data acquired in a high Reynolds number facility are used to illustrate the need for adequate scale separation when considering the coherent structure in wall-bounded turbulence. It is found that a large-scale motion in the log region becomes increasingly comparable in energy to the near-wall cycle as the Reynolds number increases. Through decomposition of fluctuating velocity signals, it is shown that this large-scale motion has a distinct modulating influence on the small-scale energy (akin to amplitude modulation). Reassessment of DNS data, in light of these results, shows similar trends, with the rate and intensity of production due to the near-wall cycle subject to a modulating influence from the largest-scale motions.

  7. Neural networks supporting audiovisual integration for speech: A large-scale lesion study.

    Science.gov (United States)

    Hickok, Gregory; Rogalsky, Corianne; Matchin, William; Basilakos, Alexandra; Cai, Julia; Pillay, Sara; Ferrill, Michelle; Mickelsen, Soren; Anderson, Steven W; Love, Tracy; Binder, Jeffrey; Fridriksson, Julius

    2018-06-01

    Auditory and visual speech information are often strongly integrated resulting in perceptual enhancements for audiovisual (AV) speech over audio alone and sometimes yielding compelling illusory fusion percepts when AV cues are mismatched, the McGurk-MacDonald effect. Previous research has identified three candidate regions thought to be critical for AV speech integration: the posterior superior temporal sulcus (STS), early auditory cortex, and the posterior inferior frontal gyrus. We assess the causal involvement of these regions (and others) in the first large-scale (N = 100) lesion-based study of AV speech integration. Two primary findings emerged. First, behavioral performance and lesion maps for AV enhancement and illusory fusion measures indicate that classic metrics of AV speech integration are not necessarily measuring the same process. Second, lesions involving superior temporal auditory, lateral occipital visual, and multisensory zones in the STS are the most disruptive to AV speech integration. Further, when AV speech integration fails, the nature of the failure-auditory vs visual capture-can be predicted from the location of the lesions. These findings show that AV speech processing is supported by unimodal auditory and visual cortices as well as multimodal regions such as the STS at their boundary. Motor related frontal regions do not appear to play a role in AV speech integration. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Large-scale laboratory observations of beach morphodynamics and turbulence beneath shoaling and breaking waves

    NARCIS (Netherlands)

    Winter, W. de; Wesselman, D.; Grasso, F.R.; Ruessink, B.G.

    2013-01-01

    In 2012, large-scale laboratory experiments were carried out in the Deltagoot in the framework of the Hydralab IV-funded BARDEXII project. The overall project aims were to examine the effect of swash/groundwater interactions to sand transport and morphological development in the swash zone and,

  9. Learning scale-variant and scale-invariant features for deep image classification

    NARCIS (Netherlands)

    van Noord, Nanne; Postma, Eric

    Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial

  10. Results of Large-Scale Spacecraft Flammability Tests

    Science.gov (United States)

    Ferkul, Paul; Olson, Sandra; Urban, David L.; Ruff, Gary A.; Easton, John; T'ien, James S.; Liao, Ta-Ting T.; Fernandez-Pello, A. Carlos; Torero, Jose L.; Eigenbrand, Christian; hide

    2017-01-01

    For the first time, a large-scale fire was intentionally set inside a spacecraft while in orbit. Testing in low gravity aboard spacecraft had been limited to samples of modest size: for thin fuels the longest samples burned were around 15 cm in length and thick fuel samples have been even smaller. This is despite the fact that fire is a catastrophic hazard for spaceflight and the spread and growth of a fire, combined with its interactions with the vehicle cannot be expected to scale linearly. While every type of occupied structure on earth has been the subject of full scale fire testing, this had never been attempted in space owing to the complexity, cost, risk and absence of a safe location. Thus, there is a gap in knowledge of fire behavior in spacecraft. The recent utilization of large, unmanned, resupply craft has provided the needed capability: a habitable but unoccupied spacecraft in low earth orbit. One such vehicle was used to study the flame spread over a 94 x 40.6 cm thin charring solid (fiberglasscotton fabric). The sample was an order of magnitude larger than anything studied to date in microgravity and was of sufficient scale that it consumed 1.5 of the available oxygen. The experiment which is called Saffire consisted of two tests, forward or concurrent flame spread (with the direction of flow) and opposed flame spread (against the direction of flow). The average forced air speed was 20 cms. For the concurrent flame spread test, the flame size remained constrained after the ignition transient, which is not the case in 1-g. These results were qualitatively different from those on earth where an upward-spreading flame on a sample of this size accelerates and grows. In addition, a curious effect of the chamber size is noted. Compared to previous microgravity work in smaller tunnels, the flame in the larger tunnel spread more slowly, even for a wider sample. This is attributed to the effect of flow acceleration in the smaller tunnels as a result of hot

  11. Scaling identity connects human mobility and social interactions.

    Science.gov (United States)

    Deville, Pierre; Song, Chaoming; Eagle, Nathan; Blondel, Vincent D; Barabási, Albert-László; Wang, Dashun

    2016-06-28

    Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality.

  12. Interaction Between Flow Elements in Large Enclosures

    DEFF Research Database (Denmark)

    Heiselberg, Per

    and if this influences the overall air flow pattern in the enclosure. The main emphasis has been put on the pathways of chilled free air jets and whether the convective flows from both distributed and concentrated heat sources affect the pathway of the jet and the air flow pattern in the enclosure as a function......In this paper experiments in a scale model are used as a first attempt to investigate how different flow elements such as supply air jets, thermal plumes and free convection flows interact with each other in a large enclosure, if the path of each individual flow element changes...... of the location of the heat source and as a function of the heat supplied by the source....

  13. PKI security in large-scale healthcare networks.

    Science.gov (United States)

    Mantas, Georgios; Lymberopoulos, Dimitrios; Komninos, Nikos

    2012-06-01

    During the past few years a lot of PKI (Public Key Infrastructures) infrastructures have been proposed for healthcare networks in order to ensure secure communication services and exchange of data among healthcare professionals. However, there is a plethora of challenges in these healthcare PKI infrastructures. Especially, there are a lot of challenges for PKI infrastructures deployed over large-scale healthcare networks. In this paper, we propose a PKI infrastructure to ensure security in a large-scale Internet-based healthcare network connecting a wide spectrum of healthcare units geographically distributed within a wide region. Furthermore, the proposed PKI infrastructure facilitates the trust issues that arise in a large-scale healthcare network including multi-domain PKI infrastructures.

  14. Sensemaking in a Value Based Context for Large Scale Complex Engineered Systems

    Science.gov (United States)

    Sikkandar Basha, Nazareen

    The design and the development of Large-Scale Complex Engineered Systems (LSCES) requires the involvement of multiple teams and numerous levels of the organization and interactions with large numbers of people and interdisciplinary departments. Traditionally, requirements-driven Systems Engineering (SE) is used in the design and development of these LSCES. The requirements are used to capture the preferences of the stakeholder for the LSCES. Due to the complexity of the system, multiple levels of interactions are required to elicit the requirements of the system within the organization. Since LSCES involves people and interactions between the teams and interdisciplinary departments, it should be socio-technical in nature. The elicitation of the requirements of most large-scale system projects are subjected to creep in time and cost due to the uncertainty and ambiguity of requirements during the design and development. In an organization structure, the cost and time overrun can occur at any level and iterate back and forth thus increasing the cost and time. To avoid such creep past researches have shown that rigorous approaches such as value based designing can be used to control it. But before the rigorous approaches can be used, the decision maker should have a proper understanding of requirements creep and the state of the system when the creep occurs. Sensemaking is used to understand the state of system when the creep occurs and provide a guidance to decision maker. This research proposes the use of the Cynefin framework, sensemaking framework which can be used in the design and development of LSCES. It can aide in understanding the system and decision making to minimize the value gap due to requirements creep by eliminating ambiguity which occurs during design and development. A sample hierarchical organization is used to demonstrate the state of the system at the occurrence of requirements creep in terms of cost and time using the Cynefin framework. These

  15. On the Elementary Neural Forms of Micro-Interactional Rituals

    DEFF Research Database (Denmark)

    Heinskou, Marie Bruvik; Liebst, Lasse Suonperä

    2016-01-01

    of the neural basis for rhythmic entrainment. The polyvagal theory furthermore challenges IR theory to reconsider the importance of individual biological differences ritual success may not merely be ascribed to interactional effects, but also to reciprocal causality between situations and neurobiological......Randall Collins’s interaction ritual (IR) theory suggests social solidarity as hardwired in the human neurological capacity for rhythmic entrainment. Yet, this article suggests that IR theory may benefit from being tied more firmly to recent neurobiological research, specifically Stephen W. Porges......’s polyvagal theory that proposes autonomic nervous system functioning as a basis for emotions and social behavior. In this perspective, IR theory does not sufficiently acknowledge the human nervous system as a system involving a phylogenetically ordered response hierarchy, of which only one subsystem supports...

  16. Emerging large-scale solar heating applications

    International Nuclear Information System (INIS)

    Wong, W.P.; McClung, J.L.

    2009-01-01

    Currently the market for solar heating applications in Canada is dominated by outdoor swimming pool heating, make-up air pre-heating and domestic water heating in homes, commercial and institutional buildings. All of these involve relatively small systems, except for a few air pre-heating systems on very large buildings. Together these applications make up well over 90% of the solar thermal collectors installed in Canada during 2007. These three applications, along with the recent re-emergence of large-scale concentrated solar thermal for generating electricity, also dominate the world markets. This paper examines some emerging markets for large scale solar heating applications, with a focus on the Canadian climate and market. (author)

  17. Emerging large-scale solar heating applications

    Energy Technology Data Exchange (ETDEWEB)

    Wong, W.P.; McClung, J.L. [Science Applications International Corporation (SAIC Canada), Ottawa, Ontario (Canada)

    2009-07-01

    Currently the market for solar heating applications in Canada is dominated by outdoor swimming pool heating, make-up air pre-heating and domestic water heating in homes, commercial and institutional buildings. All of these involve relatively small systems, except for a few air pre-heating systems on very large buildings. Together these applications make up well over 90% of the solar thermal collectors installed in Canada during 2007. These three applications, along with the recent re-emergence of large-scale concentrated solar thermal for generating electricity, also dominate the world markets. This paper examines some emerging markets for large scale solar heating applications, with a focus on the Canadian climate and market. (author)

  18. The Laplacian spectrum of neural networks

    Science.gov (United States)

    de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

    2014-01-01

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

  19. State-of-the-Art in GPU-Based Large-Scale Volume Visualization

    KAUST Repository

    Beyer, Johanna

    2015-05-01

    This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera- and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. \\'output-sensitive\\' algorithms and system designs. This leads to recent output-sensitive approaches that are \\'ray-guided\\', \\'visualization-driven\\' or \\'display-aware\\'. In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks-the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context-the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

  20. State-of-the-Art in GPU-Based Large-Scale Volume Visualization

    KAUST Repository

    Beyer, Johanna; Hadwiger, Markus; Pfister, Hanspeter

    2015-01-01

    This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera- and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. 'output-sensitive' algorithms and system designs. This leads to recent output-sensitive approaches that are 'ray-guided', 'visualization-driven' or 'display-aware'. In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks-the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context-the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

  1. On unravelling mechanism of interplay between cloud and large scale circulation: a grey area in climate science

    Science.gov (United States)

    De, S.; Agarwal, N. K.; Hazra, Anupam; Chaudhari, Hemantkumar S.; Sahai, A. K.

    2018-04-01

    The interaction between cloud and large scale circulation is much less explored area in climate science. Unfolding the mechanism of coupling between these two parameters is imperative for improved simulation of Indian summer monsoon (ISM) and to reduce imprecision in climate sensitivity of global climate model. This work has made an effort to explore this mechanism with CFSv2 climate model experiments whose cloud has been modified by changing the critical relative humidity (CRH) profile of model during ISM. Study reveals that the variable CRH in CFSv2 has improved the nonlinear interactions between high and low frequency oscillations in wind field (revealed as internal dynamics of monsoon) and modulates realistically the spatial distribution of interactions over Indian landmass during the contrasting monsoon season compared to the existing CRH profile of CFSv2. The lower tropospheric wind error energy in the variable CRH simulation of CFSv2 appears to be minimum due to the reduced nonlinear convergence of error to the planetary scale range from long and synoptic scales (another facet of internal dynamics) compared to as observed from other CRH experiments in normal and deficient monsoons. Hence, the interplay between cloud and large scale circulation through CRH may be manifested as a change in internal dynamics of ISM revealed from scale interactive quasi-linear and nonlinear kinetic energy exchanges in frequency as well as in wavenumber domain during the monsoon period that eventually modify the internal variance of CFSv2 model. Conversely, the reduced wind bias and proper modulation of spatial distribution of scale interaction between the synoptic and low frequency oscillations improve the eastward and northward extent of water vapour flux over Indian landmass that in turn give feedback to the realistic simulation of cloud condensates attributing improved ISM rainfall in CFSv2.

  2. Topology influences performance in the associative memory neural networks

    International Nuclear Information System (INIS)

    Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang

    2006-01-01

    To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures. In this Letter, we found that the memory performance of neural networks obtained through asynchronous updating from 'larger' nodes to 'smaller' nodes are better than asynchronous updating in random order, especially for the scale-free topology. The computational performance of associative memory neural networks linked by the above-mentioned network topologies with the same amounts of nodes (neurons) and edges (synapses) were studied respectively. Along with topologies becoming more random and less locally disordered, we will see that the performance of associative memory neural network is quite improved. By comparing, we show that the regular lattice and random network form two extremes in terms of patterns stability and retrievability. For a network, its patterns stability and retrievability can be largely enhanced by adding a random component or some shortcuts to its structured component. According to the conclusions of this Letter, we can design the associative memory neural networks with high performance and minimal interconnect requirements

  3. Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

    Directory of Open Access Journals (Sweden)

    Lijuan Duan

    2017-01-01

    Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

  4. Large-scale regions of antimatter

    International Nuclear Information System (INIS)

    Grobov, A. V.; Rubin, S. G.

    2015-01-01

    Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era

  5. Large-scale regions of antimatter

    Energy Technology Data Exchange (ETDEWEB)

    Grobov, A. V., E-mail: alexey.grobov@gmail.com; Rubin, S. G., E-mail: sgrubin@mephi.ru [National Research Nuclear University MEPhI (Russian Federation)

    2015-07-15

    Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era.

  6. Visual Data-Analytics of Large-Scale Parallel Discrete-Event Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Ross, Caitlin; Carothers, Christopher D.; Mubarak, Misbah; Carns, Philip; Ross, Robert; Li, Jianping Kelvin; Ma, Kwan-Liu

    2016-11-13

    Parallel discrete-event simulation (PDES) is an important tool in the codesign of extreme-scale systems because PDES provides a cost-effective way to evaluate designs of highperformance computing systems. Optimistic synchronization algorithms for PDES, such as Time Warp, allow events to be processed without global synchronization among the processing elements. A rollback mechanism is provided when events are processed out of timestamp order. Although optimistic synchronization protocols enable the scalability of large-scale PDES, the performance of the simulations must be tuned to reduce the number of rollbacks and provide an improved simulation runtime. To enable efficient large-scale optimistic simulations, one has to gain insight into the factors that affect the rollback behavior and simulation performance. We developed a tool for ROSS model developers that gives them detailed metrics on the performance of their large-scale optimistic simulations at varying levels of simulation granularity. Model developers can use this information for parameter tuning of optimistic simulations in order to achieve better runtime and fewer rollbacks. In this work, we instrument the ROSS optimistic PDES framework to gather detailed statistics about the simulation engine. We have also developed an interactive visualization interface that uses the data collected by the ROSS instrumentation to understand the underlying behavior of the simulation engine. The interface connects real time to virtual time in the simulation and provides the ability to view simulation data at different granularities. We demonstrate the usefulness of our framework by performing a visual analysis of the dragonfly network topology model provided by the CODES simulation framework built on top of ROSS. The instrumentation needs to minimize overhead in order to accurately collect data about the simulation performance. To ensure that the instrumentation does not introduce unnecessary overhead, we perform a

  7. In our own image? Emotional and neural processing differences when observing human-human vs human-robot interactions.

    Science.gov (United States)

    Wang, Yin; Quadflieg, Susanne

    2015-11-01

    Notwithstanding the significant role that human-robot interactions (HRI) will play in the near future, limited research has explored the neural correlates of feeling eerie in response to social robots. To address this empirical lacuna, the current investigation examined brain activity using functional magnetic resonance imaging while a group of participants (n = 26) viewed a series of human-human interactions (HHI) and HRI. Although brain sites constituting the mentalizing network were found to respond to both types of interactions, systematic neural variation across sites signaled diverging social-cognitive strategies during HHI and HRI processing. Specifically, HHI elicited increased activity in the left temporal-parietal junction indicative of situation-specific mental state attributions, whereas HRI recruited the precuneus and the ventromedial prefrontal cortex (VMPFC) suggestive of script-based social reasoning. Activity in the VMPFC also tracked feelings of eeriness towards HRI in a parametric manner, revealing a potential neural correlate for a phenomenon known as the uncanny valley. By demonstrating how understanding social interactions depends on the kind of agents involved, this study highlights pivotal sub-routes of impression formation and identifies prominent challenges in the use of humanoid robots. © The Author (2015). Published by Oxford University Press.

