WorldWideScience

Sample records for hierarchical task network

  1. Hierarchical organization of brain functional networks during visual tasks.

    Science.gov (United States)

    Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

    2011-09-01

    The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

  2. Hierarchical Network Design

    DEFF Research Database (Denmark)

    Thomadsen, Tommy

    2005-01-01

    Communication networks are immensely important today, since both companies and individuals use numerous services that rely on them. This thesis considers the design of hierarchical (communication) networks. Hierarchical networks consist of layers of networks and are well-suited for coping...... with changing and increasing demands. Two-layer networks consist of one backbone network, which interconnects cluster networks. The clusters consist of nodes and links, which connect the nodes. One node in each cluster is a hub node, and the backbone interconnects the hub nodes of each cluster and thus...... the clusters. The design of hierarchical networks involves clustering of nodes, hub selection, and network design, i.e. selection of links and routing of ows. Hierarchical networks have been in use for decades, but integrated design of these networks has only been considered for very special types of networks...

  3. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2012-01-01

    Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose...... a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  4. APHiD: Hierarchical Task Placement to Enable a Tapered Fat Tree Topology for Lower Power and Cost in HPC Networks

    Energy Technology Data Exchange (ETDEWEB)

    Michelogiannakis, George; Ibrahim, Khaled Z.; Shalf, John; Wilke, Jeremiah J.; Knight, Samuel; Kenny, Joseph P.

    2017-05-14

    The power and procurement cost of bandwidth in system-wide networks has forced a steady drop in the byte/flop ratio. This trend of computation becoming faster relative to the network is expected to hold. In this paper, we explore how cost-oriented task placement enables reducing the cost of system-wide networks by enabling high performance even on tapered topologies where more bandwidth is provisioned at lower levels. We describe APHiD, an efficient hierarchical placement algorithm that uses new techniques to improve the quality of heuristic solutions and reduces the demand on high-level, expensive bandwidth in hierarchical topologies. We apply APHiD to a tapered fat-tree, demonstrating that APHiD maintains application scalability even for severely tapered network configurations. Using simulation, we show that for tapered networks APHiD improves performance by more than 50% over random placement and even 15% in some cases over costlier, state-of-the-art placement algorithms.

  5. Modular networks with hierarchical organization

    Indian Academy of Sciences (India)

    Several networks occurring in real life have modular structures that are arranged in a hierarchical fashion. In this paper, we have proposed a model for such networks, using a stochastic generation method. Using this model we show that, the scaling relation between the clustering and degree of the nodes is not a necessary ...

  6. Hierarchical modularity in human brain functional networks

    Directory of Open Access Journals (Sweden)

    David Meunier

    2009-10-01

    Full Text Available The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules” decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.

  7. Loops in hierarchical channel networks

    Science.gov (United States)

    Katifori, Eleni; Magnasco, Marcelo

    2012-02-01

    Nature provides us with many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture. Although a number of methods have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated and natural graphs extracted from digitized images of dicotyledonous leaves and animal vasculature. We calculate various metrics on the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

  8. Stability of glassy hierarchical networks

    Science.gov (United States)

    Zamani, M.; Camargo-Forero, L.; Vicsek, T.

    2018-02-01

    The structure of interactions in most animal and human societies can be best represented by complex hierarchical networks. In order to maintain close-to-optimal function both stability and adaptability are necessary. Here we investigate the stability of hierarchical networks that emerge from the simulations of an organization type with an efficiency function reminiscent of the Hamiltonian of spin glasses. Using this quantitative approach we find a number of expected (from everyday observations) and highly non-trivial results for the obtained locally optimal networks, including, for example: (i) stability increases with growing efficiency and level of hierarchy; (ii) the same perturbation results in a larger change for more efficient states; (iii) networks with a lower level of hierarchy become more efficient after perturbation; (iv) due to the huge number of possible optimal states only a small fraction of them exhibit resilience and, finally, (v) ‘attacks’ targeting the nodes selectively (regarding their position in the hierarchy) can result in paradoxical outcomes.

  9. Biased trapping issue on weighted hierarchical networks

    Indian Academy of Sciences (India)

    archical networks which are based on the classic scale-free hierarchical networks. ... Weighted hierarchical networks; weight-dependent walks; mean first passage ..... The weighted networks can mimic some real-world natural and social systems to ... the Priority Academic Program Development of Jiangsu Higher Education ...

  10. Task Switching in a Hierarchical Task Structure: Evidence for the Fragility of the Task Repetition Benefit

    Science.gov (United States)

    Lien, Mei-Ching; Ruthruff, Eric

    2004-01-01

    This study examined how task switching is affected by hierarchical task organization. Traditional task-switching studies, which use a constant temporal and spatial distance between each task element (defined as a stimulus requiring a response), promote a flat task structure. Using this approach, Experiment 1 revealed a large switch cost of 238 ms.…

  11. The hierarchical brain network for face recognition.

    Science.gov (United States)

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  12. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  13. Analyzing security protocols in hierarchical networks

    DEFF Research Database (Denmark)

    Zhang, Ye; Nielson, Hanne Riis

    2006-01-01

    Validating security protocols is a well-known hard problem even in a simple setting of a single global network. But a real network often consists of, besides the public-accessed part, several sub-networks and thereby forms a hierarchical structure. In this paper we first present a process calculus...... capturing the characteristics of hierarchical networks and describe the behavior of protocols on such networks. We then develop a static analysis to automate the validation. Finally we demonstrate how the technique can benefit the protocol development and the design of network systems by presenting a series...

  14. Deep hierarchical attention network for video description

    Science.gov (United States)

    Li, Shuohao; Tang, Min; Zhang, Jun

    2018-03-01

    Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.

  15. Hierarchical spatial organization of geographical networks

    International Nuclear Information System (INIS)

    Travencolo, Bruno A N; Costa, Luciano da F

    2008-01-01

    In this work, we propose a hierarchical extension of the polygonality index as the means to characterize geographical planar networks. By considering successive neighborhoods around each node, it is possible to obtain more complete information about the spatial order of the network at progressive spatial scales. The potential of the methodology is illustrated with respect to synthetic and real geographical networks

  16. Mitigating Herding in Hierarchical Crowdsourcing Networks.

    Science.gov (United States)

    Yu, Han; Miao, Chunyan; Leung, Cyril; Chen, Yiqiang; Fauvel, Simon; Lesser, Victor R; Yang, Qiang

    2016-12-05

    Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers' current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.

  17. Ultrafast Hierarchical OTDM/WDM Network

    Directory of Open Access Journals (Sweden)

    Hideyuki Sotobayashi

    2003-12-01

    Full Text Available Ultrafast hierarchical OTDM/WDM network is proposed for the future core-network. We review its enabling technologies: C- and L-wavelength-band generation, OTDM-WDM mutual multiplexing format conversions, and ultrafast OTDM wavelengthband conversions.

  18. Noise enhances information transfer in hierarchical networks.

    Science.gov (United States)

    Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A

    2013-01-01

    We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

  19. Modular networks with hierarchical organization: The dynamical ...

    Indian Academy of Sciences (India)

    Most of the complex systems seen in real life also have associated dynamics [10], and the ... another example, this time a hierarchical structure, viz., the Cayley tree with b ..... natural constraints operating on networks in real life, such as the ...

  20. Hierarchical Network Design Using Simulated Annealing

    DEFF Research Database (Denmark)

    Thomadsen, Tommy; Clausen, Jens

    2002-01-01

    networks are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the network. The problem is solved heuristically using simulated annealing which as a sub......-algorithm uses a construction algorithm to determine edges and route the demand. Performance for different versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to find solutions of reasonable quality in approximately 1 hour for networks with 100 nodes....

  1. Object recognition with hierarchical discriminant saliency networks.

    Science.gov (United States)

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  2. Hierarchical regular small-world networks

    International Nuclear Information System (INIS)

    Boettcher, Stefan; Goncalves, Bruno; Guclu, Hasan

    2008-01-01

    Two new networks are introduced that resemble small-world properties. These networks are recursively constructed but retain a fixed, regular degree. They possess a unique one-dimensional lattice backbone overlaid by a hierarchical sequence of long-distance links, mixing real-space and small-world features. Both networks, one 3-regular and the other 4-regular, lead to distinct behaviors, as revealed by renormalization group studies. The 3-regular network is planar, has a diameter growing as √N with system size N, and leads to super-diffusion with an exact, anomalous exponent d w = 1.306..., but possesses only a trivial fixed point T c = 0 for the Ising ferromagnet. In turn, the 4-regular network is non-planar, has a diameter growing as ∼2 √(log 2 N 2 ) , exhibits 'ballistic' diffusion (d w = 1), and a non-trivial ferromagnetic transition, T c > 0. It suggests that the 3-regular network is still quite 'geometric', while the 4-regular network qualifies as a true small world with mean-field properties. As an engineering application we discuss synchronization of processors on these networks. (fast track communication)

  3. Epidemics and dimensionality in hierarchical networks

    Science.gov (United States)

    Zheng, Da-Fang; Hui, P. M.; Trimper, Steffen; Zheng, Bo

    2005-07-01

    Epidemiological processes are studied within a recently proposed hierarchical network model using the susceptible-infected-refractory dynamics of an epidemic. Within the network model, a population may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveal that for H>1, global spreading results regardless of the degree of homophily of the individuals forming a social circle. For H=1, a transition from global to local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large-scale outbreaks of infectious diseases (viruses).

  4. Modeling Network Interdiction Tasks

    Science.gov (United States)

    2015-09-17

    118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

  5. Hierarchical analysis of dependency in metabolic networks.

    Science.gov (United States)

    Gagneur, Julien; Jackson, David B; Casari, Georg

    2003-05-22

    Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html

  6. Organization of excitable dynamics in hierarchical biological networks.

    Directory of Open Access Journals (Sweden)

    Mark Müller-Linow

    Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

  7. A Hierarchical Approach to Real-time Activity Recognition in Body Sensor Networks

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Tao, Xianping

    2012-01-01

    Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we rst use a fast and lightweight al...

  8. Epidemic spreading in a hierarchical social network.

    Science.gov (United States)

    Grabowski, A; Kosiński, R A

    2004-09-01

    A model of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The structure of interpersonal connections is based on a scale-free network. Spatial localization of individuals belonging to different social groups, and the mobility of a contemporary community, as well as the effectiveness of different interpersonal interactions, are taken into account. Typical relations characterizing the spreading process, like a range of epidemic and epidemic curves, are discussed. The influence of preventive vaccinations on the spreading process is investigated. The critical value of preventively vaccinated individuals that is sufficient for the suppression of an epidemic is calculated. Our results are compared with solutions of the master equation for the spreading process and good agreement of the character of this process is found.

  9. A Hierarchical Dispatch Structure for Distribution Network Pricing

    OpenAIRE

    Yuan, Zhao; Hesamzadeh, Mohammad Reza

    2015-01-01

    This paper presents a hierarchical dispatch structure for efficient distribution network pricing. The dispatch coordination problem in the context of hierarchical network operators are addressed. We formulate decentralized generation dispatch into a bilevel optimization problem in which main network operator and the connected distribution network operator optimize their costs in two levels. By using Karush-Kuhn-Tucker conditions and Fortuny-Amat McCarl linearization, the bilevel optimization ...

  10. Category theoretic analysis of hierarchical protein materials and social networks.

    Directory of Open Access Journals (Sweden)

    David I Spivak

    Full Text Available Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.

  11. 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.

  12. Object recognition with hierarchical discriminant saliency networks

    Directory of Open Access Journals (Sweden)

    Sunhyoung eHan

    2014-09-01

    Full Text Available The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognitionmodel, the hierarchical discriminant saliency network (HDSN, whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. The HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a neuralnetwork implementation, all layers are convolutional and implement acombination of filtering, rectification, and pooling. The rectificationis performed with a parametric extension of the now popular rectified linearunits (ReLUs, whose parameters can be tuned for the detection of targetobject classes. This enables a number of functional enhancementsover neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation ofsaliency responses by the discriminant power of the underlying features,and the ability to detect both feature presence and absence.In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity totarget object classes and invariance. The resulting performance demonstrates benefits for all the functional enhancements of the HDSN.

  13. Multiple dynamical time-scales in networks with hierarchically

    Indian Academy of Sciences (India)

    Modular networks; hierarchical organization; synchronization. ... we show that such a topological structure gives rise to characteristic time-scale separation ... This suggests a possible functional role of such mesoscopic organization principle in ...

  14. Road network safety evaluation using Bayesian hierarchical joint model.

    Science.gov (United States)

    Wang, Jie; Huang, Helai

    2016-05-01

    Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Hierarchical Trust Management of COI in Heterogeneous Mobile Networks

    Science.gov (United States)

    2017-08-01

    Report: Hierarchical Trust Management of COI in Heterogeneous Mobile Networks The views, opinions and/or findings contained in this report are those of...Institute & State University Title: Hierarchical Trust Management of COI in Heterogeneous Mobile Networks Report Term: 0-Other Email: irchen@vt.edu...Reconfigurability, Survivability and Intrusion Tolerance for Community of Interest (COI) Applications – Our proposed COI trust management protocol will

  16. Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network

    OpenAIRE

    Rashed, Md. Golam; Kabir, M. Hasnat; Rahim, Muhammad Sajjadur; Ullah, Shaikh Enayet

    2012-01-01

    The efficient use of energy source in a sensor node is most desirable criteria for prolong the life time of wireless sensor network. In this paper, we propose a two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP). We introduce a new concept called head-set, consists of one active cluster head and some other associate cluster heads within a cluster. The head-set members are responsible for control and management of the network. Results show that t...