  8. Palmprint and Palmvein Recognition Based on DCNN and A New Large-Scale Contactless Palmvein Dataset

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    2018-03-01

    Full Text Available Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN based scheme, namely P a l m R CNN (short for palmprint/palmvein recognition using CNNs. The effectiveness and efficiency of P a l m R CNN have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.

  9. Iodine oxides in large-scale THAI tests

    International Nuclear Information System (INIS)

    Funke, F.; Langrock, G.; Kanzleiter, T.; Poss, G.; Fischer, K.; Kühnel, A.; Weber, G.; Allelein, H.-J.

    2012-01-01

    Highlights: ► Iodine oxide particles were produced from gaseous iodine and ozone. ► Ozone replaced the effect of ionizing radiation in the large-scale THAI facility. ► The mean diameter of the iodine oxide particles was about 0.35 μm. ► Particle formation was faster than the chemical reaction between iodine and ozone. ► Deposition of iodine oxide particles was slow in the absence of other aerosols. - Abstract: The conversion of gaseous molecular iodine into iodine oxide aerosols has significant relevance in the understanding of the fission product iodine volatility in a LWR containment during severe accidents. In containment, the high radiation field caused by fission products released from the reactor core induces radiolytic oxidation into iodine oxides. To study the characteristics and the behaviour of iodine oxides in large scale, two THAI tests Iod-13 and Iod-14 were performed, simulating radiolytic oxidation of molecular iodine by reaction of iodine with ozone, with ozone injected from an ozone generator. The observed iodine oxides form submicron particles with mean volume-related diameters of about 0.35 μm and show low deposition rates in the THAI tests performed in the absence of other nuclear aerosols. Formation of iodine aerosols from gaseous precursors iodine and ozone is fast as compared to their chemical interaction. The current approach in empirical iodine containment behaviour models in severe accidents, including the radiolytic production of I 2 -oxidizing agents followed by the I 2 oxidation itself, is confirmed by these THAI tests.

  10. Large-Scale Analysis of Art Proportions

    DEFF Research Database (Denmark)

    Jensen, Karl Kristoffer

    2014-01-01

    While literature often tries to impute mathematical constants into art, this large-scale study (11 databases of paintings and photos, around 200.000 items) shows a different truth. The analysis, consisting of the width/height proportions, shows a value of rarely if ever one (square) and with majo......While literature often tries to impute mathematical constants into art, this large-scale study (11 databases of paintings and photos, around 200.000 items) shows a different truth. The analysis, consisting of the width/height proportions, shows a value of rarely if ever one (square...

  11. The Expanded Large Scale Gap Test

    Science.gov (United States)

    1987-03-01

    NSWC TR 86-32 DTIC THE EXPANDED LARGE SCALE GAP TEST BY T. P. LIDDIARD D. PRICE RESEARCH AND TECHNOLOGY DEPARTMENT ’ ~MARCH 1987 Ap~proved for public...arises, to reduce the spread in the LSGT 50% gap value.) The worst charges, such as those with the highest or lowest densities, the largest re-pressed...Arlington, VA 22217 PE 62314N INS3A 1 RJ14E31 7R4TBK 11 TITLE (Include Security CIlmsilficatiorn The Expanded Large Scale Gap Test . 12. PEIRSONAL AUTHOR() T

  12. The scaling of human interactions with city size.

    Science.gov (United States)

    Schläpfer, Markus; Bettencourt, Luís M A; Grauwin, Sébastian; Raschke, Mathias; Claxton, Rob; Smoreda, Zbigniew; West, Geoffrey B; Ratti, Carlo

    2014-09-06

    The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  13. Interaction matters: A perceived social partner alters the neural processing of human speech.

    Science.gov (United States)

    Rice, Katherine; Redcay, Elizabeth

    2016-04-01

    Mounting evidence suggests that social interaction changes how communicative behaviors (e.g., spoken language, gaze) are processed, but the precise neural bases by which social-interactive context may alter communication remain unknown. Various perspectives suggest that live interactions are more rewarding, more attention-grabbing, or require increased mentalizing-thinking about the thoughts of others. Dissociating between these possibilities is difficult because most extant neuroimaging paradigms examining social interaction have not directly compared live paradigms to conventional "offline" (or recorded) paradigms. We developed a novel fMRI paradigm to assess whether and how an interactive context changes the processing of speech matched in content and vocal characteristics. Participants listened to short vignettes--which contained no reference to people or mental states--believing that some vignettes were prerecorded and that others were presented over a real-time audio-feed by a live social partner. In actuality, all speech was prerecorded. Simply believing that speech was live increased activation in each participant's own mentalizing regions, defined using a functional localizer. Contrasting live to recorded speech did not reveal significant differences in attention or reward regions. Further, higher levels of autistic-like traits were associated with altered neural specialization for live interaction. These results suggest that humans engage in ongoing mentalizing about social partners, even when such mentalizing is not explicitly required, illustrating how social context shapes social cognition. Understanding communication in social context has important implications for typical and atypical social processing, especially for disorders like autism where social difficulties are more acute in live interaction. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Imaging of human differentiated 3D neural aggregates using light sheet fluorescence microscopy

    Science.gov (United States)

    Gualda, Emilio J.; Simão, Daniel; Pinto, Catarina; Alves, Paula M.; Brito, Catarina

    2014-01-01

    The development of three dimensional (3D) cell cultures represents a big step for the better understanding of cell behavior and disease in a more natural like environment, providing not only single but multiple cell type interactions in a complex 3D matrix, highly resembling physiological conditions. Light sheet fluorescence microscopy (LSFM) is becoming an excellent tool for fast imaging of such 3D biological structures. We demonstrate the potential of this technique for the imaging of human differentiated 3D neural aggregates in fixed and live samples, namely calcium imaging and cell death processes, showing the power of imaging modality compared with traditional microscopy. The combination of light sheet microscopy and 3D neural cultures will open the door to more challenging experiments involving drug testing at large scale as well as a better understanding of relevant biological processes in a more realistic environment. PMID:25161607

  15. Imaging of human differentiated 3D neural aggregates using light sheet fluorescence microscopy

    Directory of Open Access Journals (Sweden)

    Emilio J Gualda

    2014-08-01

    Full Text Available The development of three dimensional cell cultures represents a big step for the better understanding of cell behavior and disease in a more natural like environment, providing not only single but multiple cell type interactions in a complex three dimensional matrix, highly resembling physiological conditions. Light sheet fluorescence microscopy is becoming an excellent tool for fast imaging of such three-dimensional biological structures. We demonstrate the potential of this technique for the imaging of human differentiated 3D neural aggregates in fixed and live samples, namely calcium imaging and cell death processes, showing the power of imaging modality compared with traditional microscopy. The combination of light sheet microscopy and 3D neural cultures will open the door to more challenging experiments involving drug testing at large scale as well as a better understanding of relevant biological processes in a more realistic environment.

  16. Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure

    Directory of Open Access Journals (Sweden)

    Tyler Epp

    2018-03-01

    Full Text Available Structural Health Monitoring (SHM has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC structures.

  17. Large scale and big data processing and management

    CERN Document Server

    Sakr, Sherif

    2014-01-01

    Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-bas

  18. Large-scale micromagnetics simulations with dipolar interaction using all-to-all communications

    Directory of Open Access Journals (Sweden)

    Hiroshi Tsukahara

    2016-05-01

    Full Text Available We implement on our micromagnetics simulator low-complexity parallel fast-Fourier-transform algorithms, which reduces the frequency of all-to-all communications from six to two times. Almost all the computation time of micromagnetics simulation is taken up by the calculation of the magnetostatic field which can be calculated using the fast Fourier transform method. The results show that the simulation time is decreased with good scalability, even if the micromagentics simulation is performed using 8192 physical cores. This high parallelization effect enables large-scale micromagentics simulation using over one billion to be performed. Because massively parallel computing is needed to simulate the magnetization dynamics of real permanent magnets composed of many micron-sized grains, it is expected that our simulator reveals how magnetization dynamics influences the coercivity of the permanent magnet.

  19. Orphan nuclear receptor TLX recruits histone deacetylases to repress transcription and regulate neural stem cell proliferation.

    Science.gov (United States)

    Sun, Guoqiang; Yu, Ruth T; Evans, Ronald M; Shi, Yanhong

    2007-09-25

    TLX is a transcription factor that is essential for neural stem cell proliferation and self-renewal. However, the molecular mechanism of TLX-mediated neural stem cell proliferation and self-renewal is largely unknown. We show here that TLX recruits histone deacetylases (HDACs) to its downstream target genes to repress their transcription, which in turn regulates neural stem cell proliferation. TLX interacts with HDAC3 and HDAC5 in neural stem cells. The HDAC5-interaction domain was mapped to TLX residues 359-385, which contains a conserved nuclear receptor-coregulator interaction motif IXXLL. Both HDAC3 and HDAC5 have been shown to be recruited to the promoters of TLX target genes along with TLX in neural stem cells. Recruitment of HDACs led to transcriptional repression of TLX target genes, the cyclin-dependent kinase inhibitor, p21(CIP1/WAF1)(p21), and the tumor suppressor gene, pten. Either inhibition of HDAC activity or knockdown of HDAC expression led to marked induction of p21 and pten gene expression and dramatically reduced neural stem cell proliferation, suggesting that the TLX-interacting HDACs play an important role in neural stem cell proliferation. Moreover, expression of a TLX peptide containing the minimal HDAC5 interaction domain disrupted the TLX-HDAC5 interaction. Disruption of this interaction led to significant induction of p21 and pten gene expression and to dramatic inhibition of neural stem cell proliferation. Taken together, these findings demonstrate a mechanism for neural stem cell proliferation through transcriptional repression of p21 and pten gene expression by TLX-HDAC interactions.

  20. Modeling a full-scale primary sedimentation tank using artificial neural networks.

    Science.gov (United States)

    Gamal El-Din, A; Smith, D W

    2002-05-01

    Modeling the performance of full-scale primary sedimentation tanks has been commonly done using regression-based models, which are empirical relationships derived strictly from observed daily average influent and effluent data. Another approach to model a sedimentation tank is using a hydraulic efficiency model that utilizes tracer studies to characterize the performance of model sedimentation tanks based on eddy diffusion. However, the use of hydraulic efficiency models to predict the dynamic behavior of a full-scale sedimentation tank is very difficult as the development of such models has been done using controlled studies of model tanks. In this paper, another type of model, namely artificial neural network modeling approach, is used to predict the dynamic response of a full-scale primary sedimentation tank. The neuralmodel consists of two separate networks, one uses flow and influent total suspended solids data in order to predict the effluent total suspended solids from the tank, and the other makes predictions of the effluent chemical oxygen demand using data of the flow and influent chemical oxygen demand as inputs. An extensive sampling program was conducted in order to collect a data set to be used in training and validating the networks. A systematic approach was used in the building process of the model which allowed the identification of a parsimonious neural model that is able to learn (and not memorize) from past data and generalize very well to unseen data that were used to validate the model. Theresults seem very promising. The potential of using the model as part of a real-time process control system isalso discussed.

  1. Large scale cluster computing workshop

    International Nuclear Information System (INIS)

    Dane Skow; Alan Silverman

    2002-01-01

    Recent revolutions in computer hardware and software technologies have paved the way for the large-scale deployment of clusters of commodity computers to address problems heretofore the domain of tightly coupled SMP processors. Near term projects within High Energy Physics and other computing communities will deploy clusters of scale 1000s of processors and be used by 100s to 1000s of independent users. This will expand the reach in both dimensions by an order of magnitude from the current successful production facilities. The goals of this workshop were: (1) to determine what tools exist which can scale up to the cluster sizes foreseen for the next generation of HENP experiments (several thousand nodes) and by implication to identify areas where some investment of money or effort is likely to be needed. (2) To compare and record experimences gained with such tools. (3) To produce a practical guide to all stages of planning, installing, building and operating a large computing cluster in HENP. (4) To identify and connect groups with similar interest within HENP and the larger clustering community

  2. Web tools for large-scale 3D biological images and atlases

    Directory of Open Access Journals (Sweden)

    Husz Zsolt L

    2012-06-01

    Full Text Available Abstract Background Large-scale volumetric biomedical image data of three or more dimensions are a significant challenge for distributed browsing and visualisation. Many images now exceed 10GB which for most users is too large to handle in terms of computer RAM and network bandwidth. This is aggravated when users need to access tens or hundreds of such images from an archive. Here we solve the problem for 2D section views through archive data delivering compressed tiled images enabling users to browse through very-large volume data in the context of a standard web-browser. The system provides an interactive visualisation for grey-level and colour 3D images including multiple image layers and spatial-data overlay. Results The standard Internet Imaging Protocol (IIP has been extended to enable arbitrary 2D sectioning of 3D data as well a multi-layered images and indexed overlays. The extended protocol is termed IIP3D and we have implemented a matching server to deliver the protocol and a series of Ajax/Javascript client codes that will run in an Internet browser. We have tested the server software on a low-cost linux-based server for image volumes up to 135GB and 64 simultaneous users. The section views are delivered with response times independent of scale and orientation. The exemplar client provided multi-layer image views with user-controlled colour-filtering and overlays. Conclusions Interactive browsing of arbitrary sections through large biomedical-image volumes is made possible by use of an extended internet protocol and efficient server-based image tiling. The tools open the possibility of enabling fast access to large image archives without the requirement of whole image download and client computers with very large memory configurations. The system was demonstrated using a range of medical and biomedical image data extending up to 135GB for a single image volume.

  3. Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets

    Directory of Open Access Journals (Sweden)

    Max Lam

    2017-11-01

    Full Text Available Here, we present a large (n = 107,207 genome-wide association study (GWAS of general cognitive ability (“g”, further enhanced by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with general cognitive ability. Results showed significant enrichment for genes causing Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis implicated the biological processes of neurogenesis and synaptic regulation, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker, and LY97241, a potassium channel inhibitor. Transcriptome-wide and epigenome-wide analysis revealed that the implicated loci were enriched for genes expressed across all brain regions (most strongly in the cerebellum. Enrichment was exclusive to genes expressed in neurons but not oligodendrocytes or astrocytes. Finally, we report genetic correlations between cognitive ability and disparate phenotypes including psychiatric disorders, several autoimmune disorders, longevity, and maternal age at first birth.

  4. Large-Scale Agriculture and Outgrower Schemes in Ethiopia

    DEFF Research Database (Denmark)

    Wendimu, Mengistu Assefa

    , the impact of large-scale agriculture and outgrower schemes on productivity, household welfare and wages in developing countries is highly contentious. Chapter 1 of this thesis provides an introduction to the study, while also reviewing the key debate in the contemporary land ‘grabbing’ and historical large...... sugarcane outgrower scheme on household income and asset stocks. Chapter 5 examines the wages and working conditions in ‘formal’ large-scale and ‘informal’ small-scale irrigated agriculture. The results in Chapter 2 show that moisture stress, the use of untested planting materials, and conflict over land...... commands a higher wage than ‘formal’ large-scale agriculture, while rather different wage determination mechanisms exist in the two sectors. Human capital characteristics (education and experience) partly explain the differences in wages within the formal sector, but play no significant role...

  5. Economically viable large-scale hydrogen liquefaction

    Science.gov (United States)

    Cardella, U.; Decker, L.; Klein, H.

    2017-02-01

    The liquid hydrogen demand, particularly driven by clean energy applications, will rise in the near future. As industrial large scale liquefiers will play a major role within the hydrogen supply chain, production capacity will have to increase by a multiple of today’s typical sizes. The main goal is to reduce the total cost of ownership for these plants by increasing energy efficiency with innovative and simple process designs, optimized in capital expenditure. New concepts must ensure a manageable plant complexity and flexible operability. In the phase of process development and selection, a dimensioning of key equipment for large scale liquefiers, such as turbines and compressors as well as heat exchangers, must be performed iteratively to ensure technological feasibility and maturity. Further critical aspects related to hydrogen liquefaction, e.g. fluid properties, ortho-para hydrogen conversion, and coldbox configuration, must be analysed in detail. This paper provides an overview on the approach, challenges and preliminary results in the development of efficient as well as economically viable concepts for large-scale hydrogen liquefaction.

  6. 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

    Science.gov (United States)

    Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

    2018-04-15

    This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Large scale chromatographic separations using continuous displacement chromatography (CDC)

    International Nuclear Information System (INIS)

    Taniguchi, V.T.; Doty, A.W.; Byers, C.H.

    1988-01-01

    A process for large scale chromatographic separations using a continuous chromatography technique is described. The process combines the advantages of large scale batch fixed column displacement chromatography with conventional analytical or elution continuous annular chromatography (CAC) to enable large scale displacement chromatography to be performed on a continuous basis (CDC). Such large scale, continuous displacement chromatography separations have not been reported in the literature. The process is demonstrated with the ion exchange separation of a binary lanthanide (Nd/Pr) mixture. The process is, however, applicable to any displacement chromatography separation that can be performed using conventional batch, fixed column chromatography

  8. Large Scale Processes and Extreme Floods in Brazil

    Science.gov (United States)

    Ribeiro Lima, C. H.; AghaKouchak, A.; Lall, U.

    2016-12-01

    Persistent large scale anomalies in the atmospheric circulation and ocean state have been associated with heavy rainfall and extreme floods in water basins of different sizes across the world. Such studies have emerged in the last years as a new tool to improve the traditional, stationary based approach in flood frequency analysis and flood prediction. Here we seek to advance previous studies by evaluating the dominance of large scale processes (e.g. atmospheric rivers/moisture transport) over local processes (e.g. local convection) in producing floods. We consider flood-prone regions in Brazil as case studies and the role of large scale climate processes in generating extreme floods in such regions is explored by means of observed streamflow, reanalysis data and machine learning methods. The dynamics of the large scale atmospheric circulation in the days prior to the flood events are evaluated based on the vertically integrated moisture flux and its divergence field, which are interpreted in a low-dimensional space as obtained by machine learning techniques, particularly supervised kernel principal component analysis. In such reduced dimensional space, clusters are obtained in order to better understand the role of regional moisture recycling or teleconnected moisture in producing floods of a given magnitude. The convective available potential energy (CAPE) is also used as a measure of local convection activities. We investigate for individual sites the exceedance probability in which large scale atmospheric fluxes dominate the flood process. Finally, we analyze regional patterns of floods and how the scaling law of floods with drainage area responds to changes in the climate forcing mechanisms (e.g. local vs large scale).

  9. Herbivore-induced plant volatiles and tritrophic interactions across spatial scales.

    Science.gov (United States)

    Aartsma, Yavanna; Bianchi, Felix J J A; van der Werf, Wopke; Poelman, Erik H; Dicke, Marcel

    2017-12-01

    Herbivore-induced plant volatiles (HIPVs) are an important cue used in herbivore location by carnivorous arthropods such as parasitoids. The effects of plant volatiles on parasitoids have been well characterised at small spatial scales, but little research has been done on their effects at larger spatial scales. The spatial matrix of volatiles ('volatile mosaic') within which parasitoids locate their hosts is dynamic and heterogeneous. It is shaped by the spatial pattern of HIPV-emitting plants, the concentration, chemical composition and breakdown of the emitted HIPV blends, and by environmental factors such as wind, turbulence and vegetation that affect transport and mixing of odour plumes. The volatile mosaic may be exploited differentially by different parasitoid species, in relation to species traits such as sensory ability to perceive volatiles and the physical ability to move towards the source. Understanding how HIPVs influence parasitoids at larger spatial scales is crucial for our understanding of tritrophic interactions and sustainable pest management in agriculture. However, there is a large gap in our knowledge on how volatiles influence the process of host location by parasitoids at the landscape scale. Future studies should bridge the gap between the chemical and behavioural ecology of tritrophic interactions and landscape ecology. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  10. Computing in Large-Scale Dynamic Systems

    NARCIS (Netherlands)

    Pruteanu, A.S.

    2013-01-01

    Software applications developed for large-scale systems have always been difficult to de- velop due to problems caused by the large number of computing devices involved. Above a certain network size (roughly one hundred), necessary services such as code updating, topol- ogy discovery and data

  11. Fires in large scale ventilation systems

    International Nuclear Information System (INIS)

    Gregory, W.S.; Martin, R.A.; White, B.W.; Nichols, B.D.; Smith, P.R.; Leslie, I.H.; Fenton, D.L.; Gunaji, M.V.; Blythe, J.P.

    1991-01-01

    This paper summarizes the experience gained simulating fires in large scale ventilation systems patterned after ventilation systems found in nuclear fuel cycle facilities. The series of experiments discussed included: (1) combustion aerosol loading of 0.61x0.61 m HEPA filters with the combustion products of two organic fuels, polystyrene and polymethylemethacrylate; (2) gas dynamic and heat transport through a large scale ventilation system consisting of a 0.61x0.61 m duct 90 m in length, with dampers, HEPA filters, blowers, etc.; (3) gas dynamic and simultaneous transport of heat and solid particulate (consisting of glass beads with a mean aerodynamic diameter of 10μ) through the large scale ventilation system; and (4) the transport of heat and soot, generated by kerosene pool fires, through the large scale ventilation system. The FIRAC computer code, designed to predict fire-induced transients in nuclear fuel cycle facility ventilation systems, was used to predict the results of experiments (2) through (4). In general, the results of the predictions were satisfactory. The code predictions for the gas dynamics, heat transport, and particulate transport and deposition were within 10% of the experimentally measured values. However, the code was less successful in predicting the amount of soot generation from kerosene pool fires, probably due to the fire module of the code being a one-dimensional zone model. The experiments revealed a complicated three-dimensional combustion pattern within the fire room of the ventilation system. Further refinement of the fire module within FIRAC is needed. (orig.)

  12. Dislocation-stacking fault tetrahedron interaction: what can we learn from atomic-scale modelling

    International Nuclear Information System (INIS)

    Osetsky, Yu.N.; Stoller, R.E.; Matsukawa, Y.

    2004-01-01

    The high number density of stacking fault tetrahedra (SFTs) observed in irradiated fcc metals suggests that they should contribute to radiation-induced hardening and, therefore, taken into account when estimating mechanical properties changes of irradiated materials. The central issue is describing the individual interaction between a moving dislocation and an SFT, which is characterized by a very fine size scale, ∼100 nm. This scale is amenable to both in situ TEM experiments and large-scale atomic modelling. In this paper we present results of an atomistic simulation of dislocation-SFT interactions using molecular dynamics (MD). The results are compared with observations from in situ deformation experiments. It is demonstrated that in some cases the simulations and experimental observations are quite similar, suggesting a reasonable interpretation of experimental observations

  13. Large-scale Complex IT Systems

    OpenAIRE

    Sommerville, Ian; Cliff, Dave; Calinescu, Radu; Keen, Justin; Kelly, Tim; Kwiatkowska, Marta; McDermid, John; Paige, Richard

    2011-01-01

    This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that identifies the major challen...

  14. Large-scale complex IT systems

    OpenAIRE

    Sommerville, Ian; Cliff, Dave; Calinescu, Radu; Keen, Justin; Kelly, Tim; Kwiatkowska, Marta; McDermid, John; Paige, Richard

    2012-01-01

    12 pages, 2 figures This paper explores the issues around the construction of large-scale complex systems which are built as 'systems of systems' and suggests that there are fundamental reasons, derived from the inherent complexity in these systems, why our current software engineering methods and techniques cannot be scaled up to cope with the engineering challenges of constructing such systems. It then goes on to propose a research and education agenda for software engineering that ident...

  15. First Mile Challenges for Large-Scale IoT

    KAUST Repository

    Bader, Ahmed; Elsawy, Hesham; Gharbieh, Mohammad; Alouini, Mohamed-Slim; Adinoyi, Abdulkareem; Alshaalan, Furaih

    2017-01-01

    The Internet of Things is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the sheer scale of spatial traffic intensity that must be accommodated, primarily in the uplink direction. To that end

  16. Linear scale bounds on dark matter--dark radiation interactions and connection with the small scale crisis of cold dark matter

    DEFF Research Database (Denmark)

    Hannestad, Steen; Archidiacono, Maria; Bohr, Sebastian

    2017-01-01

    One of the open questions in modern cosmology is the small scale crisis of the cold dark matter paradigm. Increasing attention has recently been devoted to self-interacting dark matter models as a possible answer. However, solving the so-called "missing satellites" problem requires in addition...... the presence of an extra relativistic particle (dubbed dark radiation) scattering with dark matter in the early universe. Here we investigate the impact of different theoretical models devising dark matter dark radiation interactions on large scale cosmological observables. We use cosmic microwave background...... data to put constraints on the dark radiation component and its coupling to dark matter. We find that the values of the coupling allowed by the data imply a cut-off scale of the halo mass function consistent with the one required to match the observations of satellites in the Milky Way....

  17. Restoring large-scale brain networks in PTSD and related disorders: a proposal for neuroscientifically-informed treatment interventions

    Directory of Open Access Journals (Sweden)

    Ruth A. Lanius

    2015-03-01

    Full Text Available Background: Three intrinsic connectivity networks in the brain, namely the central executive, salience, and default mode networks, have been identified as crucial to the understanding of higher cognitive functioning, and the functioning of these networks has been suggested to be impaired in psychopathology, including posttraumatic stress disorder (PTSD. Objective: 1 To describe three main large-scale networks of the human brain; 2 to discuss the functioning of these neural networks in PTSD and related symptoms; and 3 to offer hypotheses for neuroscientifically-informed interventions based on treating the abnormalities observed in these neural networks in PTSD and related disorders. Method: Literature relevant to this commentary was reviewed. Results: Increasing evidence for altered functioning of the central executive, salience, and default mode networks in PTSD has been demonstrated. We suggest that each network is associated with specific clinical symptoms observed in PTSD, including cognitive dysfunction (central executive network, increased and decreased arousal/interoception (salience network, and an altered sense of self (default mode network. Specific testable neuroscientifically-informed treatments aimed to restore each of these neural networks and related clinical dysfunction are proposed. Conclusions: Neuroscientifically-informed treatment interventions will be essential to future research agendas aimed at targeting specific PTSD and related symptoms.

  18. Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

    Science.gov (United States)

    Wolf-Grosse, Tobias; Esau, Igor; Reuder, Joachim

    2017-06-01

    Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s-1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water

  19. Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

    Directory of Open Access Journals (Sweden)

    T. Wolf-Grosse

    2017-06-01

    Full Text Available Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s−1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES experiments with the Parallelised Large-Eddy Simulation Model (PALM for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a

  20. Prospects for large scale electricity storage in Denmark

    DEFF Research Database (Denmark)

    Krog Ekman, Claus; Jensen, Søren Højgaard

    2010-01-01

    In a future power systems with additional wind power capacity there will be an increased need for large scale power management as well as reliable balancing and reserve capabilities. Different technologies for large scale electricity storage provide solutions to the different challenges arising w...

  1. Disruption of posteromedial large-scale neural communication predicts recovery from coma.

    Science.gov (United States)

    Silva, Stein; de Pasquale, Francesco; Vuillaume, Corine; Riu, Beatrice; Loubinoux, Isabelle; Geeraerts, Thomas; Seguin, Thierry; Bounes, Vincent; Fourcade, Olivier; Demonet, Jean-Francois; Péran, Patrice

    2015-12-08

    We hypothesize that the major consciousness deficit observed in coma is due to the breakdown of long-range neuronal communication supported by precuneus and posterior cingulate cortex (PCC), and that prognosis depends on a specific connectivity pattern in these networks. We compared 27 prospectively recruited comatose patients who had severe brain injury (Glasgow Coma Scale score Coma Recovery Scale-Revised (CRS-R). Patients who were comatose showed a significant disruption of functional connectivity of brain areas spontaneously synchronized with PCC, globally notwithstanding etiology. The functional connectivity strength between PCC and medial prefrontal cortex (mPFC) was significantly different between comatose patients who went on to recover and those who eventually scored an unfavorable outcome 3 months after brain injury (Kruskal-Wallis test, p coma. Sparing of functional connectivity between PCC and mPFC may predict patient outcome, and further studies are needed to substantiate this potential prognosis biomarker. © 2015 American Academy of Neurology.

  2. Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

    Science.gov (United States)

    Xiao, Min; Zheng, Wei Xing; Cao, Jinde

    2013-01-01

    Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

  3. Refining and validating the Social Interaction Anxiety Scale and the Social Phobia Scale.

    Science.gov (United States)

    Carleton, R Nicholas; Collimore, Kelsey C; Asmundson, Gordon J G; McCabe, Randi E; Rowa, Karen; Antony, Martin M

    2009-01-01

    The Social Interaction Anxiety Scale and Social Phobia Scale are companion measures for assessing symptoms of social anxiety and social phobia. The scales have good reliability and validity across several samples, however, exploratory and confirmatory factor analyses have yielded solutions comprising substantially different item content and factor structures. These discrepancies are likely the result of analyzing items from each scale separately or simultaneously. The current investigation sets out to assess items from those scales, both simultaneously and separately, using exploratory and confirmatory factor analyses in an effort to resolve the factor structure. Participants consisted of a clinical sample (n 5353; 54% women) and an undergraduate sample (n 5317; 75% women) who completed the Social Interaction Anxiety Scale and Social Phobia Scale, along with additional fear-related measures to assess convergent and discriminant validity. A three-factor solution with a reduced set of items was found to be most stable, irrespective of whether the items from each scale are assessed together or separately. Items from the Social Interaction Anxiety Scale represented one factor, whereas items from the Social Phobia Scale represented two other factors. Initial support for scale and factor validity, along with implications and recommendations for future research, is provided. (c) 2009 Wiley-Liss, Inc.

  4. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    Science.gov (United States)

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  5. Evolution of scaling emergence in large-scale spatial epidemic spreading.

    Science.gov (United States)

    Wang, Lin; Li, Xiang; Zhang, Yi-Qing; Zhang, Yan; Zhang, Kan

    2011-01-01

    Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified. In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.

  6. The Effect of Large Scale Salinity Gradient on Langmuir Turbulence

    Science.gov (United States)

    Fan, Y.; Jarosz, E.; Yu, Z.; Jensen, T.; Sullivan, P. P.; Liang, J.