  17. Road Network Selection Based on Road Hierarchical Structure Control

    Directory of Open Access Journals (Sweden)

    HE Haiwei

    2015-04-01

    Full Text Available A new road network selection method based on hierarchical structure is studied. Firstly, road network is built as strokes which are then classified into hierarchical collections according to the criteria of betweenness centrality value (BC value. Secondly, the hierarchical structure of the strokes is enhanced using structural characteristic identification technique. Thirdly, the importance calculation model was established according to the relationships among the hierarchical structure of the strokes. Finally, the importance values of strokes are got supported with the model's hierarchical calculation, and with which the road network is selected. Tests are done to verify the advantage of this method by comparing it with other common stroke-oriented methods using three kinds of typical road network data. Comparision of the results show that this method had few need to semantic data, and could eliminate the negative influence of edge strokes caused by the criteria of BC value well. So, it is better to maintain the global hierarchical structure of road network, and suitable to meet with the selection of various kinds of road network at the same time.

  18. Applying Hierarchical Task Analysis Method to Discovery Layer Evaluation

    Directory of Open Access Journals (Sweden)

    Marlen Promann

    2015-03-01

    Full Text Available Libraries are implementing discovery layers to offer better user experiences. While usability tests have been helpful in evaluating the success or failure of implementing discovery layers in the library context, the focus has remained on its relative interface benefits over the traditional federated search. The informal site- and context specific usability tests have offered little to test the rigor of the discovery layers against the user goals, motivations and workflow they have been designed to support. This study proposes hierarchical task analysis (HTA as an important complementary evaluation method to usability testing of discovery layers. Relevant literature is reviewed for the discovery layers and the HTA method. As no previous application of HTA to the evaluation of discovery layers was found, this paper presents the application of HTA as an expert based and workflow centered (e.g. retrieving a relevant book or a journal article method to evaluating discovery layers. Purdue University’s Primo by Ex Libris was used to map eleven use cases as HTA charts. Nielsen’s Goal Composition theory was used as an analytical framework to evaluate the goal carts from two perspectives: a users’ physical interactions (i.e. clicks, and b user’s cognitive steps (i.e. decision points for what to do next. A brief comparison of HTA and usability test findings is offered as a way of conclusion.

  19. Hierarchical structure of moral stages assessed by a sorting task

    NARCIS (Netherlands)

    Boom, J.; Brugman, D.; Van der Heijden, P.G.M.

    2001-01-01

    Following criticism of Kohlberg’s theory of moral judgment, an empirical re-examination of hierarchical stage structure was desirable. Utilizing Piaget’s concept of reflective abstraction as a basis, the hierarchical stage structure was investigated using a new method. Study participants (553 Dutch

  20. Chimera states in networks of logistic maps with hierarchical connectivities

    Science.gov (United States)

    zur Bonsen, Alexander; Omelchenko, Iryna; Zakharova, Anna; Schöll, Eckehard

    2018-04-01

    Chimera states are complex spatiotemporal patterns consisting of coexisting domains of coherence and incoherence. We study networks of nonlocally coupled logistic maps and analyze systematically how the dilution of the network links influences the appearance of chimera patterns. The network connectivities are constructed using an iterative Cantor algorithm to generate fractal (hierarchical) connectivities. Increasing the hierarchical level of iteration, we compare the resulting spatiotemporal patterns. We demonstrate that a high clustering coefficient and symmetry of the base pattern promotes chimera states, and asymmetric connectivities result in complex nested chimera patterns.

  1. Detecting the overlapping and hierarchical community structure in complex networks

    International Nuclear Information System (INIS)

    Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

    2009-01-01

    Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

  2. A new hierarchical method to find community structure in networks

    Science.gov (United States)

    Saoud, Bilal; Moussaoui, Abdelouahab

    2018-04-01

    Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

  3. Hierarchically structured distributed microprocessor network for control

    International Nuclear Information System (INIS)

    Greenwood, J.R.; Holloway, F.W.; Rupert, P.R.; Ozarski, R.G.; Suski, G.J.

    1979-01-01

    To satisfy a broad range of control-analysis and data-acquisition requirements for Shiva, a hierarchical, computer-based, modular-distributed control system was designed. This system handles the more than 3000 control elements and 1000 data acquisition units in a severe high-voltage, high-current environment. The control system design gives one a flexible and reliable configuration to meet the development milestones for Shiva within critical time limits

  4. Hierarchical document categorization using associative networks

    NARCIS (Netherlands)

    Bloom, Niels; Theune, Mariet; de Jong, Franciska M.G.; Klement, E.P.; Borutzky, W.; Fahringer, T.; Hamza, M.H.; Uskov, V.

    Associative networks are a connectionist language model with the ability to handle dynamic data. We used two associative networks to categorize random sets of related Wikipedia articles with only their raw text as input. We then compared the resulting categorization to a gold standard: the manual

  5. Changes of hierarchical network in local and world stock market

    Science.gov (United States)

    Patwary, Enayet Ullah; Lee, Jong Youl; Nobi, Ashadun; Kim, Doo Hwan; Lee, Jae Woo

    2017-10-01

    We consider the cross-correlation coefficients of the daily returns in the local and global stock markets. We generate the minimal spanning tree (MST) using the correlation matrix. We observe that the MSTs change their structure from chain-like networks to star-like networks during periods of market uncertainty. We quantify the measure of the hierarchical network utilizing the value of the hierarchy measured by the hierarchical path. The hierarchy and betweenness centrality characterize the state of the market regarding the impact of crises. During crises, the non-financial company is established as the central node of the MST. However, before the crisis and during stable periods, the financial company is occupying the central node of the MST in the Korean and the U.S. stock markets. The changes in the network structure and the central node are good indicators of an upcoming crisis.

  6. Interoperable Communications for Hierarchical Heterogeneous Wireless Networks

    Science.gov (United States)

    2016-04-01

    International Conference on Advanced Networks and Telecommuncations Systems (ANTS). 14-DEC-13, Kattankulathur, India. : , Husheng Li, Qi Zeng , Lijun Qian. GPS...correlation in space is too large, which implies that the correlation is overestimated. Other methods may be more accurate, faster or less memory ...limited, an intelligent mechanism is needed for the information selection and signaling design of the cross-network communication for collaborative

  7. Hierarchical modeling of molecular energies using a deep neural network

    Science.gov (United States)

    Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

    2018-06-01

    We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

  8. Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography.

    Science.gov (United States)

    Phillips, Holly N; Blenkmann, Alejandro; Hughes, Laura E; Kochen, Silvia; Bekinschtein, Tristan A; Cam-Can; Rowe, James B

    2016-09-01

    We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical 'nodes' in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography - ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural

  9. Metastable states in the hierarchical Dyson model drive parallel processing in the hierarchical Hopfield network

    International Nuclear Information System (INIS)

    Agliari, Elena; Barra, Adriano; Guerra, Francesco; Galluzzi, Andrea; Tantari, Daniele; Tavani, Flavia

    2015-01-01

    In this paper, we introduce and investigate the statistical mechanics of hierarchical neural networks. First, we approach these systems à la Mattis, by thinking of the Dyson model as a single-pattern hierarchical neural network. We also discuss the stability of different retrievable states as predicted by the related self-consistencies obtained both from a mean-field bound and from a bound that bypasses the mean-field limitation. The latter is worked out by properly reabsorbing the magnetization fluctuations related to higher levels of the hierarchy into effective fields for the lower levels. Remarkably, mixing Amit's ansatz technique for selecting candidate-retrievable states with the interpolation procedure for solving for the free energy of these states, we prove that, due to gauge symmetry, the Dyson model accomplishes both serial and parallel processing. We extend this scenario to multiple stored patterns by implementing the Hebb prescription for learning within the couplings. This results in Hopfield-like networks constrained on a hierarchical topology, for which, by restricting to the low-storage regime where the number of patterns grows at its most logarithmical with the amount of neurons, we prove the existence of the thermodynamic limit for the free energy, and we give an explicit expression of its mean-field bound and of its related improved bound. We studied the resulting self-consistencies for the Mattis magnetizations, which act as order parameters, are studied and the stability of solutions is analyzed to get a picture of the overall retrieval capabilities of the system according to both mean-field and non-mean-field scenarios. Our main finding is that embedding the Hebbian rule on a hierarchical topology allows the network to accomplish both serial and parallel processing. By tuning the level of fast noise affecting it or triggering the decay of the interactions with the distance among neurons, the system may switch from sequential retrieval to

  10. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    OpenAIRE

    Yan, Jinpei; Qi, Yong; Rao, Qifan

    2018-01-01

    Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequence...

  11. Hierarchical Communication Network Architectures for Offshore Wind Power Farms

    Directory of Open Access Journals (Sweden)

    Mohamed A. Ahmed

    2014-05-01

    Full Text Available Nowadays, large-scale wind power farms (WPFs bring new challenges for both electric systems and communication networks. Communication networks are an essential part of WPFs because they provide real-time control and monitoring of wind turbines from a remote location (local control center. However, different wind turbine applications have different requirements in terms of data volume, latency, bandwidth, QoS, etc. This paper proposes a hierarchical communication network architecture that consist of a turbine area network (TAN, farm area network (FAN, and control area network (CAN for offshore WPFs. The two types of offshore WPFs studied are small-scale WPFs close to the grid and medium-scale WPFs far from the grid. The wind turbines are modelled based on the logical nodes (LN concepts of the IEC 61400-25 standard. To keep pace with current developments in wind turbine technology, the network design takes into account the extension of the LNs for both the wind turbine foundation and meteorological measurements. The proposed hierarchical communication network is based on Switched Ethernet. Servers at the control center are used to store and process the data received from the WPF. The network architecture is modelled and evaluated via OPNET. We investigated the end-to-end (ETE delay for different WPF applications. The results are validated by comparing the amount of generated sensing data with that of received traffic at servers. The network performance is evaluated, analyzed and discussed in view of end-to-end (ETE delay for different link bandwidths.

  12. Hierarchical-control-based output synchronization of coexisting attractor networks

    International Nuclear Information System (INIS)

    Yun-Zhong, Song; Yi-Fa, Tang

    2010-01-01

    This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control around its own arena. Then each dynamic node is viewed as one agent, and on account of that, the solution of output synchronization of coexisting attractor networks is transformed into a multi-agent consensus problem, which is made possible by virtue of local interaction between individual neighbours; this distributed working way of coordination is coined as inter-control, which is only specified by the topological structure of the network. Provided that the network is connected and balanced, the output synchronization would come true naturally via synergy between intra and inter-control actions, where the Tightness is proved theoretically via convex composite Lyapunov functions. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme. (general)

  13. Growth and containment of a hierarchical criminal network

    Science.gov (United States)

    Marshak, Charles Z.; Rombach, M. Puck; Bertozzi, Andrea L.; D'Orsogna, Maria R.

    2016-02-01

    We model the hierarchical evolution of an organized criminal network via antagonistic recruitment and pursuit processes. Within the recruitment phase, a criminal kingpin enlists new members into the network, who in turn seek out other affiliates. New recruits are linked to established criminals according to a probability distribution that depends on the current network structure. At the same time, law enforcement agents attempt to dismantle the growing organization using pursuit strategies that initiate on the lower level nodes and that unfold as self-avoiding random walks. The global details of the organization are unknown to law enforcement, who must explore the hierarchy node by node. We halt the pursuit when certain local criteria of the network are uncovered, encoding if and when an arrest is made; the criminal network is assumed to be eradicated if the kingpin is arrested. We first analyze recruitment and study the large scale properties of the growing network; later we add pursuit and use numerical simulations to study the eradication probability in the case of three pursuit strategies, the time to first eradication, and related costs. Within the context of this model, we find that eradication becomes increasingly costly as the network increases in size and that the optimal way of arresting the kingpin is to intervene at the early stages of network formation. We discuss our results in the context of dark network disruption and their implications on possible law enforcement strategies.

  14. Complex networks with scale-free nature and hierarchical modularity

    Science.gov (United States)

    Shekatkar, Snehal M.; Ambika, G.

    2015-09-01

    Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

  15. Hierarchical modular granular neural networks with fuzzy aggregation

    CERN Document Server

    Sanchez, Daniela

    2016-01-01

    In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

  16. TWO-LEVEL HIERARCHICAL COORDINATION QUEUING METHOD FOR TELECOMMUNICATION NETWORK NODES

    Directory of Open Access Journals (Sweden)

    M. V. Semenyaka

    2014-07-01

    Full Text Available The paper presents hierarchical coordination queuing method. Within the proposed method a queuing problem has been reduced to optimization problem solving that was presented as two-level hierarchical structure. The required distribution of flows and bandwidth allocation was calculated at the first level independently for each macro-queue; at the second level solutions obtained on lower level for each queue were coordinated in order to prevent probable network link overload. The method of goal coordination has been determined for multilevel structure managing, which makes it possible to define the order for consideration of queue cooperation restrictions and calculation tasks distribution between levels of hierarchy. Decisions coordination was performed by the method of Lagrange multipliers. The study of method convergence has been carried out by analytical modeling.

  17. Genomic analysis of the hierarchical structure of regulatory networks

    Science.gov (United States)

    Yu, Haiyuan; Gerstein, Mark

    2006-01-01

    A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

  18. Complex networks as an emerging property of hierarchical preferential attachment

    Science.gov (United States)

    Hébert-Dufresne, Laurent; Laurence, Edward; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

    2015-12-01

    Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

  19. Visual question answering using hierarchical dynamic memory networks

    Science.gov (United States)

    Shang, Jiayu; Li, Shiren; Duan, Zhikui; Huang, Junwei

    2018-04-01

    Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What's more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.

  20. GSMNet: A Hierarchical Graph Model for Moving Objects in Networks

    Directory of Open Access Journals (Sweden)

    Hengcai Zhang

    2017-03-01

    Full Text Available Existing data models for moving objects in networks are often limited by flexibly controlling the granularity of representing networks and the cost of location updates and do not encompass semantic information, such as traffic states, traffic restrictions and social relationships. In this paper, we aim to fill the gap of traditional network-constrained models and propose a hierarchical graph model called the Geo-Social-Moving model for moving objects in Networks (GSMNet that adopts four graph structures, RouteGraph, SegmentGraph, ObjectGraph and MoveGraph, to represent the underlying networks, trajectories and semantic information in an integrated manner. The bulk of user-defined data types and corresponding operators is proposed to handle moving objects and answer a new class of queries supporting three kinds of conditions: spatial, temporal and semantic information. Then, we develop a prototype system with the native graph database system Neo4Jto implement the proposed GSMNet model. In the experiment, we conduct the performance evaluation using simulated trajectories generated from the BerlinMOD (Berlin Moving Objects Database benchmark and compare with the mature MOD system Secondo. The results of 17 benchmark queries demonstrate that our proposed GSMNet model has strong potential to reduce time-consuming table join operations an d shows remarkable advantages with regard to representing semantic information and controlling the cost of location updates.

  1. A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

    Directory of Open Access Journals (Sweden)

    Nabil Sabor

    2017-01-01

    Full Text Available Introducing mobility to Wireless Sensor Networks (WSNs puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs. Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

  2. Default mode network connectivity during task execution.

    Science.gov (United States)

    Vatansever, D; Menon, D K; Manktelow, A E; Sahakian, B J; Stamatakis, E A

    2015-11-15

    Initially described as task-induced deactivations during goal-directed paradigms of high attentional load, the unresolved functionality of default mode regions has long been assumed to interfere with task performance. However, recent evidence suggests a potential default mode network involvement in fulfilling cognitive demands. We tested this hypothesis in a finger opposition paradigm with task and fixation periods which we compared with an independent resting state scan using functional magnetic resonance imaging and a comprehensive analysis pipeline including activation, functional connectivity, behavioural and graph theoretical assessments. The results indicate task specific changes in the default mode network topography. Behaviourally, we show that increased connectivity of the posterior cingulate cortex with the left superior frontal gyrus predicts faster reaction times. Moreover, interactive and dynamic reconfiguration of the default mode network regions' functional connections illustrates their involvement with the task at hand with higher-level global parallel processing power, yet preserved small-world architecture in comparison with rest. These findings demonstrate that the default mode network does not disengage during this paradigm, but instead may be involved in task relevant processing. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Integration of relational and hierarchical network information for protein function prediction

    Directory of Open Access Journals (Sweden)

    Jiang Xiaoyu

    2008-08-01

    Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

  4. A hierarchical network modeling method for railway tunnels safety assessment

    Science.gov (United States)

    Zhou, Jin; Xu, Weixiang; Guo, Xin; Liu, Xumin

    2017-02-01

    Using network theory to model risk-related knowledge on accidents is regarded as potential very helpful in risk management. A large amount of defects detection data for railway tunnels is collected in autumn every year in China. It is extremely important to discover the regularities knowledge in database. In this paper, based on network theories and by using data mining techniques, a new method is proposed for mining risk-related regularities to support risk management in railway tunnel projects. A hierarchical network (HN) model which takes into account the tunnel structures, tunnel defects, potential failures and accidents is established. An improved Apriori algorithm is designed to rapidly and effectively mine correlations between tunnel structures and tunnel defects. Then an algorithm is presented in order to mine the risk-related regularities table (RRT) from the frequent patterns. At last, a safety assessment method is proposed by consideration of actual defects and possible risks of defects gained from the RRT. This method cannot only generate the quantitative risk results but also reveal the key defects and critical risks of defects. This paper is further development on accident causation network modeling methods which can provide guidance for specific maintenance measure.

  5. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  6. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Directory of Open Access Journals (Sweden)

    Jianfeng Xu

    Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  7. Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

  8. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Jinpei Yan

    2018-01-01

    Full Text Available Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN, a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.

  9. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  10. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

    2003-01-01

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  11. Hierarchical Data Distribution Scheme for Peer-to-Peer Networks

    Science.gov (United States)

    Bhushan, Shashi; Dave, M.; Patel, R. B.

    2010-11-01

    In the past few years, peer-to-peer (P2P) networks have become an extremely popular mechanism for large-scale content sharing. P2P systems have focused on specific application domains (e.g. music files, video files) or on providing file system like capabilities. P2P is a powerful paradigm, which provides a large-scale and cost-effective mechanism for data sharing. P2P system may be used for storing data globally. Can we implement a conventional database on P2P system? But successful implementation of conventional databases on the P2P systems is yet to be reported. In this paper we have presented the mathematical model for the replication of the partitions and presented a hierarchical based data distribution scheme for the P2P networks. We have also analyzed the resource utilization and throughput of the P2P system with respect to the availability, when a conventional database is implemented over the P2P system with variable query rate. Simulation results show that database partitions placed on the peers with higher availability factor perform better. Degradation index, throughput, resource utilization are the parameters evaluated with respect to the availability factor.

  12. Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling.

    Science.gov (United States)

    Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

    2016-01-01

    Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  13. Probabilistic inference: Task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modelling

    Directory of Open Access Journals (Sweden)

    Moritz eBoos

    2016-05-01

    Full Text Available Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modelling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities by two (likelihoods design. Five computational models of cognitive processes were compared with the observed behaviour. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model’s success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modelling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modelling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  14. Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

    Science.gov (United States)

    Nitta, Tohru

    2017-10-01

    We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

  15. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  16. A Hierarchical Convolutional Neural Network for vesicle fusion event classification.

    Science.gov (United States)

    Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke

    2017-09-01

    Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    Science.gov (United States)

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  18. A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

    DEFF Research Database (Denmark)

    Ding, Tao; Li, Cheng; Huang, Can

    2018-01-01

    –slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....

  19. Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures.

    Directory of Open Access Journals (Sweden)

    Luping Zhou

    results. Without requiring new sources of information, our proposed approach improves the accuracy of MCI prediction from 80.83% (of conventional volumetric features to 84.35% (of hierarchical network features, evaluated using data sets randomly drawn from the ADNI (Alzheimer's Disease Neuroimaging Initiative dataset.

  20. Implementation of Hierarchical Task Analysis for User Interface Design in Drawing Application for Early Childhood Education

    Directory of Open Access Journals (Sweden)

    Mira Kania Sabariah

    2016-05-01

    Full Text Available Draw learning in early childhood is an important lesson and full of stimulation of the process of growth and development of children which could help to train the fine motor skills. We have had a lot of applications that can be used to perform learning, including interactive learning applications. Referring to the observations that have been conducted showed that the experiences given by the applications that exist today are very diverse and have not been able to represent the model of learning and characteristics of early childhood (4-6 years. Based on the results, Hierarchical Task Analysis method generated a list of tasks that must be done in designing an user interface that represents the user experience in draw learning. Then by using the Heuristic Evaluation method the usability of the model has fulfilled a very good level of understanding and also it can be enhanced and produce a better model.

  1. Hierarchical Task Network Prototyping In Unity3d

    Science.gov (United States)

    2016-06-01

    provided during run-time, or only have limited high level command input. This puts more of an impetus on the fidelity and realism of the agent...development. Cinematic effects were only added where necessary for debugging. Game Loop Pattern At its core, Unity is a game development engine, and

  2. Modeling Dynamic Tactical Behaviors in Combatxxi using Hierarchical Task Networks

    Science.gov (United States)

    2014-06-01

    lay , 74 S t r i n g r a n g e I n M e t e r s , 75 S t r i n g dataType , 76 O b j e c t d a t a ) 77 { 78 79 S t r i n g d a t a P l a c e H o l d e...idTime , 214 S t r i n g cance lTime , 215 S t r i n g de lay , 216 S t r i n g r a n g e I n M e t e r s , 217 S t r i n g dataType , 218 O b j e c t...s i f i e r , 227 va l idTime , 228 cance lTime , 229 de lay , 230 r a n g e I n M e t e r s , 231 dataType , 232 d a t a ) ; 233 234 L i s t < S t r

  3. On the design of a hierarchical SS7 network: A graph theoretical approach

    Science.gov (United States)

    Krauss, Lutz; Rufa, Gerhard

    1994-04-01

    This contribution is concerned with the design of Signaling System No. 7 networks based on graph theoretical methods. A hierarchical network topology is derived by combining the advantage of the hierarchical network structure with the realization of node disjoint routes between nodes of the network. By using specific features of this topology, we develop an algorithm to construct circle-free routing data and to assure bidirectionality also in case of failure situations. The methods described are based on the requirements that the network topology, as well as the routing data, may be easily changed.

  4. Function approximation of tasks by neural networks

    International Nuclear Information System (INIS)

    Gougam, L.A.; Chikhi, A.; Mekideche-Chafa, F.

    2008-01-01

    For several years now, neural network models have enjoyed wide popularity, being applied to problems of regression, classification and time series analysis. Neural networks have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. The latter is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. In a previous contribution, we have used a well known simplified architecture to show that it provides a reasonably efficient, practical and robust, multi-frequency analysis. We have investigated the universal approximation theory of neural networks whose transfer functions are: sigmoid (because of biological relevance), Gaussian and two specified families of wavelets. The latter have been found to be more appropriate to use. The aim of the present contribution is therefore to use a m exican hat wavelet a s transfer function to approximate different tasks relevant and inherent to various applications in physics. The results complement and provide new insights into previously published results on this problem

  5. Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks

    OpenAIRE

    Kaiser, Marcus; Hilgetag, Claus C.

    2010-01-01

    An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show...

  6. Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

    Science.gov (United States)

    Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

    2018-02-05

    Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Construction of mammographic examination process ontology using bottom-up hierarchical task analysis.

    Science.gov (United States)

    Yagahara, Ayako; Yokooka, Yuki; Jiang, Guoqian; Tsuji, Shintarou; Fukuda, Akihisa; Nishimoto, Naoki; Kurowarabi, Kunio; Ogasawara, Katsuhiko

    2018-03-01

    Describing complex mammography examination processes is important for improving the quality of mammograms. It is often difficult for experienced radiologic technologists to explain the process because their techniques depend on their experience and intuition. In our previous study, we analyzed the process using a new bottom-up hierarchical task analysis and identified key components of the process. Leveraging the results of the previous study, the purpose of this study was to construct a mammographic examination process ontology to formally describe the relationships between the process and image evaluation criteria to improve the quality of mammograms. First, we identified and created root classes: task, plan, and clinical image evaluation (CIE). Second, we described an "is-a" relation referring to the result of the previous study and the structure of the CIE. Third, the procedural steps in the ontology were described using the new properties: "isPerformedBefore," "isPerformedAfter," and "isPerformedAfterIfNecessary." Finally, the relationships between tasks and CIEs were described using the "isAffectedBy" property to represent the influence of the process on image quality. In total, there were 219 classes in the ontology. By introducing new properties related to the process flow, a sophisticated mammography examination process could be visualized. In relationships between tasks and CIEs, it became clear that the tasks affecting the evaluation criteria related to positioning were greater in number than those for image quality. We developed a mammographic examination process ontology that makes knowledge explicit for a comprehensive mammography process. Our research will support education and help promote knowledge sharing about mammography examination expertise.

  8. Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks

    International Nuclear Information System (INIS)

    Wang Shengjun; Zhou Changsong

    2012-01-01

    One of the most prominent architecture properties of neural networks in the brain is the hierarchical modular structure. How does the structure property constrain or improve brain function? It is thought that operating near criticality can be beneficial for brain function. Here, we find that networks with modular structure can extend the parameter region of coupling strength over which critical states are reached compared to non-modular networks. Moreover, we find that one aspect of network function—dynamical range—is highest for the same parameter region. Thus, hierarchical modularity enhances robustness of criticality as well as function. However, too much modularity constrains function by preventing the neural networks from reaching critical states, because the modular structure limits the spreading of avalanches. Our results suggest that the brain may take advantage of the hierarchical modular structure to attain criticality and enhanced function. (paper)

  9. TiO2 nanowire-templated hierarchical nanowire network as water-repelling coating

    Science.gov (United States)

    Hang, Tian; Chen, Hui-Jiuan; Xiao, Shuai; Yang, Chengduan; Chen, Meiwan; Tao, Jun; Shieh, Han-ping; Yang, Bo-ru; Liu, Chuan; Xie, Xi

    2017-12-01

    Extraordinary water-repelling properties of superhydrophobic surfaces make them novel candidates for a great variety of potential applications. A general approach to achieve superhydrophobicity requires low-energy coating on the surface and roughness on nano- and micrometre scale. However, typical construction of superhydrophobic surfaces with micro-nano structure through top-down fabrication is restricted by sophisticated fabrication techniques and limited choices of substrate materials. Micro-nanoscale topographies templated by conventional microparticles through surface coating may produce large variations in roughness and uncontrollable defects, resulting in poorly controlled surface morphology and wettability. In this work, micro-nanoscale hierarchical nanowire network was fabricated to construct self-cleaning coating using one-dimensional TiO2 nanowires as microscale templates. Hierarchical structure with homogeneous morphology was achieved by branching ZnO nanowires on the TiO2 nanowire backbones through hydrothermal reaction. The hierarchical nanowire network displayed homogeneous micro/nano-topography, in contrast to hierarchical structure templated by traditional microparticles. This hierarchical nanowire network film exhibited high repellency to both water and cell culture medium after functionalization with fluorinated organic molecules. The hierarchical structure templated by TiO2 nanowire coating significantly increased the surface superhydrophobicity compared to vertical ZnO nanowires with nanotopography alone. Our results demonstrated a promising strategy of using nanowires as microscale templates for the rational design of hierarchical coatings with desired superhydrophobicity that can also be applied to various substrate materials.