    2017-12-01

    Langmuir circulation (LC) is believed to be one of the leading order causes of turbulent mixing in the upper ocean. It is important for momentum and heat exchange across the mixed layer (ML) and directly impact the dynamics and thermodynamics in the upper ocean and lower atmosphere including the vertical distributions of chemical, biological, optical, and acoustic properties. Based on Craik and Leibovich (1976) theory, large eddy simulation (LES) models have been developed to simulate LC in the upper ocean, yielding new insights that could not be obtained from field observations and turbulent closure models. Due its high computational cost, LES models are usually limited to small domain sizes and cannot resolve large-scale flows. Furthermore, most LES models used in the LC simulations use periodic boundary conditions in the horizontal direction, which assumes the physical properties (i.e. temperature and salinity) and expected flow patterns in the area of interest are of a periodically repeating nature so that the limited small LES domain is representative for the larger area. Using periodic boundary condition can significantly reduce computational effort in problems, and it is a good assumption for isotropic shear turbulence. However, LC is anisotropic (McWilliams et al 1997) and was observed to be modulated by crosswind tidal currents (Kukulka et al 2011). Using symmetrical domains, idealized LES studies also indicate LC could interact with oceanic fronts (Hamlington et al 2014) and standing internal waves (Chini and Leibovich, 2005). The present study expands our previous LES modeling investigations of Langmuir turbulence to the real ocean conditions with large scale environmental motion that features fresh water inflow into the study region. Large scale gradient forcing is introduced to the NCAR LES model through scale separation analysis. The model is applied to a field observation in the Gulf of Mexico in July, 2016 when the measurement site was impacted by

  7. Large-Scale Structure and Hyperuniformity of Amorphous Ices

    Science.gov (United States)

    Martelli, Fausto; Torquato, Salvatore; Giovambattista, Nicolas; Car, Roberto

    2017-09-01

    We investigate the large-scale structure of amorphous ices and transitions between their different forms by quantifying their large-scale density fluctuations. Specifically, we simulate the isothermal compression of low-density amorphous ice (LDA) and hexagonal ice to produce high-density amorphous ice (HDA). Both HDA and LDA are nearly hyperuniform; i.e., they are characterized by an anomalous suppression of large-scale density fluctuations. By contrast, in correspondence with the nonequilibrium phase transitions to HDA, the presence of structural heterogeneities strongly suppresses the hyperuniformity and the system becomes hyposurficial (devoid of "surface-area fluctuations"). Our investigation challenges the largely accepted "frozen-liquid" picture, which views glasses as structurally arrested liquids. Beyond implications for water, our findings enrich our understanding of pressure-induced structural transformations in glasses.

  8. Tracking Single Units in Chronic, Large Scale, Neural Recordings for Brain Machine Interface Applications

    Directory of Open Access Journals (Sweden)

    Ahmed eEleryan

    2014-07-01

    Full Text Available In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs. Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 seconds processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions.

  9. Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale

    Science.gov (United States)

    Kuzu, Guray; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

    2013-01-01

    Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than ‘blind’ docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested ‘difficult’ cases in a docking-benchmark dataset, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26% to 66%, and 57% of the interactions were successfully predicted in an ‘unbiased’ scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome-scale. We constructed the structural network of ERK interacting proteins as a case study. PMID:23590674

  10. Analysis using large-scale ringing data

    Directory of Open Access Journals (Sweden)

    Baillie, S. R.

    2004-06-01

    Full Text Available Birds are highly mobile organisms and there is increasing evidence that studies at large spatial scales are needed if we are to properly understand their population dynamics. While classical metapopulation models have rarely proved useful for birds, more general metapopulation ideas involving collections of populations interacting within spatially structured landscapes are highly relevant (Harrison, 1994. There is increasing interest in understanding patterns of synchrony, or lack of synchrony, between populations and the environmental and dispersal mechanisms that bring about these patterns (Paradis et al., 2000. To investigate these processes we need to measure abundance, demographic rates and dispersal at large spatial scales, in addition to gathering data on relevant environmental variables. There is an increasing realisation that conservation needs to address rapid declines of common and widespread species (they will not remain so if such trends continue as well as the management of small populations that are at risk of extinction. While the knowledge needed to support the management of small populations can often be obtained from intensive studies in a few restricted areas, conservation of widespread species often requires information on population trends and processes measured at regional, national and continental scales (Baillie, 2001. While management prescriptions for widespread populations may initially be developed from a small number of local studies or experiments, there is an increasing need to understand how such results will scale up when applied across wider areas. There is also a vital role for monitoring at large spatial scales both in identifying such population declines and in assessing population recovery. Gathering data on avian abundance and demography at large spatial scales usually relies on the efforts of large numbers of skilled volunteers. Volunteer studies based on ringing (for example Constant Effort Sites [CES

  11. The PREP Pipeline: Standardized preprocessing for large-scale EEG analysis

    Directory of Open Access Journals (Sweden)

    Nima eBigdelys Shamlo

    2015-06-01

    Full Text Available The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode/.

  12. The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

    Science.gov (United States)

    Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

    2015-01-01

    The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

  13. multi scale analysis of a function by neural networks elementary derivatives functions

    International Nuclear Information System (INIS)

    Chikhi, A.; Gougam, A.; Chafa, F.

    2006-01-01

    Recently, the wavelet network has been introduced as a special neural network supported by the wavelet theory . Such networks constitute a tool for function approximation problems as it has been already proved in reference . Our present work deals with this model, treating a multi scale analysis of a function. We have then used a linear expansion of a given function in wavelets, neglecting the usual translation parameters. We investigate two training operations. The first one consists on an optimization of the output synaptic layer, the second one, optimizing the output function with respect to scale parameters. We notice a temporary merging of the scale parameters leading to some interesting results : new elementary derivatives units emerge, representing a new elementary task, which is the derivative of the output task

  14. Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks

    International Nuclear Information System (INIS)

    Amanifard, N.; Nariman-Zadeh, N.; Farahani, M.H.; Khalkhali, A.

    2008-01-01

    Over the past 15 years there have been several research efforts to capture the stall inception nature in axial flow compressors. However previous analytical models could not explain the formation of short-length-scale stall cells. This paper provides a new model based on evolved GMDH neural network for transient evolution of multiple short-length-scale stall cells in an axial compressor. Genetic Algorithms (GAs) are also employed for optimal design of connectivity configuration of such GMDH-type neural networks. In this way, low-pass filter (LPF) pressure trace near the rotor leading edge is modelled with respect to the variation of pressure coefficient, flow rate coefficient, and number of rotor rotations which are defined as inputs

  15. Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

    Directory of Open Access Journals (Sweden)

    Christian Nowke

    2018-06-01

    Full Text Available Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

  16. MicroEcos: Micro-Scale Explorations of Large-Scale Late Pleistocene Ecosystems

    Science.gov (United States)

    Gellis, B. S.

    2017-12-01

    Pollen data can inform the reconstruction of early-floral environments by providing data for artistic representations of what early-terrestrial ecosystems looked like, and how existing terrestrial landscapes have evolved. For example, what did the Bighorn Basin look like when large ice sheets covered modern Canada, the Yellowstone Plateau had an ice cap, and the Bighorn Mountains were mantled with alpine glaciers? MicroEcos is an immersive, multimedia project that aims to strengthen human-nature connections through the understanding and appreciation of biological ecosystems. Collected pollen data elucidates flora that are visible in the fossil record - associated with the Late-Pleistocene - and have been illustrated and described in botanical literature. It aims to make scientific data accessible and interesting to all audiences through a series of interactive-digital sculptures, large-scale photography and field-based videography. While this project is driven by scientific data, it is rooted in deeply artistic and outreach-based practices, which include broad artistic practices, e.g.: digital design, illustration, photography, video and sound design. Using 3D modeling and printing technology MicroEcos centers around a series of 3D-printed models of the Last Canyon rock shelter on the Wyoming and Montana border, Little Windy Hill pond site in Wyoming's Medicine Bow National Forest, and Natural Trap Cave site in Wyoming's Big Horn Basin. These digital, interactive-3D sculpture provide audiences with glimpses of three-dimensional Late-Pleistocene environments, and helps create dialogue of how grass, sagebrush, and spruce based ecosystems form. To help audiences better contextualize how MicroEcos bridges notions of time, space, and place, modern photography and videography of the Last Canyon, Little Windy Hill and Natural Trap Cave sites surround these 3D-digital reconstructions.

  17. Double inflation: A possible resolution of the large-scale structure problem

    International Nuclear Information System (INIS)

    Turner, M.S.; Villumsen, J.V.; Vittorio, N.; Silk, J.; Juszkiewicz, R.

    1986-11-01

    A model is presented for the large-scale structure of the universe in which two successive inflationary phases resulted in large small-scale and small large-scale density fluctuations. This bimodal density fluctuation spectrum in an Ω = 1 universe dominated by hot dark matter leads to large-scale structure of the galaxy distribution that is consistent with recent observational results. In particular, large, nearly empty voids and significant large-scale peculiar velocity fields are produced over scales of ∼100 Mpc, while the small-scale structure over ≤ 10 Mpc resembles that in a low density universe, as observed. Detailed analytical calculations and numerical simulations are given of the spatial and velocity correlations. 38 refs., 6 figs

  18. Large-scale fracture mechancis testing -- requirements and possibilities

    International Nuclear Information System (INIS)

    Brumovsky, M.

    1993-01-01

    Application of fracture mechanics to very important and/or complicated structures, like reactor pressure vessels, brings also some questions about the reliability and precision of such calculations. These problems become more pronounced in cases of elastic-plastic conditions of loading and/or in parts with non-homogeneous materials (base metal and austenitic cladding, property gradient changes through material thickness) or with non-homogeneous stress fields (nozzles, bolt threads, residual stresses etc.). For such special cases some verification by large-scale testing is necessary and valuable. This paper discusses problems connected with planning of such experiments with respect to their limitations, requirements to a good transfer of received results to an actual vessel. At the same time, an analysis of possibilities of small-scale model experiments is also shown, mostly in connection with application of results between standard, small-scale and large-scale experiments. Experience from 30 years of large-scale testing in SKODA is used as an example to support this analysis. 1 fig

  19. Ethics of large-scale change

    DEFF Research Database (Denmark)

    Arler, Finn

    2006-01-01

    , which kind of attitude is appropriate when dealing with large-scale changes like these from an ethical point of view. Three kinds of approaches are discussed: Aldo Leopold's mountain thinking, the neoclassical economists' approach, and finally the so-called Concentric Circle Theories approach...

  20. A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language.

    Directory of Open Access Journals (Sweden)

    Bruno Golosio

    Full Text Available Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.

  1. Neural Network for Sparse Reconstruction

    Directory of Open Access Journals (Sweden)

    Qingfa Li

    2014-01-01

    Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.

  2. Event-driven simulation of neural population synchronization facilitated by electrical coupling.

    Science.gov (United States)

    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.

  3. Statistical downscaling of precipitation using long short-term memory recurrent neural networks

    Science.gov (United States)

    Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

    2017-11-01

    Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

  4. Comparison Between Overtopping Discharge in Small and Large Scale Models

    DEFF Research Database (Denmark)

    Helgason, Einar; Burcharth, Hans F.

    2006-01-01

    The present paper presents overtopping measurements from small scale model test performed at the Haudraulic & Coastal Engineering Laboratory, Aalborg University, Denmark and large scale model tests performed at the Largde Wave Channel,Hannover, Germany. Comparison between results obtained from...... small and large scale model tests show no clear evidence of scale effects for overtopping above a threshold value. In the large scale model no overtopping was measured for waveheights below Hs = 0.5m as the water sunk into the voids between the stones on the crest. For low overtopping scale effects...

  5. Protein-material interactions: From micro-to-nano scale

    International Nuclear Information System (INIS)

    Tsapikouni, Theodora S.; Missirlis, Yannis F.

    2008-01-01

    The article presents a survey on the significance of protein-material interactions, the mechanisms which control them and the techniques used for their study. Protein-surface interactions play a key role in regenerative medicine, drug delivery, biosensor technology and chromatography, while it is related to various undesired effects such as biofouling and bio-prosthetic malfunction. Although the effects of protein-surface interaction concern the micro-scale, being sometimes obvious even with bare eyes, they derive from biophysical events at the nano-scale. The sequential steps for protein adsorption involve events at the single biomolecule level and the forces driving or inhibiting protein adsorption act at the molecular level too. Following the scaling of protein-surface interactions, various techniques have been developed for their study both in the micro- and nano-scale. Protein labelling with radioisotopes or fluorescent probes, colorimetric assays and the quartz crystal microbalance were the first techniques used to monitor protein adsorption isotherms, while the surface force apparatus was used to measure the interaction forces between protein layers at the micro-scale. Recently, more elaborate techniques like total internal reflection fluorescence (TIRF), Fourier transform infrared spectroscopy (FTIR), surface plasmon resonance, Raman spectroscopy, ellipsometry and time of flight secondary ion mass spectrometry (ToF-SIMS) have been applied for the investigation of protein density, structure or orientation at the interfaces. However, a turning point in the study of protein interactions with the surfaces was the invention and the wide-spread use of atomic force microscopy (AFM) which can both image single protein molecules on surfaces and directly measure the interaction force

  6. Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin

    Directory of Open Access Journals (Sweden)

    Mohsen Ghafoorian

    2017-01-01

    In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN. We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.

  7. Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain

    Science.gov (United States)

    Barrett, Lisa Feldman; Satpute, Ajay

    2013-01-01

    Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure-function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain. PMID:23352202

  8. A review of sensing technologies for small and large-scale touch panels

    Science.gov (United States)

    Akhtar, Humza; Kemao, Qian; Kakarala, Ramakrishna

    2017-06-01

    A touch panel is an input device for human computer interaction. It consists of a network of sensors, a sampling circuit and a micro controller for detecting and locating a touch input. Touch input can come from either finger or stylus depending upon the type of touch technology. These touch panels provide an intuitive and collaborative workspace so that people can perform various tasks with the use of their fingers instead of traditional input devices like keyboard and mouse. Touch sensing technology is not new. At the time of this writing, various technologies are available in the market and this paper reviews the most common ones. We review traditional designs and sensing algorithms for touch technology. We also observe that due to its various strengths, capacitive touch will dominate the large-scale touch panel industry in years to come. In the end, we discuss the motivation for doing academic research on large-scale panels.

  9. Limitations and tradeoffs in synchronization of large-scale networks with uncertain links

    Science.gov (United States)

    Diwadkar, Amit; Vaidya, Umesh

    2016-01-01

    The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies. PMID:27067994

  10. Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

    Science.gov (United States)

    Johnson, J L

    1994-09-10

    The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

  11. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  12. Deciphering the clinical effect of drugs through large-scale data integration

    DEFF Research Database (Denmark)

    Kjærulff, Sonny Kim

    . This work demonstrates the power of a strategy that uses clinical data mining in association with chemical biology in order to reduce the search space and aid identification of novel drug actions. The second article described in chapter 3 outlines a high confidence side-effect-drug interaction dataset. We...... demonstrates the importance of using high-confidence drug-side-effect data in deciphering the effect of small molecules in humans. In summary, this thesis presents computational systems chemical biology approaches that can help identify clinical effects of small molecules through large-scale data integration...

  13. Large-scale shell model calculations for the N=126 isotones Po-Pu

    International Nuclear Information System (INIS)

    Caurier, E.; Rejmund, M.; Grawe, H.

    2003-04-01

    Large-scale shell model calculations were performed in the full Z=82-126 proton model space π(Oh 9/2 , 1f 7/2 , Oi 13/2 , 2p 3/2 , 1f 5/2 , 2p 1/2 ) employing the code NATHAN. The modified Kuo-Herling interaction was used, no truncation was applied up to protactinium (Z=91) and seniority truncation beyond. The results are compared to experimental data including binding energies, level schemes and electromagnetic transition rates. An overall excellent agreement is obtained for states that can be described in this model space. Limitations of the approach with respect to excitations across the Z=82 and N=126 shells and deficiencies of the interaction are discussed. (orig.)

  14. The influence of Seychelles Dome on the large scale Tropical Variability

    Science.gov (United States)

    Manola, Iris; Selten, Frank; Hazeleger, Wilco

    2013-04-01

    . Vecchi, 2010: Submonthly Indian Ocean cooling events and their interaction with large-scale conditions. J. Climate, 23, 700-716. -Tozuka, T., T. Yokoi, and T. Yamagata, 2010: A modeling study of interannual variations of the Seychelles Dome. J. Geophys. Res., 115, C04005, doi:10.1029/2009JC005547.

  15. Needs, opportunities, and options for large scale systems research

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, G.L.

    1984-10-01

    The Office of Energy Research was recently asked to perform a study of Large Scale Systems in order to facilitate the development of a true large systems theory. It was decided to ask experts in the fields of electrical engineering, chemical engineering and manufacturing/operations research for their ideas concerning large scale systems research. The author was asked to distribute a questionnaire among these experts to find out their opinions concerning recent accomplishments and future research directions in large scale systems research. He was also requested to convene a conference which included three experts in each area as panel members to discuss the general area of large scale systems research. The conference was held on March 26--27, 1984 in Pittsburgh with nine panel members, and 15 other attendees. The present report is a summary of the ideas presented and the recommendations proposed by the attendees.