  10. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

    Science.gov (United States)

    Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

    2017-01-01

    The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

  11. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems.

    Directory of Open Access Journals (Sweden)

    Martin Rosvall

    Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.

  12. Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

    OpenAIRE

    Chih-Yu Wen; Ying-Chih Chen

    2009-01-01

    This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show t...

  13. Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

    Science.gov (United States)

    Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

    2010-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  14. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

    Directory of Open Access Journals (Sweden)

    Sheng-Jun Wang

    2011-06-01

    Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

  15. Numerical analysis of a neural network with hierarchically organized patterns

    International Nuclear Information System (INIS)

    Bacci, Silvia; Wiecko, Cristina; Parga, Nestor

    1988-01-01

    A numerical analysis of the retrieval behaviour of an associative memory model where the memorized patterns are stored hierarchically is performed. It is found that the model is able to categorize errors. For a finite number of categories, these are retrieved correctly even when the stored patterns are not. Instead, when they are allowed to increase with the number of neurons, their retrieval quality deteriorates above a critical category capacity. (Author)

  16. Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chih-Yu Wen

    2009-05-01

    Full Text Available This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

  17. Dynamic hierarchical sleep scheduling for wireless ad-hoc sensor networks.

    Science.gov (United States)

    Wen, Chih-Yu; Chen, Ying-Chih

    2009-01-01

    This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

  18. Multiple dynamical time-scales in networks with hierarchically ...

    Indian Academy of Sciences (India)

    cists from resistor networks to polymer contact structure to spin interactions in disordered ... the intracellular signalling system to neuronal networks to ecological food ... tion of the key players can be used to develop drugs targeted specifically ...

  19. Loss Performance Modeling for Hierarchical Heterogeneous Wireless Networks With Speed-Sensitive Call Admission Control

    DEFF Research Database (Denmark)

    Huang, Qian; Huang, Yue-Cai; Ko, King-Tim

    2011-01-01

    . This approach avoids unnecessary and frequent handoff between cells and reduces signaling overheads. An approximation model with guaranteed accuracy and low computational complexity is presented for the loss performance of multiservice traffic. The accuracy of numerical results is validated by comparing......A hierarchical overlay structure is an alternative solution that integrates existing and future heterogeneous wireless networks to provide subscribers with better mobile broadband services. Traffic loss performance in such integrated heterogeneous networks is necessary for an operator's network...

  20. Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

    Science.gov (United States)

    2008-03-01

    warfare, computer network operations, psychological operations, military deception, and operations security, in concert with specified supporting and...you up short—you were subconsciously predicting something else and were surprised by the mismatch” [3]. Notable neurobiologist Horace Barlow of the...malicious network activity is flagged as abnormal . That is, test data should present the N-HTM network with spatial-temporal patterns that do not match 46

  1. A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2014-12-01

    Full Text Available This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data.

  2. A Hardware-Supported Algorithm for Self-Managed and Choreographed Task Execution in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Borja Bordel

    2018-03-01

    Full Text Available Nowadays, sensor networks are composed of a great number of tiny resource-constraint nodes, whose management is increasingly more complex. In fact, although collaborative or choreographic task execution schemes are which fit in the most perfect way with the nature of sensor networks, they are rarely implemented because of the high resource consumption of these algorithms (especially if networks include many resource-constrained devices. On the contrary, hierarchical networks are usually designed, in whose cusp it is included a heavy orchestrator with a remarkable processing power, being able to implement any necessary management solution. However, although this orchestration approach solves most practical management problems of sensor networks, a great amount of the operation time is wasted while nodes request the orchestrator to address a conflict and they obtain the required instructions to operate. Therefore, in this paper it is proposed a new mechanism for self-managed and choreographed task execution in sensor networks. The proposed solution considers only a lightweight gateway instead of traditional heavy orchestrators and a hardware-supported algorithm, which consume a negligible amount of resources in sensor nodes. The gateway avoids the congestion of the entire sensor network and the hardware-supported algorithm enables a choreographed task execution scheme, so no particular node is overloaded. The performance of the proposed solution is evaluated through numerical and electronic ModelSim-based simulations.

  3. The exact Laplacian spectrum for the Dyson hierarchical network.

    Science.gov (United States)

    Agliari, Elena; Tavani, Flavia

    2017-01-09

    We consider the Dyson hierarchical graph , that is a weighted fully-connected graph, where the pattern of weights is ruled by the parameter σ ∈ (1/2, 1]. Exploiting the deterministic recursivity through which is built, we are able to derive explicitly the whole set of the eigenvalues and the eigenvectors for its Laplacian matrix. Given that the Laplacian operator is intrinsically implied in the analysis of dynamic processes (e.g., random walks) occurring on the graph, as well as in the investigation of the dynamical properties of connected structures themselves (e.g., vibrational structures and relaxation modes), this result allows addressing analytically a large class of problems. In particular, as examples of applications, we study the random walk and the continuous-time quantum walk embedded in , the relaxation times of a polymer whose structure is described by , and the community structure of in terms of modularity measures.

  4. Eigenspaces of networks reveal the overlapping and hierarchical community structure more precisely

    International Nuclear Information System (INIS)

    Ma, Xiaoke; Gao, Lin; Yong, Xuerong

    2010-01-01

    Identifying community structure is fundamental for revealing the structure–functionality relationship in complex networks, and spectral algorithms have been shown to be powerful for this purpose. In a traditional spectral algorithm, each vertex of a network is embedded into a spectral space by making use of the eigenvectors of the adjacency matrix or Laplacian matrix of the graph. In this paper, a novel spectral approach for revealing the overlapping and hierarchical community structure of complex networks is proposed by not only using the eigenvalues and eigenvectors but also the properties of eigenspaces of the networks involved. This gives us a better characterization of community. We first show that the communicability between a pair of vertices can be rewritten in term of eigenspaces of a network. An agglomerative clustering algorithm is then presented to discover the hierarchical communities using the communicability matrix. Finally, these overlapping vertices are discovered with the corresponding eigenspaces, based on the fact that the vertices more densely connected amongst one another are more likely to be linked through short cycles. Compared with the traditional spectral algorithms, our algorithm can identify both the overlapping and hierarchical community without increasing the time complexity O(n 3 ), where n is the size of the network. Furthermore, our algorithm can also distinguish the overlapping vertices from bridges. The method is tested by applying it to some computer-generated and real-world networks. The experimental results indicate that our algorithm can reveal community structure more precisely than the traditional spectral approaches

  5. Detecting Hidden Hierarchy of Non Hierarchical Terrorist Networks

    DEFF Research Database (Denmark)

    Memon, Nasrullah

    measures (and combinations of them) to identify key players (important nodes) in terrorist networks. Our recently introduced techniques and algorithms (which are also implemented in the investigative data mining toolkit known as iMiner) will be particularly useful for law enforcement agencies that need...... to analyze terrorist networks and prioritize their targets. Applying recently introduced mathematical methods for constructing the hidden hierarchy of "nonhierarchical" terrorist networks; we present case studies of the terrorist attacks occurred / planned in the past, in order to identify hidden hierarchy...

  6. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Science.gov (United States)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  7. On Hierarchical Extensions of Large-Scale 4-regular Grid Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Patel, A.; Knudsen, Thomas Phillip

    It is studied how the introduction of ordered hierarchies in 4-regular grid network structures decreses distances remarkably, while at the same time allowing for simple topological routing schemes. Both meshes and tori are considered; in both cases non-hierarchical structures have power law depen...

  8. Default network connectivity during a working memory task.

    Science.gov (United States)

    Bluhm, Robyn L; Clark, C Richard; McFarlane, Alexander C; Moores, Kathryn A; Shaw, Marnie E; Lanius, Ruth A

    2011-07-01

    The default network exhibits correlated activity at rest and has shown decreased activation during performance of cognitive tasks. There has been little investigation of changes in connectivity of this network during task performance. In this study, we examined task-related modulation of connectivity between two seed regions from the default network posterior cingulated cortex (PCC) and medial prefrontal cortex (mPFC) and the rest of the brain in 12 healthy adults. The purpose was to determine (1) whether connectivity within the default network differs between a resting state and performance of a cognitive (working memory) task and (2) whether connectivity differs between these nodes of the default network and other brain regions, particularly those implicated in cognitive tasks. There was little change in connectivity with the other main areas of the default network for either seed region, but moderate task-related changes in connectivity occurred between seed regions and regions outside the default network. For example, connectivity of the mPFC with the right insula and the right superior frontal gyrus decreased during task performance. Increased connectivity during the working memory task occurred between the PCC and bilateral inferior frontal gyri, and between the mPFC and the left inferior frontal gyrus, cuneus, superior parietal lobule, middle temporal gyrus and cerebellum. Overall, the areas showing greater correlation with the default network seed regions during task than at rest have been previously implicated in working memory tasks. These changes may reflect a decrease in the negative correlations occurring between the default and task-positive networks at rest. Copyright © 2010 Wiley-Liss, Inc.

  9. Extension of mixture-of-experts networks for binary classification of hierarchical data.

    Science.gov (United States)

    Ng, Shu-Kay; McLachlan, Geoffrey J

    2007-09-01

    For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be

  10. A local adaptive algorithm for emerging scale-free hierarchical networks

    International Nuclear Information System (INIS)

    Gomez Portillo, I J; Gleiser, P M

    2010-01-01

    In this work we study a growing network model with chaotic dynamical units that evolves using a local adaptive rewiring algorithm. Using numerical simulations we show that the model allows for the emergence of hierarchical networks. First, we show that the networks that emerge with the algorithm present a wide degree distribution that can be fitted by a power law function, and thus are scale-free networks. Using the LaNet-vi visualization tool we present a graphical representation that reveals a central core formed only by hubs, and also show the presence of a preferential attachment mechanism. In order to present a quantitative analysis of the hierarchical structure we analyze the clustering coefficient. In particular, we show that as the network grows the clustering becomes independent of system size, and also presents a power law decay as a function of the degree. Finally, we compare our results with a similar version of the model that has continuous non-linear phase oscillators as dynamical units. The results show that local interactions play a fundamental role in the emergence of hierarchical networks.

  11. Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

    Science.gov (United States)

    Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

    2017-07-19

    Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

  12. Hierarchical brain networks active in approach and avoidance goal pursuit.

    Science.gov (United States)

    Spielberg, Jeffrey M; Heller, Wendy; Miller, Gregory A

    2013-01-01

    Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal-pursuit processes (e.g., motivation) has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity) vital to goal-pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging) with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  13. Simulating individual-based models of epidemics in hierarchical networks

    NARCIS (Netherlands)

    Quax, R.; Bader, D.A.; Sloot, P.M.A.

    2009-01-01

    Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the Simulator of Epidemic Evolution in Complex Networks (SEECN), an efficient simulator of detailed individual-based models by parameterizing separate dynamics

  14. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  15. From "rest" to language task: Task activation selects and prunes from broader resting-state network.

    Science.gov (United States)

    Doucet, Gaelle E; He, Xiaosong; Sperling, Michael R; Sharan, Ashwini; Tracy, Joseph I

    2017-05-01

    Resting-state networks (RSNs) show spatial patterns generally consistent with networks revealed during cognitive tasks. However, the exact degree of overlap between these networks has not been clearly quantified. Such an investigation shows promise for decoding altered functional connectivity (FC) related to abnormal language functioning in clinical populations such as temporal lobe epilepsy (TLE). In this context, we investigated the network configurations during a language task and during resting state using FC. Twenty-four healthy controls, 24 right and 24 left TLE patients completed a verb generation (VG) task and a resting-state fMRI scan. We compared the language network revealed by the VG task with three FC-based networks (seeding the left inferior frontal cortex (IFC)/Broca): two from the task (ON, OFF blocks) and one from the resting state. We found that, for both left TLE patients and controls, the RSN recruited regions bilaterally, whereas both VG-on and VG-off conditions produced more left-lateralized FC networks, matching more closely with the activated language network. TLE brings with it variability in both task-dependent and task-independent networks, reflective of atypical language organization. Overall, our findings suggest that our RSN captured bilateral activity, reflecting a set of prepotent language regions. We propose that this relationship can be best understood by the notion of pruning or winnowing down of the larger language-ready RSN to carry out specific task demands. Our data suggest that multiple types of network analyses may be needed to decode the association between language deficits and the underlying functional mechanisms altered by disease. Hum Brain Mapp 38:2540-2552, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  16. Sleeping of a Complex Brain Networks with Hierarchical Organization

    Science.gov (United States)

    Zhang, Ying-Yue; Yang, Qiu-Ying; Chen, Tian-Lun

    2009-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

  17. Hierarchical brain networks active in approach and avoidance goal pursuit

    Directory of Open Access Journals (Sweden)

    Jeffrey Martin Spielberg

    2013-06-01

    Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  18. Hierarchical micro-mobility management in high-speed multihop access networks

    Institute of Scientific and Technical Information of China (English)

    TANG Bi-hua; MA Xiao-lei; LIU Yuan-an; GAO Jin-chun

    2006-01-01

    This article integrates the hierarchical micro-mobility management and the high-speed multihop access networks (HMAN), to accomplish the smooth handover between different access routers. The proposed soft handover scheme in the high-speed HMAN can solve the micro-mobility management problem in the access network. This article also proposes the hybrid access router (AR) advertisement scheme and AR selection algorithm, which uses the time delay and stable route to the AR as the gateway selection parameters. By simulation, the proposed micro-mobility management scheme can achieve high packet delivery fraction and improve the lifetime of network.

  19. Topology of the correlation networks among major currencies using hierarchical structure methods

    Science.gov (United States)

    Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf

    2011-02-01

    We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.

  20. Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

    Science.gov (United States)

    Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

    2018-04-01

    One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community

  1. Energy-efficient hierarchical processing in the network of wireless intelligent sensors (WISE)

    Science.gov (United States)

    Raskovic, Dejan

    Sensor network nodes have benefited from technological advances in the field of wireless communication, processing, and power sources. However, the processing power of microcontrollers is often not sufficient to perform sophisticated processing, while the power requirements of digital signal processing boards or handheld computers are usually too demanding for prolonged system use. We are matching the intrinsic hierarchical nature of many digital signal-processing applications with the natural hierarchy in distributed wireless networks, and building the hierarchical system of wireless intelligent sensors. Our goal is to build a system that will exploit the hierarchical organization to optimize the power consumption and extend battery life for the given time and memory constraints, while providing real-time processing of sensor signals. In addition, we are designing our system to be able to adapt to the current state of the environment, by dynamically changing the algorithm through procedure replacement. This dissertation presents the analysis of hierarchical environment and methods for energy profiling used to evaluate different system design strategies, and to optimize time-effective and energy-efficient processing.

  2. A hierarchical clustering scheme approach to assessment of IP-network traffic using detrended fluctuation analysis

    Science.gov (United States)

    Takuma, Takehisa; Masugi, Masao

    2009-03-01

    This paper presents an approach to the assessment of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, we use a hierarchical clustering scheme, which provides a way to classify high-dimensional data with a tree-like structure. Also, in the LRD-based analysis, we employ detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. Based on sequential measurements of IP-network traffic at two locations, this paper derives corresponding values for the LRD-related parameter α that reflects the degree of LRD of measured data. In performing the hierarchical clustering scheme, we use three parameters: the α value, average throughput, and the proportion of network traffic that exceeds 80% of network bandwidth for each measured data set. We visually confirm that the traffic data can be classified in accordance with the network traffic properties, resulting in that the combined depiction of the LRD and other factors can give us an effective assessment of network conditions at different times.

  3. Systemic risk and hierarchical transitions of financial networks

    Science.gov (United States)

    Nobi, Ashadun; Lee, Jae Woo

    2017-06-01

    In this paper, the change in topological hierarchy, which is measured by the minimum spanning tree constructed from the cross-correlations between the stock indices from the S & P 500 for 1998-2012 in a one year moving time window, was used to analyze a financial crisis. The hierarchy increased in all minor crises in the observation time window except for the sharp crisis of 2007-2008 when the global financial crisis occurred. The sudden increase in hierarchy just before the global financial crisis can be used for the early detection of an upcoming crisis. Clearly, the higher the hierarchy, the higher the threats to financial stability. The scaling relations were developed to observe the changes in hierarchy with the network topology. These scaling relations can also identify and quantify the financial crisis periods, and appear to contain the predictive power of an upcoming crisis.

  4. Crowdsourcing sensor tasks to a socio-geographic network

    NARCIS (Netherlands)

    Lasnia, Damian; Broering, Arne; Jirka, Simon; Remke, Albert; Pianho, M.; Santos, M.Y.; Pundt, H.

    2010-01-01

    This work describes an approach of a socio-geographic network for crowdsourcing sensor tasks to a human sensor web. Users can register as human sensors at the system by defining their skills and impact area. Based on that information, submitted sensor tasks are forwarded to the most suitable human

  5. N-Doped carbon spheres with hierarchical micropore-nanosheet networks for high performance supercapacitors.

    Science.gov (United States)

    Wang, Shoupei; Zhang, Jianan; Shang, Pei; Li, Yuanyuan; Chen, Zhimin; Xu, Qun

    2014-10-18

    N-doped carbon spheres with hierarchical micropore-nanosheet networks (HPSCSs) were facilely fabricated by a one-step carbonization and activation process of N containing polymer spheres by KOH. With the synergy effect of the multiple structures, HPSCSs exhibit a very high specific capacitance of 407.9 F g(-1) at 1 mV s(-1) (1.2 times higher than that of porous carbon spheres) and a robust cycling stability for supercapacitors.

  6. Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

    International Nuclear Information System (INIS)

    Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

    2008-01-01

    This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

  7. Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

    OpenAIRE

    Ning Yu; Renjian Feng; Jiangwen Wan; Yinfeng Wu; Yang Yu

    2011-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initia...

  8. An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sungyoung Lee

    2011-12-01

    Full Text Available In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO clustering scheme and traditional multihop Single-Input-Single-Output (SISO routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.

  9. Optimal task scheduling policy in energy harvesting wireless sensor networks

    NARCIS (Netherlands)

    Rao, Vijay S.; Prasad, R. Venkatesha; Niemegeers, Ignas G M M

    2015-01-01

    Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for long-lasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of

  10. A bioscaffolding strategy for hierarchical zeolites with a nanotube-trimodal network.

    Science.gov (United States)

    Li, Guannan; Huang, Haibo; Yu, Bowen; Wang, Yun; Tao, Jiawei; Wei, Yingxu; Li, Shougui; Liu, Zhongmin; Xu, Yan; Xu, Ruren

    2016-02-01

    Hierarchical zeolite monoliths with multimodal porosity are of paramount importance as they open up new horizons for advanced applications. So far, hierarchical zeolites based on nanotube scaffolds have never been reported. Inspired by the organization of biominerals, we have developed a novel precursor scaffolding-solid phase crystallization strategy for hierarchical zeolites with a unique nanotube scaffolding architecture and nanotube-trimodal network, where biomolecular self-assembly (BSA) provides a scaffolding blueprint. By vapor-treating Sil-1 seeded precursor scaffolds, zeolite MFI nanotube scaffolds are self-generated, during which evolution phenomena such as segmented voids and solid bridges are observed, in agreement with the Kirkendall effect in a solid-phase crystallization system. The nanotube walls are made of intergrown single crystals rendering good mechanical stability. The inner diameter of the nanotube is tunable between 30 and 90 nm by varying the thickness of the precursor layers. Macropores enclosed by cross-linked nanotubes can be modulated by the choice of BSA. Narrow mesopores are formed by intergrown nanocrystals. Hierarchical ZSM-5 monoliths with nanotube (90 nm), micropore (0.55 nm), mesopore (2 nm) and macropore (700 nm) exhibit superior catalytic performance in the methanol-to-hydrocarbon (MTH) conversion compared to conventional ZSM-5. BSA remains intact after crystallization, allowing a higher level of organization and functionalization of the zeolite nanotube scaffolds. The current work may afford a versatile strategy for hierarchical zeolite monoliths with nanotube scaffolding architectures and a nanotube-multimodal network leading to self-supporting and active zeolite catalysts, and for applications beyond.

  11. Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

    NARCIS (Netherlands)

    Basten, U.; Stelzel, C.; Fiebach, C.J.

    2013-01-01

    Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands

  12. Hierarchically Coordinated Power Management for Target Tracking in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Feng Juan

    2013-10-01

    Full Text Available Energy efficiency is very important for wireless sensor networks (WSNs since sensor nodes have a limited energy supply from a battery. So far, a lot research has focused on this issue, while less emphasis has been placed on the adaptive sleep time for each node with a consideration for the application constraints. In this paper, we propose a hierarchically coordinated power management (HCPM approach, which both addresses the energy conservation problem and reduces the packet forwarding delay for target tracking WSNs based on a virtual-grid-based network structure. We extend the network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM and the adaptive coordinate PM strategies to schedule the activities of the sensor nodes at the surveillance stage. Furthermore, we propose a hierarchical structure for the tracking stage. Experimental results show that the proposed approach has a greater capability of extending the network lifetime while maintaining a short transmission delay when compared with the protocol which does not consider the application constraints in target tracking sensor networks.

  13. Impact of hierarchical modular structure on ranking of individual nodes in directed networks

    Energy Technology Data Exchange (ETDEWEB)

    Masuda, Naoki [Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); Kawamura, Yoji [Institute for Research on Earth Evolution, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001 (Japan); Kori, Hiroshi [PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan)], E-mail: masuda@mist.i.u-tokyo.ac.jp

    2009-11-15

    Many systems, ranging from biological and engineering systems to social systems, can be modeled as directed networks, with links representing directed interaction between two nodes. To assess the importance of a node in a directed network, various centrality measures based on different criteria have been proposed. However, calculating the centrality of a node is often difficult because of the overwhelming size of the network or because the information held about the network is incomplete. Thus, developing an approximation method for estimating centrality measures is needed. In this study, we focus on modular networks; many real-world networks are composed of modules, where connection is dense within a module and sparse across different modules. We show that ranking-type centrality measures, including the PageRank, can be efficiently estimated once the modular structure of a network is extracted. We develop an analytical method to evaluate the centrality of nodes by combining the local property (i.e. indegree and outdegree of nodes) and the global property (i.e. centrality of modules). The proposed method is corroborated by real data. Our results provide a linkage between the ranking-type centrality values of modules and those of individual nodes. They also reveal the hierarchical structure of networks in the sense of subordination (not nestedness) laid out by connectivity among modules of different relative importance. The present study raises a novel motive for identifying modules in networks.

  14. Impact of hierarchical modular structure on ranking of individual nodes in directed networks

    International Nuclear Information System (INIS)

    Masuda, Naoki; Kawamura, Yoji; Kori, Hiroshi

    2009-01-01

    Many systems, ranging from biological and engineering systems to social systems, can be modeled as directed networks, with links representing directed interaction between two nodes. To assess the importance of a node in a directed network, various centrality measures based on different criteria have been proposed. However, calculating the centrality of a node is often difficult because of the overwhelming size of the network or because the information held about the network is incomplete. Thus, developing an approximation method for estimating centrality measures is needed. In this study, we focus on modular networks; many real-world networks are composed of modules, where connection is dense within a module and sparse across different modules. We show that ranking-type centrality measures, including the PageRank, can be efficiently estimated once the modular structure of a network is extracted. We develop an analytical method to evaluate the centrality of nodes by combining the local property (i.e. indegree and outdegree of nodes) and the global property (i.e. centrality of modules). The proposed method is corroborated by real data. Our results provide a linkage between the ranking-type centrality values of modules and those of individual nodes. They also reveal the hierarchical structure of networks in the sense of subordination (not nestedness) laid out by connectivity among modules of different relative importance. The present study raises a novel motive for identifying modules in networks.

  15. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  16. Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Bilal Jan

    2017-01-01

    Full Text Available Wireless sensor networks (WSN are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

  17. Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

    Science.gov (United States)

    Colclough, Giles L; Woolrich, Mark W; Harrison, Samuel J; Rojas López, Pedro A; Valdes-Sosa, Pedro A; Smith, Stephen M

    2018-05-07

    A Bayesian model for sparse, hierarchical inverse covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fmri, meg and eeg data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in meg beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Copyright © 2018. Published by Elsevier Inc.

  18. Sensor Network Disposition Facing the Task of Multisensor Cross Cueing

    Directory of Open Access Journals (Sweden)

    Ce Pang

    2017-01-01

    Full Text Available In order to build the sensor network facing the task of multisensor crossing cueing, the requirements of initiating cueing and being cued are analyzed. Probability theory is used when building models, then probability of sensor cueing in the case of target moving is given, and, after that, the best distance between two sensors is calculated. The operational environment is described by normal distribution function. In the process of distributing sensor network, their elements, operational environment demand of cueing, and the probability of sensor network coverage are considered; then the optimization algorithm of sensor network based on hypothesis testing theory is made. The simulation result indicates that the algorithm can make sensor network which is required. On the basis of that, the two cases, including targets that make linear motion and orbit motion, are used to test the performance of the sensor network, which show that the sensor network can make uninterrupted detection on targets through multisensor cross cuing.

  19. A Hybrid P2P Overlay Network for Non-strictly Hierarchically Categorized Content

    Science.gov (United States)

    Wan, Yi; Asaka, Takuya; Takahashi, Tatsuro

    In P2P content distribution systems, there are many cases in which the content can be classified into hierarchically organized categories. In this paper, we propose a hybrid overlay network design suitable for such content called Pastry/NSHCC (Pastry for Non-Strictly Hierarchically Categorized Content). The semantic information of classification hierarchies of the content can be utilized regardless of whether they are in a strict tree structure or not. By doing so, the search scope can be restrained to any granularity, and the number of query messages also decreases while maintaining keyword searching availability. Through simulation, we showed that the proposed method provides better performance and lower overhead than unstructured overlays exploiting the same semantic information.

  20. Hierarchical self-assembly of a striped gyroid formed by threaded chiral mesoscale networks

    DEFF Research Database (Denmark)

    Kirkensgaard, Jacob Judas Kain; Evans, Myfanwy; de Campo, Lilliana

    2014-01-01

    Numerical simulations reveal a family of hierarchical and chiral multicontinuous network structures self-assembled from a melt blend of Y-shaped ABC and ABD three-miktoarm star terpolymers, constrained to have equal-sized A/B and C/D chains, respectively. The C and D majority domains within...... components also forming labyrinthine domains whose geometry and topology changes systematically as a function of composition. These smaller labyrinths are well described by a family of patterns that tile the hyperbolic plane by regular degree-three trees mapped onto the gyroid. The labyrinths within......-ridden achiral patterns, containing domains of either hand, due to the achiral terpolymeric starting molecules. These mesostructures are among the most topologically complex morphologies identified to date and represent an example of hierarchical ordering within a hyperbolic pattern, a unique mode of soft...