  16. Timing of Formal Phase Safety Reviews for Large-Scale Integrated Hazard Analysis

    Science.gov (United States)

    Massie, Michael J.; Morris, A. Terry

    2010-01-01

    Integrated hazard analysis (IHA) is a process used to identify and control unacceptable risk. As such, it does not occur in a vacuum. IHA approaches must be tailored to fit the system being analyzed. Physical, resource, organizational and temporal constraints on large-scale integrated systems impose additional direct or derived requirements on the IHA. The timing and interaction between engineering and safety organizations can provide either benefits or hindrances to the overall end product. The traditional approach for formal phase safety review timing and content, which generally works well for small- to moderate-scale systems, does not work well for very large-scale integrated systems. This paper proposes a modified approach to timing and content of formal phase safety reviews for IHA. Details of the tailoring process for IHA will describe how to avoid temporary disconnects in major milestone reviews and how to maintain a cohesive end-to-end integration story particularly for systems where the integrator inherently has little to no insight into lower level systems. The proposal has the advantage of allowing the hazard analysis development process to occur as technical data normally matures.

  17. United States Temperature and Precipitation Extremes: Phenomenology, Large-Scale Organization, Physical Mechanisms and Model Representation

    Science.gov (United States)

    Black, R. X.

    2017-12-01

    We summarize results from a project focusing on regional temperature and precipitation extremes over the continental United States. Our project introduces a new framework for evaluating these extremes emphasizing their (a) large-scale organization, (b) underlying physical sources (including remote-excitation and scale-interaction) and (c) representation in climate models. Results to be reported include the synoptic-dynamic behavior, seasonality and secular variability of cold waves, dry spells and heavy rainfall events in the observational record. We also study how the characteristics of such extremes are systematically related to Northern Hemisphere planetary wave structures and thus planetary- and hemispheric-scale forcing (e.g., those associated with major El Nino events and Arctic sea ice change). The underlying physics of event onset are diagnostically quantified for different categories of events. Finally, the representation of these extremes in historical coupled climate model simulations is studied and the origins of model biases are traced using new metrics designed to assess the large-scale atmospheric forcing of local extremes.

  18. Seismic safety in conducting large-scale blasts

    Science.gov (United States)

    Mashukov, I. V.; Chaplygin, V. V.; Domanov, V. P.; Semin, A. A.; Klimkin, M. A.

    2017-09-01

    In mining enterprises to prepare hard rocks for excavation a drilling and blasting method is used. With the approach of mining operations to settlements the negative effect of large-scale blasts increases. To assess the level of seismic impact of large-scale blasts the scientific staff of Siberian State Industrial University carried out expertise for coal mines and iron ore enterprises. Determination of the magnitude of surface seismic vibrations caused by mass explosions was performed using seismic receivers, an analog-digital converter with recording on a laptop. The registration results of surface seismic vibrations during production of more than 280 large-scale blasts at 17 mining enterprises in 22 settlements are presented. The maximum velocity values of the Earth’s surface vibrations are determined. The safety evaluation of seismic effect was carried out according to the permissible value of vibration velocity. For cases with exceedance of permissible values recommendations were developed to reduce the level of seismic impact.

  19. [Research Progress on the Interaction Effects and Its Neural Mechanisms between Physical Fatigue and Mental Fatigue].

    Science.gov (United States)

    Zhang, Lixin; Zhang, Chuncui; He, Feng; Zhao, Xin; Qi, Hongzhi; Wan, Baikun; Ming, Dong

    2015-10-01

    Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.

  20. Image-based Exploration of Large-Scale Pathline Fields

    KAUST Repository

    Nagoor, Omniah H.

    2014-05-27

    While real-time applications are nowadays routinely used in visualizing large nu- merical simulations and volumes, handling these large-scale datasets requires high-end graphics clusters or supercomputers to process and visualize them. However, not all users have access to powerful clusters. Therefore, it is challenging to come up with a visualization approach that provides insight to large-scale datasets on a single com- puter. Explorable images (EI) is one of the methods that allows users to handle large data on a single workstation. Although it is a view-dependent method, it combines both exploration and modification of visual aspects without re-accessing the original huge data. In this thesis, we propose a novel image-based method that applies the concept of EI in visualizing large flow-field pathlines data. The goal of our work is to provide an optimized image-based method, which scales well with the dataset size. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.

  1. Event-driven processing for hardware-efficient neural spike sorting

    Science.gov (United States)

    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.

  2. Neural network regulation driven by autonomous neural firings

    Science.gov (United States)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  3. Improving Large-scale Storage System Performance via Topology-aware and Balanced Data Placement

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Feiyi [ORNL; Oral, H Sarp [ORNL; Vazhkudai, Sudharshan S [ORNL

    2014-01-01

    With the advent of big data, the I/O subsystems of large-scale compute clusters are becoming a center of focus, with more applications putting greater demands on end-to-end I/O performance. These subsystems are often complex in design. They comprise of multiple hardware and software layers to cope with the increasing capacity, capability and scalability requirements of data intensive applications. The sharing nature of storage resources and the intrinsic interactions across these layers make it to realize user-level, end-to-end performance gains a great challenge. We propose a topology-aware resource load balancing strategy to improve per-application I/O performance. We demonstrate the effectiveness of our algorithm on an extreme-scale compute cluster, Titan, at the Oak Ridge Leadership Computing Facility (OLCF). Our experiments with both synthetic benchmarks and a real-world application show that, even under congestion, our proposed algorithm can improve large-scale application I/O performance significantly, resulting in both the reduction of application run times and higher resolution simulation runs.

  4. Large-Scale Brain Simulation and Disorders of Consciousness. Mapping Technical and Conceptual Issues

    Directory of Open Access Journals (Sweden)

    Michele Farisco

    2018-04-01

    Full Text Available Modeling and simulations have gained a leading position in contemporary attempts to describe, explain, and quantitatively predict the human brain’s operations. Computer models are highly sophisticated tools developed to achieve an integrated knowledge of the brain with the aim of overcoming the actual fragmentation resulting from different neuroscientific approaches. In this paper we investigate the plausibility of simulation technologies for emulation of consciousness and the potential clinical impact of large-scale brain simulation on the assessment and care of disorders of consciousness (DOCs, e.g., Coma, Vegetative State/Unresponsive Wakefulness Syndrome, Minimally Conscious State. Notwithstanding their technical limitations, we suggest that simulation technologies may offer new solutions to old practical problems, particularly in clinical contexts. We take DOCs as an illustrative case, arguing that the simulation of neural correlates of consciousness is potentially useful for improving treatments of patients with DOCs.

  5. Soil-Structure Interaction for Non-Slender, Large-Diameter Offshore Monopiles

    DEFF Research Database (Denmark)

    Sørensen, Søren Peder Hyldal

    conducted. The initial part of p-y curves for non-slender piles has been investigated by means of numerical modelling. The general behaviour of eccentrically loaded non-slender piles has been investigated by physical modelling. These tests have been conducted in the pressure tank at Aalborg University....... The monopile foundation concept has been employed as the foundation for the majority of the currently installed offshore wind turbines. Therefore, this PhD thesis concerns the soil-pile interaction for non-slender, large-diameter offshore piles. A combination of numerical and physical modelling has been....... Hence, the application of an overburden pressure is possible. The timescale of the backfill process and the compaction of soil material backfilled around piles in storm conditions have been investigated by means of large-scale physical modelling....

  6. Homogenization of Large-Scale Movement Models in Ecology

    Science.gov (United States)

    Garlick, M.J.; Powell, J.A.; Hooten, M.B.; McFarlane, L.R.

    2011-01-01

    A difficulty in using diffusion models to predict large scale animal population dispersal is that individuals move differently based on local information (as opposed to gradients) in differing habitat types. This can be accommodated by using ecological diffusion. However, real environments are often spatially complex, limiting application of a direct approach. Homogenization for partial differential equations has long been applied to Fickian diffusion (in which average individual movement is organized along gradients of habitat and population density). We derive a homogenization procedure for ecological diffusion and apply it to a simple model for chronic wasting disease in mule deer. Homogenization allows us to determine the impact of small scale (10-100 m) habitat variability on large scale (10-100 km) movement. The procedure generates asymptotic equations for solutions on the large scale with parameters defined by small-scale variation. The simplicity of this homogenization procedure is striking when compared to the multi-dimensional homogenization procedure for Fickian diffusion,and the method will be equally straightforward for more complex models. ?? 2010 Society for Mathematical Biology.

  7. Separating true V0's from combinatoric background with a neural network

    International Nuclear Information System (INIS)

    Justice, M.

    1997-01-01

    A feedforward multilayered neural network has been trained to ''recognize'' true V0's in the presence of a large combinatoric background using simulated data for 2 GeV/nucleon Ni + Cu interactions. The resulting neural network filter has been applied to actual data from the EOS TPC experiment. An enhancement of signal to background over more traditional selection mechanisms has been observed. (orig.)

  8. Toward automatic time-series forecasting using neural networks.

    Science.gov (United States)

    Yan, Weizhong

    2012-07-01

    Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

  9. Status: Large-scale subatmospheric cryogenic systems

    International Nuclear Information System (INIS)

    Peterson, T.

    1989-01-01

    In the late 1960's and early 1970's an interest in testing and operating RF cavities at 1.8K motivated the development and construction of four large (300 Watt) 1.8K refrigeration systems. in the past decade, development of successful superconducting RF cavities and interest in obtaining higher magnetic fields with the improved Niobium-Titanium superconductors has once again created interest in large-scale 1.8K refrigeration systems. The L'Air Liquide plant for Tore Supra is a recently commissioned 300 Watt 1.8K system which incorporates new technology, cold compressors, to obtain the low vapor pressure for low temperature cooling. CEBAF proposes to use cold compressors to obtain 5KW at 2.0K. Magnetic refrigerators of 10 Watt capacity or higher at 1.8K are now being developed. The state of the art of large-scale refrigeration in the range under 4K will be reviewed. 28 refs., 4 figs., 7 tabs

  10. Large-scale weakly supervised object localization via latent category learning.

    Science.gov (United States)

    Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

    2015-04-01

    Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

  11. A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

    Science.gov (United States)

    Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

    2016-01-01

    Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

  12. Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

    Science.gov (United States)

    Casey, Matthew C.; Hickman, Duncan L.; Pavlou, Athanasios; Sadler, James R. E.

    2011-06-01

    Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

  13. Workshop Report on Additive Manufacturing for Large-Scale Metal Components - Development and Deployment of Metal Big-Area-Additive-Manufacturing (Large-Scale Metals AM) System

    Energy Technology Data Exchange (ETDEWEB)

    Babu, Sudarsanam Suresh [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility; Love, Lonnie J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility; Peter, William H. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility; Dehoff, Ryan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Manufacturing Demonstration Facility

    2016-05-01

    Additive manufacturing (AM) is considered an emerging technology that is expected to transform the way industry can make low-volume, high value complex structures. This disruptive technology promises to replace legacy manufacturing methods for the fabrication of existing components in addition to bringing new innovation for new components with increased functional and mechanical properties. This report outlines the outcome of a workshop on large-scale metal additive manufacturing held at Oak Ridge National Laboratory (ORNL) on March 11, 2016. The charter for the workshop was outlined by the Department of Energy (DOE) Advanced Manufacturing Office program manager. The status and impact of the Big Area Additive Manufacturing (BAAM) for polymer matrix composites was presented as the background motivation for the workshop. Following, the extension of underlying technology to low-cost metals was proposed with the following goals: (i) High deposition rates (approaching 100 lbs/h); (ii) Low cost (<$10/lbs) for steel, iron, aluminum, nickel, as well as, higher cost titanium, (iii) large components (major axis greater than 6 ft) and (iv) compliance of property requirements. The above concept was discussed in depth by representatives from different industrial sectors including welding, metal fabrication machinery, energy, construction, aerospace and heavy manufacturing. In addition, DOE’s newly launched High Performance Computing for Manufacturing (HPC4MFG) program was reviewed. This program will apply thermo-mechanical models to elucidate deeper understanding of the interactions between design, process, and materials during additive manufacturing. Following these presentations, all the attendees took part in a brainstorming session where everyone identified the top 10 challenges in large-scale metal AM from their own perspective. The feedback was analyzed and grouped in different categories including, (i) CAD to PART software, (ii) selection of energy source, (iii

  14. Optimization of large-scale industrial systems : an emerging method

    Energy Technology Data Exchange (ETDEWEB)

    Hammache, A.; Aube, F.; Benali, M.; Cantave, R. [Natural Resources Canada, Varennes, PQ (Canada). CANMET Energy Technology Centre

    2006-07-01

    This paper reviewed optimization methods of large-scale industrial production systems and presented a novel systematic multi-objective and multi-scale optimization methodology. The methodology was based on a combined local optimality search with global optimality determination, and advanced system decomposition and constraint handling. The proposed method focused on the simultaneous optimization of the energy, economy and ecology aspects of industrial systems (E{sup 3}-ISO). The aim of the methodology was to provide guidelines for decision-making strategies. The approach was based on evolutionary algorithms (EA) with specifications including hybridization of global optimality determination with a local optimality search; a self-adaptive algorithm to account for the dynamic changes of operating parameters and design variables occurring during the optimization process; interactive optimization; advanced constraint handling and decomposition strategy; and object-oriented programming and parallelization techniques. Flowcharts of the working principles of the basic EA were presented. It was concluded that the EA uses a novel decomposition and constraint handling technique to enhance the Pareto solution search procedure for multi-objective problems. 6 refs., 9 figs.

  15. Short-Range Temporal Interactions in Sleep; Hippocampal Spike Avalanches Support a Large Milieu of Sequential Activity Including Replay.

    Directory of Open Access Journals (Sweden)

    J Matthew Mahoney

    Full Text Available Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networks for recollection during future cognition. Here we decode sequential firing structure within spike avalanches of all pyramidal cells recorded in sleeping rats after running in a circular track. We find that short sequences that combine into multiple long sequences capture the majority of the sequential structure during sleep, including replay of hippocampal place cells. The ensemble, however, is not optimized for maximally producing the behavior-enriched episode. Thus behavioral programming of sequential correlations occurs at the level of short-range interactions, not whole behavioral sequences and these short sequences are assembled into a large and complex milieu that could support complex memory consolidation.

  16. Load reduction test method of similarity theory and BP neural networks of large cranes

    Science.gov (United States)

    Yang, Ruigang; Duan, Zhibin; Lu, Yi; Wang, Lei; Xu, Gening

    2016-01-01

    Static load tests are an important means of supervising and detecting a crane's lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes.

  17. An Novel Architecture of Large-scale Communication in IOT

    Science.gov (United States)

    Ma, Wubin; Deng, Su; Huang, Hongbin

    2018-03-01

    In recent years, many scholars have done a great deal of research on the development of Internet of Things and networked physical systems. However, few people have made the detailed visualization of the large-scale communications architecture in the IOT. In fact, the non-uniform technology between IPv6 and access points has led to a lack of broad principles of large-scale communications architectures. Therefore, this paper presents the Uni-IPv6 Access and Information Exchange Method (UAIEM), a new architecture and algorithm that addresses large-scale communications in the IOT.

  18. Benefits of transactive memory systems in large-scale development

    OpenAIRE

    Aivars, Sablis

    2016-01-01

    Context. Large-scale software development projects are those consisting of a large number of teams, maybe even spread across multiple locations, and working on large and complex software tasks. That means that neither a team member individually nor an entire team holds all the knowledge about the software being developed and teams have to communicate and coordinate their knowledge. Therefore, teams and team members in large-scale software development projects must acquire and manage expertise...