  1. Visualization and Hierarchical Analysis of Flow in Discrete Fracture Network Models

    Science.gov (United States)

    Aldrich, G. A.; Gable, C. W.; Painter, S. L.; Makedonska, N.; Hamann, B.; Woodring, J.

    2013-12-01

    Flow and transport in low permeability fractured rock is primary in interconnected fracture networks. Prediction and characterization of flow and transport in fractured rock has important implications in underground repositories for hazardous materials (eg. nuclear and chemical waste), contaminant migration and remediation, groundwater resource management, and hydrocarbon extraction. We have developed methods to explicitly model flow in discrete fracture networks and track flow paths using passive particle tracking algorithms. Visualization and analysis of particle trajectory through the fracture network is important to understanding fracture connectivity, flow patterns, potential contaminant pathways and fast paths through the network. However, occlusion due to the large number of highly tessellated and intersecting fracture polygons preclude the effective use of traditional visualization methods. We would also like quantitative analysis methods to characterize the trajectory of a large number of particle paths. We have solved these problems by defining a hierarchal flow network representing the topology of particle flow through the fracture network. This approach allows us to analyses the flow and the dynamics of the system as a whole. We are able to easily query the flow network, and use paint-and-link style framework to filter the fracture geometry and particle traces based on the flow analytics. This allows us to greatly reduce occlusion while emphasizing salient features such as the principal transport pathways. Examples are shown that demonstrate the methodology and highlight how use of this new method allows quantitative analysis and characterization of flow and transport in a number of representative fracture networks.

  2. Dynamically Allocated Hub in Task-Evoked Network Predicts the Vulnerable Prefrontal Locus for Contextual Memory Retrieval in Macaques.

    Directory of Open Access Journals (Sweden)

    Takahiro Osada

    2015-06-01

    Full Text Available Neuroimaging and neurophysiology have revealed that multiple areas in the prefrontal cortex (PFC are activated in a specific memory task, but severity of impairment after PFC lesions is largely different depending on which activated area is damaged. The critical relationship between lesion sites and impairments has not yet been given a clear mechanistic explanation. Although recent works proposed that a whole-brain network contains hubs that play integrative roles in cortical information processing, this framework relying on an anatomy-based structural network cannot account for the vulnerable locus for a specific task, lesioning of which would bring impairment. Here, we hypothesized that (i activated PFC areas dynamically form an ordered network centered at a task-specific "functional hub" and (ii the lesion-effective site corresponds to the "functional hub," but not to a task-invariant "structural hub." To test these hypotheses, we conducted functional magnetic resonance imaging experiments in macaques performing a temporal contextual memory task. We found that the activated areas formed a hierarchical hub-centric network based on task-evoked directed connectivity, differently from the anatomical network reflecting axonal projection patterns. Using a novel simulated-lesion method based on support vector machine, we estimated severity of impairment after lesioning of each area, which accorded well with a known dissociation in contextual memory impairment in macaques (impairment after lesioning in area 9/46d, but not in area 8Ad. The predicted severity of impairment was proportional to the network "hubness" of the virtually lesioned area in the task-evoked directed connectivity network, rather than in the anatomical network known from tracer studies. Our results suggest that PFC areas dynamically and cooperatively shape a functional hub-centric network to reallocate the lesion-effective site depending on the cognitive processes, apart from

  3. A Hierarchical Energy Efficient Reliable Transport Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Prabhudutta Mohanty

    2014-12-01

    Full Text Available The two important requirements for many Wireless Senor Networks (WSNs are prolonged network lifetime and end-to-end reliability. The sensor nodes consume more energy during data transmission than the data sensing. In WSN, the redundant data increase the energy consumption, latency and reduce reliability during data transmission. Therefore, it is important to support energy efficient reliable data transport in WSNs. In this paper, we present a Hierarchical Energy Efficient Reliable Transport Protocol (HEERTP for the data transmission within the WSN. This protocol maximises the network lifetime by controlling the redundant data transmission with the co-ordination of Base Station (BS. The proposed protocol also achieves end-to-end reliability using a hop-by-hop acknowledgement scheme. We evaluate the performance of the proposed protocol through simulation. The simulation results reveal that our proposed protocol achieves better performance in terms of energy efficiency, latency and reliability than the existing protocols.

  4. Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

    Science.gov (United States)

    Wang, Fang-Fang; Su, Chao-Ton

    2014-11-01

    The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

  5. Structure-function relationship in complex brain networks expressed by hierarchical synchronization

    International Nuclear Information System (INIS)

    Zhou Changsong; Zemanova, Lucia; Zamora-Lopez, Gorka; Hilgetag, Claus C; Kurths, Juergen

    2007-01-01

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks

  6. Structure-function relationship in complex brain networks expressed by hierarchical synchronization

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Changsong [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Zemanova, Lucia [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Zamora-Lopez, Gorka [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Hilgetag, Claus C [Jacobs University Bremen, Campus Ring 6, Rm 116, D-28759 Bremen (Germany); Kurths, Juergen [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany)

    2007-06-15

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

  7. Hierarchical neural network model of the visual system determining figure/ground relation

    Science.gov (United States)

    Kikuchi, Masayuki

    2017-07-01

    One of the most important functions of the visual perception in the brain is figure/ground interpretation from input images. Figural region in 2D image corresponding to object in 3D space are distinguished from background region extended behind the object. Previously the author proposed a neural network model of figure/ground separation constructed on the standpoint that local geometric features such as curvatures and outer angles at corners are extracted and propagated along input contour in a single layer network (Kikuchi & Akashi, 2001). However, such a processing principle has the defect that signal propagation requires manyiterations despite the fact that actual visual system determines figure/ground relation within the short period (Zhou et al., 2000). In order to attain speed-up for determining figure/ground, this study incorporates hierarchical architecture into the previous model. This study confirmed the effect of the hierarchization as for the computation time by simulation. As the number of layers increased, the required computation time reduced. However, such speed-up effect was saturatedas the layers increased to some extent. This study attempted to explain this saturation effect by the notion of average distance between vertices in the area of complex network, and succeeded to mimic the saturation effect by computer simulation.

  8. An Efficient, Hierarchical Viewpoint Planning Strategy for Terrestrial Laser Scanner Networks

    Science.gov (United States)

    Jia, F.; Lichti, D. D.

    2018-05-01

    Terrestrial laser scanner (TLS) techniques have been widely adopted in a variety of applications. However, unlike in geodesy or photogrammetry, insufficient attention has been paid to the optimal TLS network design. It is valuable to develop a complete design system that can automatically provide an optimal plan, especially for high-accuracy, large-volume scanning networks. To achieve this goal, one should look at the "optimality" of the solution as well as the computational complexity in reaching it. In this paper, a hierarchical TLS viewpoint planning strategy is developed to solve the optimal scanner placement problems. If one targeted object to be scanned is simplified as discretized wall segments, any possible viewpoint can be evaluated by a score table representing its visible segments under certain scanning geometry constraints. Thus, the design goal is to find a minimum number of viewpoints that achieves complete coverage of all wall segments. The efficiency is improved by densifying viewpoints hierarchically, instead of a "brute force" search within the entire workspace. The experiment environments in this paper were simulated from two buildings located on University of Calgary campus. Compared with the "brute force" strategy in terms of the quality of the solutions and the runtime, it is shown that the proposed strategy can provide a scanning network with a compatible quality but with more than a 70 % time saving.

  9. A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-06-01

    Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

  10. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

    Science.gov (United States)

    Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

    2016-08-18

    This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

  11. Network Routing Using the Network Tasking Order, a Chron Approach

    Science.gov (United States)

    2015-03-26

    Network traffic decision algorithms have been in place since the creation of the Internet. These algorithms are successful in redirecting...example, the fifth line indicates a location of 29° 42’ 48”N, 47° 31’ 06”E and a time-on target of 1200 Zulu on the 24th of January. A typical ATO is

  12. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

  13. Task and task-free fMRI reproducibility comparison for motor network identification

    NARCIS (Netherlands)

    Kristo, G.; Rutten, G.J.; Raemaekers, M.; de Gelder, B.; Rombouts, S.A.R.B.; Ramsey, N.F.

    2014-01-01

    Test-retest reliability of individual functional magnetic resonance imaging (fMRI) results is of importance in clinical practice and longitudinal experiments. While several studies have investigated reliability of task-induced motor network activation, less is known about the reliability of the

  14. Performance Analysis of Hierarchical Group Key Management Integrated with Adaptive Intrusion Detection in Mobile ad hoc Networks

    Science.gov (United States)

    2016-04-05

    applications in wireless networks such as military battlefields, emergency response, mobile commerce , online gaming, and collaborative work are based on the...www.elsevier.com/locate/peva Performance analysis of hierarchical group key management integrated with adaptive intrusion detection in mobile ad hoc...Accepted 19 September 2010 Available online 26 September 2010 Keywords: Mobile ad hoc networks Intrusion detection Group communication systems Group

  15. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

    Science.gov (United States)

    Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

    2004-12-01

    As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

  16. Hierarchical structures of correlations networks among Turkey’s exports and imports by currencies

    Science.gov (United States)

    Kocakaplan, Yusuf; Deviren, Bayram; Keskin, Mustafa

    2012-12-01

    We have examined the hierarchical structures of correlations networks among Turkey’s exports and imports by currencies for the 1996-2010 periods, using the concept of a minimal spanning tree (MST) and hierarchical tree (HT) which depend on the concept of ultrametricity. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial markets. We derived a hierarchical organization and build the MSTs and HTs during the 1996-2001 and 2002-2010 periods. The reason for studying two different sub-periods, namely 1996-2001 and 2002-2010, is that the Euro (EUR) came into use in 2001, and some countries have made their exports and imports with Turkey via the EUR since 2002, and in order to test various time-windows and observe temporal evolution. We have carried out bootstrap analysis to associate a value of the statistical reliability to the links of the MSTs and HTs. We have also used the average linkage cluster analysis (ALCA) to observe the cluster structure more clearly. Moreover, we have obtained the bidimensional minimal spanning tree (BMST) due to economic trade being a bidimensional problem. From the structural topologies of these trees, we have identified different clusters of currencies according to their proximity and economic ties. Our results show that some currencies are more important within the network, due to a tighter connection with other currencies. We have also found that the obtained currencies play a key role for Turkey’s exports and imports and have important implications for the design of portfolio and investment strategies.

  17. Optimization of workflow scheduling in Utility Management System with hierarchical neural network

    Directory of Open Access Journals (Sweden)

    Srdjan Vukmirovic

    2011-08-01

    Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

  18. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Science.gov (United States)

    Zhu, Aichun; Wang, Tian; Snoussi, Hichem

    2018-03-01

    This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  19. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Directory of Open Access Journals (Sweden)

    Aichun Zhu

    2018-03-01

    Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  20. SMR-Based Adaptive Mobility Management Scheme in Hierarchical SIP Networks

    Directory of Open Access Journals (Sweden)

    KwangHee Choi

    2014-10-01

    Full Text Available In hierarchical SIP networks, paging is performed to reduce location update signaling cost for mobility management. However, the cost efficiency largely depends on each mobile node’s session-to-mobility-ratio (SMR, which is defined as a ratio of the session arrival rate to the movement rate. In this paper, we propose the adaptive mobility management scheme that can determine the policy regarding to each mobile node’s SMR. Each mobile node determines whether the paging is applied or not after comparing its SMR with the threshold. In other words, the paging is applied to a mobile node when a mobile node’s SMR is less than the threshold. Therefore, the proposed scheme provides a way to minimize signaling costs according to each mobile node’s SMR. We find out the optimal threshold through performance analysis, and show that the proposed scheme can reduce signaling cost than the existing SIP and paging schemes in hierarchical SIP networks.

  1. A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

    Science.gov (United States)

    Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

    2013-01-01

    Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

  2. Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

    Science.gov (United States)

    Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

    2017-09-01

    Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

  3. Enhancement of Adaptive Cluster Hierarchical Routing Protocol using Distance and Energy for Wireless Sensor Networks

    International Nuclear Information System (INIS)

    Nawar, N.M.; Soliman, S.E.; Kelash, H.M.; Ayad, N.M.

    2014-01-01

    The application of wireless networking is widely used in nuclear applications. This includes reactor control and fire dedication system. This paper is devoted to the application of this concept in the intrusion system of the Radioisotope Production Facility (RPF) of the Egyptian Atomic Energy Authority. This includes the tracking, monitoring and control components of this system. The design and implementation of wireless sensor networks has become a hot area of research due to the extensive use of sensor networks to enable applications that connect the physical world to the virtual world [1-2]. The original LEACH is named a communication protocol (clustering-based); the extended LEACH’s stochastic cluster head selection algorithm by a deterministic component. Depending on the network configuration an increase of network lifetime can be accomplished [3]. The proposed routing mechanisms after enhancement divide the nodes into clusters. A cluster head performs its task which is considerably more energy-intensive than the rest of the nodes inside sensor network. So, nodes rotate tasks at different rounds between a cluster head and other sensors throughout the lifetime of the network to balance the energy dissipation [4-5].The performance improvement when using routing protocol after enhancement of the algorithm which takes into consideration the distance and the remaining energy for choosing the cluster head by obtains from the advertise message. Network Simulator (Ns2 simulator) is used to prove that LEACH after enhancement performs better than the original LEACH protocol in terms of Average Energy, Network Life Time, Delay, Throughput and Overhead.

  4. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

    Science.gov (United States)

    Alshehhi, Rasha; Marpu, Prashanth Reddy

    2017-04-01

    Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

  5. Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

    Science.gov (United States)

    Horikawa, Tomoyasu; Kamitani, Yukiyasu

    2017-01-01

    Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

  6. Diagnostics for generalized linear hierarchical models in network meta-analysis.

    Science.gov (United States)

    Zhao, Hong; Hodges, James S; Carlin, Bradley P

    2017-09-01

    Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

  7. Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

    Science.gov (United States)

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

  8. Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ning Yu

    2011-12-01

    Full Text Available In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

  9. Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

    Science.gov (United States)

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

  10. Hierarchically porous MgCo2O4 nanochain networks: template-free synthesis and catalytic application

    Science.gov (United States)

    Guan, Xiangfeng; Yu, Yunlong; Li, Xiaoyan; Chen, Dagui; Luo, Peihui; Zhang, Yu; Guo, Shanxin

    2018-01-01

    In this work, hierarchically porous MgCo2O4 nanochain networks were successfully synthesized by a novel template-free method realized via a facile solvothermal synthesis followed by a heat treatment. The morphologies of MgCo2O4 precursor could be adjusted from nanosheets to nanobelts and finally to interwoven nanowires, depending on the volume ratio of diethylene glycol to deionized water in the solution. After calcination, the interwoven precursor nanowires were transformed to hierarchical MgCo2O4 nanochain networks with marco-/meso-porosity, which are composed of 10-20 nm nanoparticles connected one by one. Moreover, the relative formation mechanism of the MgCo2O4 nanochain networks was discussed. More importantly, when evaluated as catalytic additive for AP thermal decomposition, the MgCo2O4 nanochain networks show excellent accelerating effect. It is benefited from the unique hierarchically porous network structure and multicomponent effect, which effectively accelerates ammonia oxidation and {{{{ClO}}}4}- species dissociation. This approach opens the way to design other hierarchically porous multicomponent metal oxides.

  11. Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Antonio Molina-Pico

    2016-01-01

    Full Text Available A wildland fire is an uncontrolled fire that occurs mainly in forest areas, although it can also invade urban or agricultural areas. Among the main causes of wildfires, human factors, either intentional or accidental, are the most usual ones. The number and impact of forest fires are expected to grow as a consequence of the global warming. In order to fight against these disasters, it is necessary to adopt a comprehensive, multifaceted approach that enables a continuous situational awareness and instant responsiveness. This paper describes a hierarchical wireless sensor network aimed at early fire detection in risky areas, integrated with the fire fighting command centres, geographical information systems, and fire simulators. This configuration has been successfully tested in two fire simulations involving all the key players in fire fighting operations: fire brigades, communication systems, and aerial, coordination, and land means.

  12. Electropolymerized Star-Shaped Benzotrithiophenes Yield π-Conjugated Hierarchical Networks with High Areal Capacitance

    KAUST Repository

    Ringk, Andreas

    2016-03-30

    High-surface-area π-conjugated polymeric networks have the potential to lend outstanding capacitance to supercapacitors because of the pronounced faradaic processes that take place across the dense intimate interface between active material and electrolytes. In this report, we describe how benzo[1,2-b:3,4-b’:5,6-b’’]trithiophene (BTT) and tris-EDOT-benzo[1,2-b:3,4-b’:5,6-b’’]trithiophene (TEBTT) can serve as 2D (trivalent) building blocks in the development of electropolymerized hierarchical π-conjugated frameworks with particularly high areal capacitance. In comparing electropolymerized networks of BTT, TEBTT, and their copolymers with EDOT, we show that P(TEBTT/EDOT)-based frameworks can achieve higher areal capacitance (e.g., as high as 443.8 mF cm-2 at 1 mA cm-2) than those achieved by their respective homopolymers (PTEBTT and PEDOT) in the same experimental conditions of electrodeposition (PTEBTT: 271.1 mF cm-2 (at 1 mA cm-2) and PEDOT: 12.1 mF cm-2 (at 1 mA cm-2)). For example, P(TEBTT/EDOT)-based frameworks synthesized in a 1:1 monomer-to-comonomer ratio show a ca. 35x capacitance improvement over PEDOT. The high areal capacitance measured for P(TEBTT/EDOT) copolymers can be explained by the open, highly porous hierarchical morphologies formed during the electropolymerization step. With >70% capacitance retention over 1,000 cycles (up to 89% achieved), both PTEBTT- and P(TEBTT/EDOT)-based frameworks are resilient to repeated electrochemical cycling and can be considered promising systems for high life cycle capacitive electrode applications.

  13. Electropolymerized Star-Shaped Benzotrithiophenes Yield π-Conjugated Hierarchical Networks with High Areal Capacitance

    KAUST Repository

    Ringk, Andreas; Lignie, Adrien; Hou, Yuanfang; Alshareef, Husam N.; Beaujuge, Pierre

    2016-01-01

    High-surface-area π-conjugated polymeric networks have the potential to lend outstanding capacitance to supercapacitors because of the pronounced faradaic processes that take place across the dense intimate interface between active material and electrolytes. In this report, we describe how benzo[1,2-b:3,4-b’:5,6-b’’]trithiophene (BTT) and tris-EDOT-benzo[1,2-b:3,4-b’:5,6-b’’]trithiophene (TEBTT) can serve as 2D (trivalent) building blocks in the development of electropolymerized hierarchical π-conjugated frameworks with particularly high areal capacitance. In comparing electropolymerized networks of BTT, TEBTT, and their copolymers with EDOT, we show that P(TEBTT/EDOT)-based frameworks can achieve higher areal capacitance (e.g., as high as 443.8 mF cm-2 at 1 mA cm-2) than those achieved by their respective homopolymers (PTEBTT and PEDOT) in the same experimental conditions of electrodeposition (PTEBTT: 271.1 mF cm-2 (at 1 mA cm-2) and PEDOT: 12.1 mF cm-2 (at 1 mA cm-2)). For example, P(TEBTT/EDOT)-based frameworks synthesized in a 1:1 monomer-to-comonomer ratio show a ca. 35x capacitance improvement over PEDOT. The high areal capacitance measured for P(TEBTT/EDOT) copolymers can be explained by the open, highly porous hierarchical morphologies formed during the electropolymerization step. With >70% capacitance retention over 1,000 cycles (up to 89% achieved), both PTEBTT- and P(TEBTT/EDOT)-based frameworks are resilient to repeated electrochemical cycling and can be considered promising systems for high life cycle capacitive electrode applications.

  14. Hierarchical surface code for network quantum computing with modules of arbitrary size

    Science.gov (United States)

    Li, Ying; Benjamin, Simon C.

    2016-10-01

    The network paradigm for quantum computing involves interconnecting many modules to form a scalable machine. Typically it is assumed that the links between modules are prone to noise while operations within modules have a significantly higher fidelity. To optimize fault tolerance in such architectures we introduce a hierarchical generalization of the surface code: a small "patch" of the code exists within each module and constitutes a single effective qubit of the logic-level surface code. Errors primarily occur in a two-dimensional subspace, i.e., patch perimeters extruded over time, and the resulting noise threshold for intermodule links can exceed ˜10 % even in the absence of purification. Increasing the number of qubits within each module decreases the number of qubits necessary for encoding a logical qubit. But this advantage is relatively modest, and broadly speaking, a "fine-grained" network of small modules containing only about eight qubits is competitive in total qubit count versus a "course" network with modules containing many hundreds of qubits.

  15. A Persistent Structured Hierarchical Overlay Network to Counter Intentional Churn Attack

    Directory of Open Access Journals (Sweden)

    Ramanpreet Kaur

    2016-01-01

    Full Text Available The increased use of structured overlay network for a variety of applications has attracted a lot of attention from both research community and attackers. However, the structural constraints, open nature (anybody can join and anybody may leave, and unreliability of its participant nodes significantly affect the performance of these applications and make it vulnerable to a variety of attacks such as eclipse, Sybil, and churn. One attack to compromise the service availability in overlay network is intentional churn (join/leave attack, where a large number of malicious users will join and leave the overlay network so frequently that the entire structure collapses and becomes unavailable. The focus of this paper is to provide a new robust, efficient, and scalable hierarchical overlay architecture that will counter these attacks by providing a structure that can accommodate the fleeting behaviour of nodes without causing much structural inconsistencies. The performance evaluation showed that the proposed architecture has more failure resilience and self-organization as compared to chord based architecture. Experimental results have demonstrated that the effect of failures on an overlay is proportional to the size of failure.

  16. The use of hierarchical clustering for the design of optimized monitoring networks

    Science.gov (United States)

    Soares, Joana; Makar, Paul Andrew; Aklilu, Yayne; Akingunola, Ayodeji

    2018-05-01

    Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity and can be used to assess the efficacy and design of air quality monitoring networks. We describe here our use of Kolmogorov-Zurbenko filtering and hierarchical clustering of NO2 and SO2 passive and continuous monitoring data to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1 - R, R being the Pearson correlation coefficient, and the Euclidean distance; we find that both should be used in evaluating monitoring site similarity. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We demonstrate that clustering results depend on the air contaminant analysed, reflecting the difference in the respective emission sources of SO2 and NO2 in the region under study. Our work shows that much of the signal identifying the sources of NO2 and SO2 emissions resides in shorter timescales (hourly to daily) due to short-term variation of concentrations and that longer-term averages in data collection may lose the information needed to identify local sources. However, the methodology identifies stations mainly influenced by seasonality, if larger timescales (weekly to monthly) are considered. We have performed the first dissimilarity analysis based on gridded air quality model output and have shown that the methodology is capable of generating maps of subregions within which a single station will represent the entire subregion, to a given level of dissimilarity. We have also shown that our approach is capable of identifying different

  17. Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

    Science.gov (United States)

    Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

    In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

  18. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

    Directory of Open Access Journals (Sweden)

    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  19. Intrinsic properties of channel network structure and the hierarchical classification approach for stream-limits delineation

    Energy Technology Data Exchange (ETDEWEB)

    Afana, A.; Barrio, G. del

    2009-07-01

    Delineation of drainage networks is an essential task in hydrological and geomorphologic analysis. Manual channel definition depends on topographic contrast and is highly subjective, leading to important errors at high resolutions. different automatic methods have proposed the use of a constant threshold of up sole contributing are to define channel initiation. Actually, these are the most commonly used for the automatic-channel network extraction from Digital Models (DEMs). However, these methods fall to detect and appropriate threshold when the basin is made up to heterogeneous sub-zones, as they only work either lumped or locally. In this study, the critical threshold area for channel delineation has been defined through the analysis of dominant geometric and topologic properties of stream network formation. (Author) 5 refs.

  20. Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems

    Science.gov (United States)

    Pusuluri, Sai Teja

    Energy landscapes are often used as metaphors for phenomena in biology, social sciences and finance. Different methods have been implemented in the past for the construction of energy landscapes. Neural network models based on spin glass physics provide an excellent mathematical framework for the construction of energy landscapes. This framework uses a minimal number of parameters and constructs the landscape using data from the actual phenomena. In the past neural network models were used to mimic the storage and retrieval process of memories (patterns) in the brain. With advances in the field now, these models are being used in machine learning, deep learning and modeling of complex phenomena. Most of the past literature focuses on increasing the storage capacity and stability of stored patterns in the network but does not study these models from a modeling perspective or an energy landscape perspective. This dissertation focuses on neural network models both from a modeling perspective and from an energy landscape perspective. I firstly show how the cellular interconversion phenomenon can be modeled as a transition between attractor states on an epigenetic landscape constructed using neural network models. The model allows the identification of a reaction coordinate of cellular interconversion by analyzing experimental and simulation time course data. Monte Carlo simulations of the model show that the initial phase of cellular interconversion is a Poisson process and the later phase of cellular interconversion is a deterministic process. Secondly, I explore the static features of landscapes generated using neural network models, such as sizes of basins of attraction and densities of metastable states. The simulation results show that the static landscape features are strongly dependent on the correlation strength and correlation structure between patterns. Using different hierarchical structures of the correlation between patterns affects the landscape features

  1. Cerebrocerebellar networks during articulatory rehearsal and verbal working memory tasks.

    Science.gov (United States)

    Chen, S H Annabel; Desmond, John E

    2005-01-15

    Converging evidence has implicated the cerebellum in verbal working memory. The current fMRI study sought to further characterize cerebrocerebellar participation in this cognitive process by revealing regions of activation common to a verbal working task and an articulatory control task, as well as regions that are uniquely activated by working memory. Consistent with our model's predictions, load-dependent activations were observed in Broca's area (BA 44/6) and the superior cerebellar hemisphere (VI/CrusI) for both working memory and motoric rehearsal. In contrast, activations unique to verbal working memory were found in the inferior parietal lobule (BA 40) and the right inferior cerebellum hemisphere (VIIB). These findings provide evidence for two cerebrocerebellar networks for verbal working memory: a frontal/superior cerebellar articulatory control system and a parietal/inferior cerebellar phonological storage system.

  2. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

    Directory of Open Access Journals (Sweden)

    Buer Jan

    2004-12-01

    Full Text Available Abstract Background Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear. Results In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators. Conclusion The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E

  3. The SIS Model of Epidemic Spreading in a Hierarchical Social Network

    International Nuclear Information System (INIS)

    Grabowski, A.; Kosinski, R.A.