  19. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  20. Numerical modeling of in-vessel melt water interaction in large scale PWR`s

    Energy Technology Data Exchange (ETDEWEB)

    Kolev, N.I. [Siemens AG, KWU NA-M, Erlangen (Germany)

    1998-01-01

    This paper presents a comparison between IVA4 simulations and FARO L14, L20 experiments. Both experiments were performed with the same geometry but under different initial pressures, 51 and 20 bar respectively. A pretest prediction for test L21 which is intended to be performed under an initial pressure of 5 bar is also presented. The strong effect of the volume expansion of the evaporating water at low pressure is demonstrated. An in-vessel simulation for a 1500 MW el. PWR is presented. The insight gained from this study is: that at no time are conditions for the feared large scale melt-water intermixing at low pressure in force, with this due to the limiting effect of the expansion process which accelerates the melt and the water into all available flow paths. (author)

  1. Study of a large scale neutron measurement channel

    International Nuclear Information System (INIS)

    Amarouayache, Anissa; Ben Hadid, Hayet.

    1982-12-01

    A large scale measurement channel allows the processing of the signal coming from an unique neutronic sensor, during three different running modes: impulses, fluctuations and current. The study described in this note includes three parts: - A theoretical study of the large scale channel and its brief description are given. The results obtained till now in that domain are presented. - The fluctuation mode is thoroughly studied and the improvements to be done are defined. The study of a fluctuation linear channel with an automatic commutation of scales is described and the results of the tests are given. In this large scale channel, the method of data processing is analogical. - To become independent of the problems generated by the use of a an analogical processing of the fluctuation signal, a digital method of data processing is tested. The validity of that method is improved. The results obtained on a test system realized according to this method are given and a preliminary plan for further research is defined [fr

  2. Social Interaction Affects Neural Outcomes of Sign Language Learning As a Foreign Language in Adults.

    Science.gov (United States)

    Yusa, Noriaki; Kim, Jungho; Koizumi, Masatoshi; Sugiura, Motoaki; Kawashima, Ryuta

    2017-01-01

    Children naturally acquire a language in social contexts where they interact with their caregivers. Indeed, research shows that social interaction facilitates lexical and phonological development at the early stages of child language acquisition. It is not clear, however, whether the relationship between social interaction and learning applies to adult second language acquisition of syntactic rules. Does learning second language syntactic rules through social interactions with a native speaker or without such interactions impact behavior and the brain? The current study aims to answer this question. Adult Japanese participants learned a new foreign language, Japanese sign language (JSL), either through a native deaf signer or via DVDs. Neural correlates of acquiring new linguistic knowledge were investigated using functional magnetic resonance imaging (fMRI). The participants in each group were indistinguishable in terms of their behavioral data after the instruction. The fMRI data, however, revealed significant differences in the neural activities between two groups. Significant activations in the left inferior frontal gyrus (IFG) were found for the participants who learned JSL through interactions with the native signer. In contrast, no cortical activation change in the left IFG was found for the group who experienced the same visual input for the same duration via the DVD presentation. Given that the left IFG is involved in the syntactic processing of language, spoken or signed, learning through social interactions resulted in an fMRI signature typical of native speakers: activation of the left IFG. Thus, broadly speaking, availability of communicative interaction is necessary for second language acquisition and this results in observed changes in the brain.

  3. Large-Scale Participation: A Case Study of a Participatory Approach to Developing a New Public Library

    DEFF Research Database (Denmark)

    Dalsgaard, Peter; Eriksson, Eva

    2013-01-01

    In this paper, we present a case study of a participatory project that focuses on interaction in large-scale design, namely, the development of the new Urban Mediaspace Aarhus. This project, which has been under way for ten years, embodies a series of issues that arise when participatory design...

  4. On the Phenomenology of an Accelerated Large-Scale Universe

    Directory of Open Access Journals (Sweden)

    Martiros Khurshudyan

    2016-10-01

    Full Text Available In this review paper, several new results towards the explanation of the accelerated expansion of the large-scale universe is discussed. On the other hand, inflation is the early-time accelerated era and the universe is symmetric in the sense of accelerated expansion. The accelerated expansion of is one of the long standing problems in modern cosmology, and physics in general. There are several well defined approaches to solve this problem. One of them is an assumption concerning the existence of dark energy in recent universe. It is believed that dark energy is responsible for antigravity, while dark matter has gravitational nature and is responsible, in general, for structure formation. A different approach is an appropriate modification of general relativity including, for instance, f ( R and f ( T theories of gravity. On the other hand, attempts to build theories of quantum gravity and assumptions about existence of extra dimensions, possible variability of the gravitational constant and the speed of the light (among others, provide interesting modifications of general relativity applicable to problems of modern cosmology, too. In particular, here two groups of cosmological models are discussed. In the first group the problem of the accelerated expansion of large-scale universe is discussed involving a new idea, named the varying ghost dark energy. On the other hand, the second group contains cosmological models addressed to the same problem involving either new parameterizations of the equation of state parameter of dark energy (like varying polytropic gas, or nonlinear interactions between dark energy and dark matter. Moreover, for cosmological models involving varying ghost dark energy, massless particle creation in appropriate radiation dominated universe (when the background dynamics is due to general relativity is demonstrated as well. Exploring the nature of the accelerated expansion of the large-scale universe involving generalized

  5. Capabilities of the Large-Scale Sediment Transport Facility

    Science.gov (United States)

    2016-04-01

    pump flow meters, sediment trap weigh tanks , and beach profiling lidar. A detailed discussion of the original LSTF features and capabilities can be...ERDC/CHL CHETN-I-88 April 2016 Approved for public release; distribution is unlimited. Capabilities of the Large-Scale Sediment Transport...describes the Large-Scale Sediment Transport Facility (LSTF) and recent upgrades to the measurement systems. The purpose of these upgrades was to increase

  6. Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

    Science.gov (United States)

    Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

    2015-01-01

    Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

  7. Spatiotemporal property and predictability of large-scale human mobility

    Science.gov (United States)

    Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

    2018-04-01

    Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

  8. Problems of large-scale vertically-integrated aquaculture

    Energy Technology Data Exchange (ETDEWEB)

    Webber, H H; Riordan, P F

    1976-01-01

    The problems of vertically-integrated aquaculture are outlined; they are concerned with: species limitations (in the market, biological and technological); site selection, feed, manpower needs, and legal, institutional and financial requirements. The gaps in understanding of, and the constraints limiting, large-scale aquaculture are listed. Future action is recommended with respect to: types and diversity of species to be cultivated, marketing, biotechnology (seed supply, disease control, water quality and concerted effort), siting, feed, manpower, legal and institutional aids (granting of water rights, grants, tax breaks, duty-free imports, etc.), and adequate financing. The last of hard data based on experience suggests that large-scale vertically-integrated aquaculture is a high risk enterprise, and with the high capital investment required, banks and funding institutions are wary of supporting it. Investment in pilot projects is suggested to demonstrate that large-scale aquaculture can be a fully functional and successful business. Construction and operation of such pilot farms is judged to be in the interests of both the public and private sector.

  9. Large-scale computing with Quantum Espresso

    International Nuclear Information System (INIS)

    Giannozzi, P.; Cavazzoni, C.

    2009-01-01

    This paper gives a short introduction to Quantum Espresso: a distribution of software for atomistic simulations in condensed-matter physics, chemical physics, materials science, and to its usage in large-scale parallel computing.

  10. Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer's dementia

    Science.gov (United States)

    Karageorgiou, Elissaios; Lewis, Scott M.; Riley McCarten, J.; Leuthold, Arthur C.; Hemmy, Laura S.; McPherson, Susan E.; Rottunda, Susan J.; Rubins, David M.; Georgopoulos, Apostolos P.

    2012-10-01

    In previous work (Georgopoulos et al 2007 J. Neural Eng. 4 349-55) we reported on the use of magnetoencephalographic (MEG) synchronous neural interactions (SNI) as a functional biomarker in Alzheimer's dementia (AD) diagnosis. Here we report on the application of canonical correlation analysis to investigate the relations between SNI and cognitive neuropsychological (NP) domains in AD patients. First, we performed individual correlations between each SNI and each NP, which provided an initial link between SNI and specific cognitive tests. Next, we performed factor analysis on each set, followed by a canonical correlation analysis between the derived SNI and NP factors. This last analysis optimally associated the entire MEG signal with cognitive function. The results revealed that SNI as a whole were mostly associated with memory and language, and, slightly less, executive function, processing speed and visuospatial abilities, thus differentiating functions subserved by the frontoparietal and the temporal cortices. These findings provide a direct interpretation of the information carried by the SNI and set the basis for identifying specific neural disease phenotypes according to cognitive deficits.

  11. Volume Visualization and Compositing on Large-Scale Displays Using Handheld Touchscreen Interaction

    KAUST Repository

    Gastelum, Cristhopper Jacobo Armenta

    2011-07-27

    Advances in the physical sciences have progressively delivered ever increasing, already extremely large data sets to be analyzed. High performance volume rendering has become critical to the scientists for a better understanding of the massive amounts of data to be visualized. Cluster based rendering systems have become the base line to achieve the power and flexibility required to perform such task. Furthermore, display arrays have become the most suitable solution to display these data sets at their natural size and resolution which can be critical for human perception and evaluation. The work in this thesis aims at improving the scalability and usability of volume rendering systems that target visualization on display arrays. The first part deals with improving the performance by introducing the implementations of two parallel compositing algorithms for volume rendering: direct send and binary swap. The High quality Volume Rendering (HVR) framework has been extended to accommodate parallel compositing where previously only serial compositing was possible. The preliminary results show improvements in the compositing times for direct send even for a small number of processors. Unfortunately, the results of binary swap exhibit a negative behavior. This is due to the naive use of the graphics hardware blending mechanism. The expensive transfers account for the lengthy compositing times. The second part targets the development of scalable and intuitive interaction mechanisms. It introduces the development of a new client application for multitouch tablet devices, like the Apple iPad. The main goal is to provide the HVR framework, that has been extended to use tiled displays, a more intuitive and portable interaction mechanism that can get advantage of the new environment. The previous client is a PC application for the typical desktop settings that use a mouse and keyboard as sources of interaction. The current implementation of the client lets the user steer and

  12. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  13. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  14. Stochastic inflation lattice simulations: Ultra-large scale structure of the universe

    International Nuclear Information System (INIS)

    Salopek, D.S.

    1990-11-01

    Non-Gaussian fluctuations for structure formation may arise in inflation from the nonlinear interaction of long wavelength gravitational and scalar fields. Long wavelength fields have spatial gradients α -1 triangledown small compared to the Hubble radius, and they are described in terms of classical random fields that are fed by short wavelength quantum noise. Lattice Langevin calculations are given for a ''toy model'' with a scalar field interacting with an exponential potential where one can obtain exact analytic solutions of the Fokker-Planck equation. For single scalar field models that are consistent with current microwave background fluctuations, the fluctuations are Gaussian. However, for scales much larger than our observable Universe, one expects large metric fluctuations that are non-Guassian. This example illuminates non-Gaussian models involving multiple scalar fields which are consistent with current microwave background limits. 21 refs., 3 figs

  15. VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

    Directory of Open Access Journals (Sweden)

    Andrew E. Webb

    2017-06-01

    Full Text Available Background Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges, particularly when working with entire proteomes (all protein coding sequences in a genome from a large number of species. Methods We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and Perl and is designed to run within a UNIX environment. Results We have benchmarked VESPA and our results show that the method is consistent, performs well on both large scale and smaller scale datasets, and produces results in line with previously published datasets. Discussion Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: http://www.mol-evol.org/VESPA.

  16. Large-Scale Context-Aware Volume Navigation using Dynamic Insets

    KAUST Repository

    Al-Awami, Ali

    2012-07-01

    Latest developments in electron microscopy (EM) technology produce high resolution images that enable neuro-scientists to identify and put together the complex neural connections in a nervous system. However, because of the massive size and underlying complexity of this kind of data, processing, navigation and analysis suffer drastically in terms of time and effort. In this work, we propose the use of state-of- the-art navigation techniques, such as dynamic insets, built on a peta-scale volume visualization framework to provide focus and context-awareness to help neuro-scientists in their mission to analyze, reconstruct, navigate and explore EM neuroscience data.

  17. An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

    Science.gov (United States)

    2012-01-01

    Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

  18. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, J.; Schutz, J.; Chirayath, V.; Li, A.

    2017-12-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Net's convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign.Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users' input against pre-classified coral imagery to gauge their accuracy and utilizes in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  19. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved

    2017-01-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  20. RESTRUCTURING OF THE LARGE-SCALE SPRINKLERS

    Directory of Open Access Journals (Sweden)

    Paweł Kozaczyk

    2016-09-01

    Full Text Available One of the best ways for agriculture to become independent from shortages of precipitation is irrigation. In the seventies and eighties of the last century a number of large-scale sprinklers in Wielkopolska was built. At the end of 1970’s in the Poznan province 67 sprinklers with a total area of 6400 ha were installed. The average size of the sprinkler reached 95 ha. In 1989 there were 98 sprinklers, and the area which was armed with them was more than 10 130 ha. The study was conducted on 7 large sprinklers with the area ranging from 230 to 520 hectares in 1986÷1998. After the introduction of the market economy in the early 90’s and ownership changes in agriculture, large-scale sprinklers have gone under a significant or total devastation. Land on the State Farms of the State Agricultural Property Agency has leased or sold and the new owners used the existing sprinklers to a very small extent. This involved a change in crop structure, demand structure and an increase in operating costs. There has also been a threefold increase in electricity prices. Operation of large-scale irrigation encountered all kinds of barriers in practice and limitations of system solutions, supply difficulties, high levels of equipment failure which is not inclined to rational use of available sprinklers. An effect of a vision of the local area was to show the current status of the remaining irrigation infrastructure. The adopted scheme for the restructuring of Polish agriculture was not the best solution, causing massive destruction of assets previously invested in the sprinkler system.

  1. Large-scale synthesis of YSZ nanopowder by Pechini method

    Indian Academy of Sciences (India)

    Administrator

    structure and chemical purity of 99⋅1% by inductively coupled plasma optical emission spectroscopy on a large scale. Keywords. Sol–gel; yttria-stabilized zirconia; large scale; nanopowder; Pechini method. 1. Introduction. Zirconia has attracted the attention of many scientists because of its tremendous thermal, mechanical ...

  2. The Phoenix series large scale LNG pool fire experiments.

    Energy Technology Data Exchange (ETDEWEB)

    Simpson, Richard B.; Jensen, Richard Pearson; Demosthenous, Byron; Luketa, Anay Josephine; Ricks, Allen Joseph; Hightower, Marion Michael; Blanchat, Thomas K.; Helmick, Paul H.; Tieszen, Sheldon Robert; Deola, Regina Anne; Mercier, Jeffrey Alan; Suo-Anttila, Jill Marie; Miller, Timothy J.

    2010-12-01

    The increasing demand for natural gas could increase the number and frequency of Liquefied Natural Gas (LNG) tanker deliveries to ports across the United States. Because of the increasing number of shipments and the number of possible new facilities, concerns about the potential safety of the public and property from an accidental, and even more importantly intentional spills, have increased. While improvements have been made over the past decade in assessing hazards from LNG spills, the existing experimental data is much smaller in size and scale than many postulated large accidental and intentional spills. Since the physics and hazards from a fire change with fire size, there are concerns about the adequacy of current hazard prediction techniques for large LNG spills and fires. To address these concerns, Congress funded the Department of Energy (DOE) in 2008 to conduct a series of laboratory and large-scale LNG pool fire experiments at Sandia National Laboratories (Sandia) in Albuquerque, New Mexico. This report presents the test data and results of both sets of fire experiments. A series of five reduced-scale (gas burner) tests (yielding 27 sets of data) were conducted in 2007 and 2008 at Sandia's Thermal Test Complex (TTC) to assess flame height to fire diameter ratios as a function of nondimensional heat release rates for extrapolation to large-scale LNG fires. The large-scale LNG pool fire experiments were conducted in a 120 m diameter pond specially designed and constructed in Sandia's Area III large-scale test complex. Two fire tests of LNG spills of 21 and 81 m in diameter were conducted in 2009 to improve the understanding of flame height, smoke production, and burn rate and therefore the physics and hazards of large LNG spills and fires.

  3. Improving predictions of large scale soil carbon dynamics: Integration of fine-scale hydrological and biogeochemical processes, scaling, and benchmarking

    Science.gov (United States)

    Riley, W. J.; Dwivedi, D.; Ghimire, B.; Hoffman, F. M.; Pau, G. S. H.; Randerson, J. T.; Shen, C.; Tang, J.; Zhu, Q.

    2015-12-01

    Numerical model representations of decadal- to centennial-scale soil-carbon dynamics are a dominant cause of uncertainty in climate change predictions. Recent attempts by some Earth System Model (ESM) teams to integrate previously unrepresented soil processes (e.g., explicit microbial processes, abiotic interactions with mineral surfaces, vertical transport), poor performance of many ESM land models against large-scale and experimental manipulation observations, and complexities associated with spatial heterogeneity highlight the nascent nature of our community's ability to accurately predict future soil carbon dynamics. I will present recent work from our group to develop a modeling framework to integrate pore-, column-, watershed-, and global-scale soil process representations into an ESM (ACME), and apply the International Land Model Benchmarking (ILAMB) package for evaluation. At the column scale and across a wide range of sites, observed depth-resolved carbon stocks and their 14C derived turnover times can be explained by a model with explicit representation of two microbial populations, a simple representation of mineralogy, and vertical transport. Integrating soil and plant dynamics requires a 'process-scaling' approach, since all aspects of the multi-nutrient system cannot be explicitly resolved at ESM scales. I will show that one approach, the Equilibrium Chemistry Approximation, improves predictions of forest nitrogen and phosphorus experimental manipulations and leads to very different global soil carbon predictions. Translating model representations from the site- to ESM-scale requires a spatial scaling approach that either explicitly resolves the relevant processes, or more practically, accounts for fine-resolution dynamics at coarser scales. To that end, I will present recent watershed-scale modeling work that applies reduced order model methods to accurately scale fine-resolution soil carbon dynamics to coarse-resolution simulations. Finally, we

  4. Neural responses to threat and reward interact to predict stress-related problem drinking: A novel protective role of the amygdala

    Science.gov (United States)

    2012-01-01

    Background Research into neural mechanisms of drug abuse risk has focused on the role of dysfunction in neural circuits for reward. In contrast, few studies have examined the role of dysfunction in neural circuits of threat in mediating drug abuse risk. Although typically regarded as a risk factor for mood and anxiety disorders, threat-related amygdala reactivity may serve as a protective factor against substance use disorders, particularly in individuals with exaggerated responsiveness to reward. Findings We used well-established neuroimaging paradigms to probe threat-related amygdala and reward-related ventral striatum reactivity in a sample of 200 young adult students from the ongoing Duke Neurogenetics Study. Recent life stress and problem drinking were assessed using self-report. We found a significant three-way interaction between threat-related amygdala reactivity, reward-related ventral striatum reactivity, and recent stress, wherein individuals with higher reward-related ventral striatum reactivity exhibit higher levels of problem drinking in the context of stress, but only if they also have lower threat-related amygdala reactivity. This three-way interaction predicted both contemporaneous problem drinking and problem drinking reported three-months later in a subset of participants. Conclusions These findings suggest complex interactions between stress and neural responsiveness to both threat and reward mediate problem drinking. Furthermore, they highlight a novel protective role for threat-related amygdala reactivity against drug use in individuals with high neural reactivity to reward. PMID:23151390

  5. Computational chaos in massively parallel neural networks

    Science.gov (United States)

    Barhen, Jacob; Gulati, Sandeep

    1989-01-01

    A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.

  6. Large-scale Agricultural Land Acquisitions in West Africa | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    This project will examine large-scale agricultural land acquisitions in nine West African countries -Burkina Faso, Guinea-Bissau, Guinea, Benin, Mali, Togo, Senegal, Niger, and Côte d'Ivoire. ... They will use the results to increase public awareness and knowledge about the consequences of large-scale land acquisitions.

  7. Neural basis of attachment-caregiving systems interaction:insights from neuroimaging

    Directory of Open Access Journals (Sweden)

    Delia eLenzi

    2015-08-01

    Full Text Available The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the attachment-caregiving social bond (Bowlby, 1969. We then follow with those neuroimaging studies that have focused on this particular issue, i.e. those which have studied the activation of the careging system in women (using infant stimuli and have explored how the individual attachment model (through the Adult Attachment Interview modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction.

  8. Dynamical networks: Finding, measuring, and tracking neural population activity using network science

    Directory of Open Access Journals (Sweden)

    Mark D. Humphries

    2017-12-01

    Full Text Available Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

  9. Large-scale motions in the universe: a review

    International Nuclear Information System (INIS)

    Burstein, D.

    1990-01-01

    The expansion of the universe can be retarded in localised regions within the universe both by the presence of gravity and by non-gravitational motions generated in the post-recombination universe. The motions of galaxies thus generated are called 'peculiar motions', and the amplitudes, size scales and coherence of these peculiar motions are among the most direct records of the structure of the universe. As such, measurements of these properties of the present-day universe provide some of the severest tests of cosmological theories. This is a review of the current evidence for large-scale motions of galaxies out to a distance of ∼5000 km s -1 (in an expanding universe, distance is proportional to radial velocity). 'Large-scale' in this context refers to motions that are correlated over size scales larger than the typical sizes of groups of galaxies, up to and including the size of the volume surveyed. To orient the reader into this relatively new field of study, a short modern history is given together with an explanation of the terminology. Careful consideration is given to the data used to measure the distances, and hence the peculiar motions, of galaxies. The evidence for large-scale motions is presented in a graphical fashion, using only the most reliable data for galaxies spanning a wide range in optical properties and over the complete range of galactic environments. The kinds of systematic errors that can affect this analysis are discussed, and the reliability of these motions is assessed. The predictions of two models of large-scale motion are compared to the observations, and special emphasis is placed on those motions in which our own Galaxy directly partakes. (author)

  10. Population-wide distributions of neural activity during perceptual decision-making

    Science.gov (United States)

    Machens, Christian

    2018-01-01

    Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding. PMID:23123501

  11. Including investment risk in large-scale power market models

    DEFF Research Database (Denmark)

    Lemming, Jørgen Kjærgaard; Meibom, P.

    2003-01-01

    Long-term energy market models can be used to examine investments in production technologies, however, with market liberalisation it is crucial that such models include investment risks and investor behaviour. This paper analyses how the effect of investment risk on production technology selection...... can be included in large-scale partial equilibrium models of the power market. The analyses are divided into a part about risk measures appropriate for power market investors and a more technical part about the combination of a risk-adjustment model and a partial-equilibrium model. To illustrate...... the analyses quantitatively, a framework based on an iterative interaction between the equilibrium model and a separate risk-adjustment module was constructed. To illustrate the features of the proposed modelling approach we examined how uncertainty in demand and variable costs affects the optimal choice...

  12. Large scale oil lease automation and electronic custody transfer

    International Nuclear Information System (INIS)

    Price, C.R.; Elmer, D.C.

    1995-01-01

    Typically, oil field production operations have only been automated at fields with long term production profiles and enhanced recovery. The automation generally consists of monitoring and control at the wellhead and centralized facilities. However, Union Pacific Resources Co. (UPRC) has successfully implemented a large scale automation program for rapid-decline primary recovery Austin Chalk wells where purchasers buy and transport oil from each individual wellsite. This project has resulted in two significant benefits. First, operators are using the system to re-engineer their work processes. Second, an inter-company team created a new electronic custody transfer method. This paper will describe: the progression of the company's automation objectives in the area; the field operator's interaction with the system, and the related benefits; the research and development of the new electronic custody transfer method

  13. Interactions between large-scale modes of climate and their relationship with Australian climate and hydrology

    Science.gov (United States)

    Whan, K. R.; Lindesay, J. A.; Timbal, B.; Raupach, M. R.; Williams, E.

    2010-12-01

    Australia’s natural environment is adapted to low rainfall availability and high variability but human systems are less able to adapt to variability in the hydrological cycle. Understanding the mechanisms underlying drought persistence and severity is vital to contextualising future climate change. Multiple external forcings mean the mechanisms of drought occurrence in south-eastern Australian are complex. The key influences on SEA climate are El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the sub-tropical ridge (STR); each of these large-scale climate modes (LSCM) has been studied widely. The need for research into the interactions among the modes has been noted [1], although to date this has received limited attention. Relationships between LSCM and hydrometeorological variability are nonlinear, making linearity assumptions underlying usual statistical techniques (e.g. correlation, principle components analysis) questionable. In the current research a statistical technique that can deal with nonlinear interactions is applied to a new dataset enabling a full examination of the Australian water balance. The Australian Water Availability Project (AWAP) dataset models the Australian water balance on a fine grid [2]. Hydrological parameters (e.g. soil moisture, evaporation, runoff) are modelled from meteorological data, allowing the complete Australian water balance (climate and hydrology) to be examined and the mechanisms of drought to be studied holistically. Classification and regression trees (CART) are a powerful regression-based technique that is capable of accounting for nonlinear effects. Although it has limited previous application in climate research [3] this methodology is particularly informative in cases with multiple predictors and nonlinear relationships such as climate variability. Statistical relationships between variables are the basis for the decision rules in CART that are used to split

  14. Experimental study on dynamic behavior of large scale foundation, 1

    International Nuclear Information System (INIS)

    Hanada, Kazufumi; Sawada, Yoshihiro; Esashi, Yasuyuki; Ueshima, Teruyuki; Nakamura, Hideharu

    1983-01-01

    The large-sized, high performance vibrating table in the Nuclear Power Engineering Test Center is installed on a large-scale concrete foundation of length 90.9 m, width 44.8 m and maximum thickness 21 m, weighing 150,000 tons. Through the experimental study on the behavior of the foundation, which is set on gravel ground, useful information should be obtained on the siting of a nuclear power plant on the Quaternary stratum ground. The objective of research is to grasp the vibration characteristics of the foundation during the vibration of the table to evaluate the interaction between the foundation and the ground, and to evaluate an analytical method for numerically simulating the vibration behavior. In the present study, the vibration behavior of the foundation was clarified by measurement, and in order to predict the vibration behavior, the semi-infinite theory of elasticity was applied. The accuracy of this analytical method was demonstrated by comparison with the measured results. (Mori, K.)

  15. A route to explosive large-scale magnetic reconnection in a super-ion-scale current sheet

    Directory of Open Access Journals (Sweden)

    K. G. Tanaka

    2009-01-01

    Full Text Available How to trigger magnetic reconnection is one of the most interesting and important problems in space plasma physics. Recently, electron temperature anisotropy (αeo=Te⊥/Te|| at the center of a current sheet and non-local effect of the lower-hybrid drift instability (LHDI that develops at the current sheet edges have attracted attention in this context. In addition to these effects, here we also study the effects of ion temperature anisotropy (αio=Ti⊥/Ti||. Electron anisotropy effects are known to be helpless in a current sheet whose thickness is of ion-scale. In this range of current sheet thickness, the LHDI effects are shown to weaken substantially with a small increase in thickness and the obtained saturation level is too low for a large-scale reconnection to be achieved. Then we investigate whether introduction of electron and ion temperature anisotropies in the initial stage would couple with the LHDI effects to revive quick triggering of large-scale reconnection in a super-ion-scale current sheet. The results are as follows. (1 The initial electron temperature anisotropy is consumed very quickly when a number of minuscule magnetic islands (each lateral length is 1.5~3 times the ion inertial length form. These minuscule islands do not coalesce into a large-scale island to enable large-scale reconnection. (2 The subsequent LHDI effects disturb the current sheet filled with the small islands. This makes the triggering time scale to be accelerated substantially but does not enhance the saturation level of reconnected flux. (3 When the ion temperature anisotropy is added, it survives through the small island formation stage and makes even quicker triggering to happen when the LHDI effects set-in. Furthermore the saturation level is seen to be elevated by a factor of ~2 and large-scale reconnection is achieved only in this case. Comparison with two-dimensional simulations that exclude the LHDI effects confirms that the saturation level

  16. Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

    Science.gov (United States)

    Maskey, M.; Ramachandran, R.; Miller, J.

    2017-12-01

    Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

  17. Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.

    Science.gov (United States)

    Hutt, Axel; Buhry, Laure

    2014-12-01

    Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.

  18. Modeling and experiments of biomass combustion in a large-scale grate boiler

    DEFF Research Database (Denmark)

    Yin, Chungen; Rosendahl, Lasse; Kær, Søren Knudsen

    2007-01-01

    is inherently more difficult due to the complexity of the solid biomass fuel bed on the grate, the turbulent reacting flow in the combustion chamber and the intensive interaction between them. This paper presents the CFD validation efforts for a modern large-scale biomass-fired grate boiler. Modeling...... and experiments are both done for the grate boiler. The comparison between them shows an overall acceptable agreement in tendency. However at some measuring ports, big discrepancies between the modeling and the experiments are observed, mainly because the modeling-based boundary conditions (BCs) could differ...

  19. Energy Decomposition Analysis Based on Absolutely Localized Molecular Orbitals for Large-Scale Density Functional Theory Calculations in Drug Design.

    Science.gov (United States)

    Phipps, M J S; Fox, T; Tautermann, C S; Skylaris, C-K

    2016-07-12

    We report the development and implementation of an energy decomposition analysis (EDA) scheme in the ONETEP linear-scaling electronic structure package. Our approach is hybrid as it combines the localized molecular orbital EDA (Su, P.; Li, H. J. Chem. Phys., 2009, 131, 014102) and the absolutely localized molecular orbital EDA (Khaliullin, R. Z.; et al. J. Phys. Chem. A, 2007, 111, 8753-8765) to partition the intermolecular interaction energy into chemically distinct components (electrostatic, exchange, correlation, Pauli repulsion, polarization, and charge transfer). Limitations shared in EDA approaches such as the issue of basis set dependence in polarization and charge transfer are discussed, and a remedy to this problem is proposed that exploits the strictly localized property of the ONETEP orbitals. Our method is validated on a range of complexes with interactions relevant to drug design. We demonstrate the capabilities for large-scale calculations with our approach on complexes of thrombin with an inhibitor comprised of up to 4975 atoms. Given the capability of ONETEP for large-scale calculations, such as on entire proteins, we expect that our EDA scheme can be applied in a large range of biomolecular problems, especially in the context of drug design.

  20. Large Display Interaction Using Mobile Devices

    OpenAIRE

    Bauer, Jens

    2015-01-01

    Large displays become more and more popular, due to dropping prices. Their size and high resolution leverages collaboration and they are capable of dis- playing even large datasets in one view. This becomes even more interesting as the number of big data applications increases. The increased screen size and other properties of large displays pose new challenges to the Human- Computer-Interaction with these screens. This includes issues such as limited scalability to the number of users, diver...

  1. Large-scale structure observables in general relativity

    International Nuclear Information System (INIS)

    Jeong, Donghui; Schmidt, Fabian

    2015-01-01

    We review recent studies that rigorously define several key observables of the large-scale structure of the Universe in a general relativistic context. Specifically, we consider (i) redshift perturbation of cosmic clock events; (ii) distortion of cosmic rulers, including weak lensing shear and magnification; and (iii) observed number density of tracers of the large-scale structure. We provide covariant and gauge-invariant expressions of these observables. Our expressions are given for a linearly perturbed flat Friedmann–Robertson–Walker metric including scalar, vector, and tensor metric perturbations. While we restrict ourselves to linear order in perturbation theory, the approach can be straightforwardly generalized to higher order. (paper)

  2. Fatigue Analysis of Large-scale Wind turbine

    Directory of Open Access Journals (Sweden)

    Zhu Yongli

    2017-01-01

    Full Text Available The paper does research on top flange fatigue damage of large-scale wind turbine generator. It establishes finite element model of top flange connection system with finite element analysis software MSC. Marc/Mentat, analyzes its fatigue strain, implements load simulation of flange fatigue working condition with Bladed software, acquires flange fatigue load spectrum with rain-flow counting method, finally, it realizes fatigue analysis of top flange with fatigue analysis software MSC. Fatigue and Palmgren-Miner linear cumulative damage theory. The analysis result indicates that its result provides new thinking for flange fatigue analysis of large-scale wind turbine generator, and possesses some practical engineering value.

  3. Real-time simulation of large-scale floods

    Science.gov (United States)

    Liu, Q.; Qin, Y.; Li, G. D.; Liu, Z.; Cheng, D. J.; Zhao, Y. H.

    2016-08-01

    According to the complex real-time water situation, the real-time simulation of large-scale floods is very important for flood prevention practice. Model robustness and running efficiency are two critical factors in successful real-time flood simulation. This paper proposed a robust, two-dimensional, shallow water model based on the unstructured Godunov- type finite volume method. A robust wet/dry front method is used to enhance the numerical stability. An adaptive method is proposed to improve the running efficiency. The proposed model is used for large-scale flood simulation on real topography. Results compared to those of MIKE21 show the strong performance of the proposed model.

  4. Toward multi-area distributed network of implanted neural interrogators

    Science.gov (United States)

    Powell, Marc P.; Hou, Xiaoxiao; Galligan, Craig; Ashe, Jeffrey; Borton, David A.

    2017-08-01

    As we aim to improve our understanding of the brain, it is critical that researchers have simultaneous multi-area, large-scale access to the brain. Information processing in the brain occurs through close and distant coupling of functional sub-domains, as opposed to within isolated single neurons. However, commercially available neural interfaces capable of sensing electrophysiology of single neurons, currently allow access to only a small, mm3 volume of cortical cells, are not scalable to recording from orders of magnitude more neurons, and leverage bulky, skull mounted hardware and cabling sensitive to relative movements of the skull and brain. In this work, we propose a system capable of recording from many individual distributed neural interrogator nodes, untethered from any external electronics. Using an array of epidural inductive coils to wirelessly power the implanted electronics, the system is intended to be agnostic to the surgical placement of any individual node. Here, we demonstrate the ability to transmit nearly 15mW of power with greater than 50% power transfer efficiency, benchtop testing of individual subcircuit system components showing successful digitization of neural signals, and wireless transmission currently supporting a data rate of 3.84Mbps. We leverage a software defined radio based RF receiver to demodulate the data which can be stored in memory for later retrieval. Finally, we introduce a packaging technology capable of isolating active electronics from the surrounding tissue while providing capability for electrical feed-through assemblies for external neural interfacing. We expect, based on the presented preliminary findings, that the system can be integrated into a platform technology for the study of the intricate interactions between cortical domains.

  5. Large scale validation of the M5L lung CAD on heterogeneous CT datasets

    Energy Technology Data Exchange (ETDEWEB)

    Lopez Torres, E., E-mail: Ernesto.Lopez.Torres@cern.ch, E-mail: cerello@to.infn.it [CEADEN, Havana 11300, Cuba and INFN, Sezione di Torino, Torino 10125 (Italy); Fiorina, E.; Pennazio, F.; Peroni, C. [Department of Physics, University of Torino, Torino 10125, Italy and INFN, Sezione di Torino, Torino 10125 (Italy); Saletta, M.; Cerello, P., E-mail: Ernesto.Lopez.Torres@cern.ch, E-mail: cerello@to.infn.it [INFN, Sezione di Torino, Torino 10125 (Italy); Camarlinghi, N.; Fantacci, M. E. [Department of Physics, University of Pisa, Pisa 56127, Italy and INFN, Sezione di Pisa, Pisa 56127 (Italy)

    2015-04-15

    Purpose: M5L, a fully automated computer-aided detection (CAD) system for the detection and segmentation of lung nodules in thoracic computed tomography (CT), is presented and validated on several image datasets. Methods: M5L is the combination of two independent subsystems, based on the Channeler Ant Model as a segmentation tool [lung channeler ant model (lungCAM)] and on the voxel-based neural approach. The lungCAM was upgraded with a scan equalization module and a new procedure to recover the nodules connected to other lung structures; its classification module, which makes use of a feed-forward neural network, is based of a small number of features (13), so as to minimize the risk of lacking generalization, which could be possible given the large difference between the size of the training and testing datasets, which contain 94 and 1019 CTs, respectively. The lungCAM (standalone) and M5L (combined) performance was extensively tested on 1043 CT scans from three independent datasets, including a detailed analysis of the full Lung Image Database Consortium/Image Database Resource Initiative database, which is not yet found in literature. Results: The lungCAM and M5L performance is consistent across the databases, with a sensitivity of about 70% and 80%, respectively, at eight false positive findings per scan, despite the variable annotation criteria and acquisition and reconstruction conditions. A reduced sensitivity is found for subtle nodules and ground glass opacities (GGO) structures. A comparison with other CAD systems is also presented. Conclusions: The M5L performance on a large and heterogeneous dataset is stable and satisfactory, although the development of a dedicated module for GGOs detection could further improve it, as well as an iterative optimization of the training procedure. The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large

  6. Network dynamics with BrainX(3): a large-scale simulation of the human brain network with real-time interaction.

    Science.gov (United States)

    Arsiwalla, Xerxes D; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F M J

    2015-01-01

    BrainX(3) is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX(3) in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX(3) can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

  7. Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

    Science.gov (United States)

    Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas. PMID:25759649

  8. Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

    Directory of Open Access Journals (Sweden)

    Xerxes D. Arsiwalla

    2015-02-01

    Full Text Available BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for real-time exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably, due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

  9. Interaction of ion clusters with fusion plasmas: Scaling laws

    International Nuclear Information System (INIS)

    Arista, N.R.; Bringa, E.M.

    1997-01-01

    The interaction between large ion clusters or very intense ion beams with fusion plasma is studied using the dielectric function formalism with appropriate quantum corrections. The contributions from individual and collective modes to the energy loss are calculated. The general properties of the interference effects are characterized in terms of the relevant parameters, and simple scaling laws are obtained. In particular, the conditions for a maximum enhancement in the energy deposition are derived. The study provides a unified view and a general formulation of collective effects in the energy loss for low and high velocities of the beam particles. copyright 1997 The American Physical Society

  10. Large-scale numerical simulations of plasmas

    International Nuclear Information System (INIS)

    Hamaguchi, Satoshi

    2004-01-01

    The recent trend of large scales simulations of fusion plasma and processing plasmas is briefly summarized. Many advanced simulation techniques have been developed for fusion plasmas and some of these techniques are now applied to analyses of processing plasmas. (author)

  11. Nearly incompressible fluids: Hydrodynamics and large scale inhomogeneity

    International Nuclear Information System (INIS)

    Hunana, P.; Zank, G. P.; Shaikh, D.

    2006-01-01

    A system of hydrodynamic equations in the presence of large-scale inhomogeneities for a high plasma beta solar wind is derived. The theory is derived under the assumption of low turbulent Mach number and is developed for the flows where the usual incompressible description is not satisfactory and a full compressible treatment is too complex for any analytical studies. When the effects of compressibility are incorporated only weakly, a new description, referred to as 'nearly incompressible hydrodynamics', is obtained. The nearly incompressible theory, was originally applied to homogeneous flows. However, large-scale gradients in density, pressure, temperature, etc., are typical in the solar wind and it was unclear how inhomogeneities would affect the usual incompressible and nearly incompressible descriptions. In the homogeneous case, the lowest order expansion of the fully compressible equations leads to the usual incompressible equations, followed at higher orders by the nearly incompressible equations, as introduced by Zank and Matthaeus. With this work we show that the inclusion of large-scale inhomogeneities (in this case time-independent and radially symmetric background solar wind) modifies the leading-order incompressible description of solar wind flow. We find, for example, that the divergence of velocity fluctuations is nonsolenoidal and that density fluctuations can be described to leading order as a passive scalar. Locally (for small lengthscales), this system of equations converges to the usual incompressible equations and we therefore use the term 'locally incompressible' to describe the equations. This term should be distinguished from the term 'nearly incompressible', which is reserved for higher-order corrections. Furthermore, we find that density fluctuations scale with Mach number linearly, in contrast to the original homogeneous nearly incompressible theory, in which density fluctuations scale with the square of Mach number. Inhomogeneous nearly

  12. The cavitation erosion of ultrasonic sonotrode during large-scale metallic casting: Experiment and simulation.

    Science.gov (United States)

    Tian, Yang; Liu, Zhilin; Li, Xiaoqian; Zhang, Lihua; Li, Ruiqing; Jiang, Ripeng; Dong, Fang

    2018-05-01

    Ultrasonic sonotrodes play an essential role in transmitting power ultrasound into the large-scale metallic casting. However, cavitation erosion considerably impairs the in-service performance of ultrasonic sonotrodes, leading to marginal microstructural refinement. In this work, the cavitation erosion behaviour of ultrasonic sonotrodes in large-scale castings was explored using the industry-level experiments of Al alloy cylindrical ingots (i.e. 630 mm in diameter and 6000 mm in length). When introducing power ultrasound, severe cavitation erosion was found to reproducibly occur at some specific positions on ultrasonic sonotrodes. However, there is no cavitation erosion present on the ultrasonic sonotrodes that were not driven by electric generator. Vibratory examination showed cavitation erosion depended on the vibration state of ultrasonic sonotrodes. Moreover, a finite element (FE) model was developed to simulate the evolution and distribution of acoustic pressure in 3-D solidification volume. FE simulation results confirmed that significant dynamic interaction between sonotrodes and melts only happened at some specific positions corresponding to severe cavitation erosion. This work will allow for developing more advanced ultrasonic sonotrodes with better cavitation erosion-resistance, in particular for large-scale castings, from the perspectives of ultrasonic physics and mechanical design. Copyright © 2018 Elsevier B.V. All rights reserved.

  13. Performance Health Monitoring of Large-Scale Systems

    Energy Technology Data Exchange (ETDEWEB)

    Rajamony, Ram [IBM Research, Austin, TX (United States)

    2014-11-20

    This report details the progress made on the ASCR funded project Performance Health Monitoring for Large Scale Systems. A large-­scale application may not achieve its full performance potential due to degraded performance of even a single subsystem. Detecting performance faults, isolating them, and taking remedial action is critical for the scale of systems on the horizon. PHM aims to develop techniques and tools that can be used to identify and mitigate such performance problems. We accomplish this through two main aspects. The PHM framework encompasses diagnostics, system monitoring, fault isolation, and performance evaluation capabilities that indicates when a performance fault has been detected, either due to an anomaly present in the system itself or due to contention for shared resources between concurrently executing jobs. Software components called the PHM Control system then build upon the capabilities provided by the PHM framework to mitigate degradation caused by performance problems.

  14. Computational Cosmology: from the Early Universe to the Large Scale Structure

    Directory of Open Access Journals (Sweden)

    Peter Anninos

    1998-09-01

    Full Text Available In order to account for the observable Universe, any comprehensive theory or model of cosmology must draw from many disciplines of physics, including gauge theories of strong and weak interactions, the hydrodynamics and microphysics of baryonic matter, electromagnetic fields, and spacetime curvature, for example. Although it is difficult to incorporate all these physical elements into a single complete model of our Universe, advances in computing methods and technologies have contributed significantly towards our understanding of cosmological models, the Universe, and astrophysical processes within them. A sample of numerical calculations addressing specific issues in cosmology are reviewed in this article: from the Big Bang singularity dynamics to the fundamental interactions of gravitational waves; from the quark--hadron phase transition to the large scale structure of the Universe. The emphasis, although not exclusively, is on thosecalculations designed to test different models of cosmology against the observed Universe.

  15. Computational Cosmology: From the Early Universe to the Large Scale Structure.

    Science.gov (United States)

    Anninos, Peter

    2001-01-01

    In order to account for the observable Universe, any comprehensive theory or model of cosmology must draw from many disciplines of physics, including gauge theories of strong and weak interactions, the hydrodynamics and microphysics of baryonic matter, electromagnetic fields, and spacetime curvature, for example. Although it is difficult to incorporate all these physical elements into a single complete model of our Universe, advances in computing methods and technologies have contributed significantly towards our understanding of cosmological models, the Universe, and astrophysical processes within them. A sample of numerical calculations (and numerical methods applied to specific issues in cosmology are reviewed in this article: from the Big Bang singularity dynamics to the fundamental interactions of gravitational waves; from the quark-hadron phase transition to the large scale structure of the Universe. The emphasis, although not exclusively, is on those calculations designed to test different models of cosmology against the observed Universe.

  16. Complete Neuron-Astrocyte Interaction Model: Digital Multiplierless Design and Networking Mechanism.

    Science.gov (United States)

    Haghiri, Saeed; Ahmadi, Arash; Saif, Mehrdad

    2017-02-01

    Glial cells, also known as neuroglia or glia, are non-neuronal cells providing support and protection for neurons in the central nervous system (CNS). They also act as supportive cells in the brain. Among a variety of glial cells, the star-shaped glial cells, i.e., astrocytes, are the largest cell population in the brain. The important role of astrocyte such as neuronal synchronization, synaptic information regulation, feedback to neural activity and extracellular regulation make the astrocytes play a vital role in brain disease. This paper presents a modified complete neuron-astrocyte interaction model that is more suitable for efficient and large scale biological neural network realization on digital platforms. Simulation results show that the modified complete interaction model can reproduce biological-like behavior of the original neuron-astrocyte mechanism. The modified interaction model is investigated in terms of digital realization feasibility and cost targeting a low cost hardware implementation. Networking behavior of this interaction is investigated and compared between two cases: i) the neuron spiking mechanism without astrocyte effects, and ii) the effect of astrocyte in regulating the neurons behavior and synaptic transmission via controlling the LTP and LTD processes. Hardware implementation on FPGA shows that the modified model mimics the main mechanism of neuron-astrocyte communication with higher performance and considerably lower hardware overhead cost compared with the original interaction model.

  17. Feedforward inhibition and synaptic scaling--two sides of the same coin?

    Science.gov (United States)

    Keck, Christian; Savin, Cristina; Lücke, Jörg

    2012-01-01

    Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

  18. Feedforward inhibition and synaptic scaling--two sides of the same coin?

    Directory of Open Access Journals (Sweden)

    Christian Keck

    Full Text Available Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

  19. Quantification of Linkages between Large-Scale Climate Patterns and Annual Precipitation for the Colorado River Basin

    Science.gov (United States)

    Kalra, A.; Ahmad, S.

    2010-12-01

    Precipitation is regarded as one of the key variables driving various hydrologic processes and the future precipitation information can be useful to better understand the long-term climate dynamics. In this paper, a simple, robust, and parsimonious precipitation forecast model, Support Vector Machine (SVM) is proposed which uses large-scale climate information and predict annual precipitation 1-year in advance. SVM’s are a novel class of neural networks (NNs) which are based on the statistical learning theory. The SVM’s has three main advantages over the traditional NNs: 1) better generalization ability, 2) the architecture and weights of SVM’s are guaranteed to be unique and globally optimum, and 3) SVM’s are trained more rapidly than the corresponding NN. With these advantages, an application of SVM incorporating large-scale climate information is developed and applied to seventeen climate divisions encompassing the Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1900-2007 are used to generate annual precipitation estimates with 1-year lead time. The results from the present study indicate that long-term precipitation predictions for the Upper Colorado River Basin can be successfully obtained using a combination of NAO and ENSO indices whereas coupling PDO and AMO results in improved precipitation predictions for the Lower Colorado River Basin. Precipitation predictions from the SVM model are found to be better when compared with the predictions obtained from feed-forward back propagation Artificial Neural Network and Multivariate Linear Regression models. The overall results of this study revealed that the annual precipitation of the Colorado River Basin was significantly influenced by oceanic-atmospheric oscillations and the proposed SVM

  20. Visual coherence for large-scale line-plot visualizations

    KAUST Repository

    Muigg, Philipp

    2011-06-01

    Displaying a large number of lines within a limited amount of screen space is a task that is common to many different classes of visualization techniques such as time-series visualizations, parallel coordinates, link-node diagrams, and phase-space diagrams. This paper addresses the challenging problems of cluttering and overdraw inherent to such visualizations. We generate a 2x2 tensor field during line rasterization that encodes the distribution of line orientations through each image pixel. Anisotropic diffusion of a noise texture is then used to generate a dense, coherent visualization of line orientation. In order to represent features of different scales, we employ a multi-resolution representation of the tensor field. The resulting technique can easily be applied to a wide variety of line-based visualizations. We demonstrate this for parallel coordinates, a time-series visualization, and a phase-space diagram. Furthermore, we demonstrate how to integrate a focus+context approach by incorporating a second tensor field. Our approach achieves interactive rendering performance for large data sets containing millions of data items, due to its image-based nature and ease of implementation on GPUs. Simulation results from computational fluid dynamics are used to evaluate the performance and usefulness of the proposed method. © 2011 The Author(s).