    2005-01-01

    The phenomenon of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The SIS model with temporal immunity to a disease and a time of incubation is used. In our model spatial localization of individuals belonging to different social groups, effectiveness of different interpersonal interactions and the mobility of a contemporary community are taken into account. The structure of interpersonal connections is based on a scale-free network. The influence of the structure of the social network on typical relations characterizing the spreading process, like a range of epidemic and epidemic curves, is discussed. The probability that endemic state occurs is also calculated. Surprisingly it occurs, that less contagious diseases has greater chance to survive. The influence of preventive vaccinations on the spreading process is investigated and critical range of vaccinations that is sufficient for the suppression of an epidemic is calculated. Our results of numerical calculations are compared with the solutions of the master equation for the spreading process, and good agreement is found. (author)

  4. Hierarchical ZnO microspheres built by sheet-like network: Large-scale synthesis and structurally enhanced catalytic performances

    International Nuclear Information System (INIS)

    Zhu Guoxing; Liu Yuanjun; Ji Zhenyuan; Bai Song; Shen Xiaoping; Xu Zheng

    2012-01-01

    Highlights: ► Hierarchical ZnO microspheres were prepared through a facile precursor procedure in the absence of self-assembled templates, organic additives, or matrices. ► The building blocks of microspheres, sheet-like ZnO networks, are porous mesocrystal terminated with (0 1 −1 0) crystal planes. ► The hierarchical ZnO microsphere catalyst exhibits structure-induced enhancement of catalytic performance and a strong durability. - Abstract: Large-scale novel hierarchical ZnO microspheres were fabricated by a facile precursor procedure in the absence of self-assembled templates, organic additives, or matrices. A field emission scanning electron microscopy (FESEM) image reveals that the ZnO microspheres with diameter of 5–18 μm are built by sheet-like ZnO networks with average thickness of 40 nm and length of several microns. High resolution transmission electron microscopy (HRTEM) image indicates that the building blocks, sheet-like ZnO networks, are porous mesocrystal terminated with {0 1 −1 0} crystal planes. A potential application of the ZnO microspheres as a catalyst in the synthesis of 5-substituted 1H-tetrazoles was investigated. It was found that the hierarchical ZnO microsphere catalyst exhibits structure-induced enhancement of catalytic performance and a strong durability.

  5. Immune networks: multi-tasking capabilities at medium load

    Science.gov (United States)

    Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

    2013-08-01

    Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ˜ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ˜ Nδ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ frameworks are required to achieve effective retrieval.

  6. A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Eva González-Parada

    2017-01-01

    Full Text Available Autonomous mobile nodes in mobile wireless sensor networks (MWSN allow self-deployment and self-healing. In both cases, the goals are: (i to achieve adequate coverage; and (ii to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage.

  7. A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks.

    Science.gov (United States)

    González-Parada, Eva; Cano-García, Jose; Aguilera, Francisco; Sandoval, Francisco; Urdiales, Cristina

    2017-01-09

    Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage.

  8. The ames network and the task group on WWER's

    International Nuclear Information System (INIS)

    Davies, L.M.; Duysen, J.C. van; Estorff, U. von; Sycamore, D.

    1997-01-01

    The European Network on 'Ageing Materials Evaluation and Studies' (AMES) was created in 1993. Its main objectives are (a) to provide information and understanding on neutron irradiation effects in reactor materials in support of designers, operators, regulators and researchers and (b) to establish and discharge projects in the above areas. The Steering Committee is composed of at least one participant from each nuclear European Union country. The JRC's Institute for Advanced Materials of the European Commission plays the role of Operating Agent and Manager of the AMES Network. This paper describes the structure, objectives, and major projects of the AMES network. Particular emphasis is placed upon the work it is intended to perform within the Task Group on 'WWER's of the first AMES project (AMES1) on 'Validation of surveillance practice and mitigation methods'. EC DGXVII is addressing the question of how to facilitate contacts between EU and Russian industries in the framework of nuclear Industrial co-operation, and this project may provide a suitable starting point upon which to develop a basis for further work of mutual interest. (author)

  9. Immune networks: multi-tasking capabilities at medium load

    International Nuclear Information System (INIS)

    Agliari, E; Annibale, A; Barra, A; Coolen, A C C; Tantari, D

    2013-01-01

    Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ∼ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ∼ N δ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ < 1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime δ = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval. (paper)

  10. A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

    Directory of Open Access Journals (Sweden)

    H.H. Soliman

    2012-11-01

    Full Text Available An explosive growth in the field of wireless sensor networks (WSNs has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

  11. Interference mitigation for broadcast in hierarchical cell structure networks: Transmission strategy and area spectral efficiency

    KAUST Repository

    Yang, Yuli

    2014-10-01

    In this paper, a hierarchical cell structure (HCS) is considered, where an access point (AP) broadcasts to local nodes (LNs) over orthogonal frequency subbands within a local cell located in a macrocell. Since the local cell shares the spectrum licensed to the macrocell, a given LN is interfered with by the macrocell user (MU)\\'s transmissions over the same subband. To improve the performance of the AP\\'s broadcast service, a novel transmission strategy is proposed to mitigate the interference from the MU to the LN while achieving diversity gain. For the purpose of performance evaluation, the ergodic capacity of the proposed scheme is quantified, and the corresponding closed-form expression is obtained. By comparing with the traditional transmission scheme, which suffers from MU\\'s interference, illustrative numerical results substantiate that the proposed scheme achieves better performance than the traditional scheme as the MU-LN mean channel power gain is larger than half of the AP-LN mean channel power gain. Subsequently, we develop an optimized network design by maximizing the area spectral efficiency (ASE) of the AP\\'s broadcast in the local cell.

  12. Correlation between hierarchical structure of crystal networks and macroscopic performance of mesoscopic soft materials and engineering principles.

    Science.gov (United States)

    Lin, Naibo; Liu, Xiang Yang

    2015-11-07

    This review examines how the concepts and ideas of crystallization can be extended further and applied to the field of mesoscopic soft materials. It concerns the structural characteristics vs. the macroscopic performance, and the formation mechanism of crystal networks. Although this subject can be discussed in a broad sense across the area of mesoscopic soft materials, our main focus is on supramolecular materials, spider and silkworm silks, and biominerals. First, the occurrence of a hierarchical structure, i.e. crystal network and domain network structures, will facilitate the formation kinetics of mesoscopic phases and boost up the macroscopic performance of materials in some cases (i.e. spider silk fibres). Second, the structure and performance of materials can be correlated in some way by the four factors: topology, correlation length, symmetry/ordering, and strength of association of crystal networks. Moreover, four different kinetic paths of crystal network formation are identified, namely, one-step process of assembly, two-step process of assembly, mixed mode of assembly and foreign molecule mediated assembly. Based on the basic mechanisms of crystal nucleation and growth, the formation of crystal networks, such as crystallographic mismatch (or noncrystallographic) branching (tip branching and fibre side branching) and fibre/polymeric side merging, are reviewed. This facilitates the rational design and construction of crystal networks in supramolecular materials. In this context, the (re-)construction of a hierarchical crystal network structure can be implemented by thermal, precipitate, chemical, and sonication stimuli. As another important class of soft materials, the unusual mechanical performance of spider and silkworm silk fibres are reviewed in comparison with the regenerated silk protein derivatives. It follows that the considerably larger breaking stress and unusual breaking strain of spider silk fibres vs. silkworm silk fibres can be interpreted

  13. CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

    Science.gov (United States)

    Franke, R.

    2016-11-01

    In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

  14. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Li, Kangji [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China); School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013 (China); Su, Hongye [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China)

    2010-11-15

    There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel's daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy. (author)

  15. Transparent conducting films of hierarchically nanostructured polyaniline networks on flexible substrates for high-performance gas sensors.

    Science.gov (United States)

    Bai, Shouli; Sun, Chaozheng; Wan, Pengbo; Wang, Cheng; Luo, Ruixian; Li, Yaping; Liu, Junfeng; Sun, Xiaoming

    2015-01-21

    Transparent chemical gas sensors are assembled from a transparent conducting film of hierarchically nanostructured polyaniline (PANI) networks fabricated on a flexible PET substrate, by coating silver nanowires (Ag NWs) followed by the in situ polymerization of aniline near the sacrificial Ag NW template. The sensor exhibits enhanced gas sensing performance at room temperature in both sensitivity and selectivity to NH3 compared to pure PANI film. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. SDN‐Based Hierarchical Agglomerative Clustering Algorithm for Interference Mitigation in Ultra‐Dense Small Cell Networks

    Directory of Open Access Journals (Sweden)

    Guang Yang

    2018-04-01

    Full Text Available Ultra‐dense small cell networks (UD‐SCNs have been identified as a promising scheme for next‐generation wireless networks capable of meeting the ever‐increasing demand for higher transmission rates and better quality of service. However, UD‐SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software‐defined networking (SDN‐based hierarchical agglomerative clustering (SDN‐HAC framework, which leverages SDN to centrally control all sub‐channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non‐cooperative scenarios, respectively.

  17. A class of vertex-edge-growth small-world network models having scale-free, self-similar and hierarchical characters

    Science.gov (United States)

    Ma, Fei; Su, Jing; Hao, Yongxing; Yao, Bing; Yan, Guanghui

    2018-02-01

    The problem of uncovering the internal operating function of network models is intriguing, demanded and attractive in researches of complex networks. Notice that, in the past two decades, a great number of artificial models are built to try to answer the above mentioned task. Based on the different growth ways, these previous models can be divided into two categories, one type, possessing the preferential attachment, follows a power-law P(k) ∼k-γ, 2 motivated from a new attachment way, vertex-edge-growth network-operation, more precisely, the couple of both them. We report that this model is sparse, small world and hierarchical. And then, not only is scale-free feature in our model, but also lies the degree parameter γ(≈ 3 . 242) out the typical range. Note that, we suggest that the coexistence of multiple vertex growth ways will have a prominent effect on the power-law parameter γ, and the preferential attachment plays a dominate role on the development of networks over time. At the end of this paper, we obtain an exact analytical expression for the total number of spanning trees of models and also capture spanning trees entropy which we have compared with those of their corresponding component elements.

  18. Analytical reasoning task reveals limits of social learning in networks.

    Science.gov (United States)

    Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

    2014-04-06

    Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

  19. Bidirectional QoS support for novelty detection applications based on hierarchical wireless sensor network model

    Science.gov (United States)

    Edwards, Mark; Hu, Fei; Kumar, Sunil

    2004-10-01

    The research on the Novelty Detection System (NDS) (called as VENUS) at the authors' universities has generated exciting results. For example, we can detect an abnormal behavior (such as cars thefts from the parking lot) from a series of video frames based on the cognitively motivated theory of habituation. In this paper, we would like to describe the implementation strategies of lower layer protocols for using large-scale Wireless Sensor Networks (WSN) to NDS with Quality-of-Service (QoS) support. Wireless data collection framework, consisting of small and low-power sensor nodes, provides an alternative mechanism to observe the physical world, by using various types of sensing capabilities that include images (and even videos using Panoptos), sound and basic physical measurements such as temperature. We do not want to lose any 'data query command' packets (in the downstream direction: sink-to-sensors) or have any bit-errors in them since they are so important to the whole sensor network. In the upstream direction (sensors-to-sink), we may tolerate the loss of some sensing data packets. But the 'interested' sensing flow should be assigned a higher priority in terms of multi-hop path choice, network bandwidth allocation, and sensing data packet generation frequency (we hope to generate more sensing data packet for that novel event in the specified network area). The focus of this paper is to investigate MAC-level Quality of Service (QoS) issue in Wireless Sensor Networks (WSN) for Novelty Detection applications. Although QoS has been widely studied in other types of networks including wired Internet, general ad hoc networks and mobile cellular networks, we argue that QoS in WSN has its own characteristics. In wired Internet, the main QoS parameters include delay, jitter and bandwidth. In mobile cellular networks, two most common QoS metrics are: handoff call dropping probability and new call blocking probability. Since the main task of WSN is to detect and report

  20. Study on the correlation between the hierarchical urban system and high-speed railway network planning in China

    Directory of Open Access Journals (Sweden)

    Hong Sun

    2016-09-01

    Full Text Available This study examines the interrelatedness between the hierarchical structure of China׳s urban system and high-speed railway (HSR network planning at the national level. As a multi-layered system, the Chinese HSR can be categorized into three sub-networks, namely, the national HSR trunk network, the national HSR extensional network, and the intercity HSR network. By examining the direct HSR network connection, HSR nodal connection, and HSR operational frequency of 287 prefecture-level cities, this study demonstrates that the hierarchies of China׳s administrative, demographic, and economic urban systems strongly influence HSR network planning. The national HSR trunk network prioritizes the connection of top-level central cities, whereas the extensional network prioritizes cities at the lower level of the urban system. Moreover, the national HSR system forms the backbone of the HSR network structure based on a national scale, whereas the intercity HSR system satisfies the travel needs within urban agglomerations based on the regional level.

  1. On Hierarchical Extensions of Large-Scale 4-regular Grid Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Patel, A.; Knudsen, Thomas Phillip

    2004-01-01

    dependencies between the number of nodes and the distances in the structures. The perfect square mesh is introduced for hierarchies, and it is shown that applying ordered hierarchies in this way results in logarithmic dependencies between the number of nodes and the distances, resulting in better scaling...... structures. For example, in a mesh of 391876 nodes the average distance is reduced from 417.33 to 17.32 by adding hierarchical lines. This is gained by increasing the number of lines by 4.20% compared to the non-hierarchical structure. A similar hierarchical extension of the torus structure also results...

  2. Interference mitigation for broadcast in hierarchical cell structure networks: Transmission strategy and area spectral efficiency

    KAUST Repository

    Yang, Yuli; Salama, Khaled N.; Aï ssa, Sonia

    2014-01-01

    In this paper, a hierarchical cell structure (HCS) is considered, where an access point (AP) broadcasts to local nodes (LNs) over orthogonal frequency subbands within a local cell located in a macrocell. Since the local cell shares the spectrum

  3. Intrinsic and task-evoked network architectures of the human brain

    Science.gov (United States)

    Cole, Michael W.; Bassett, Danielle S.; Power, Jonathan D.; Braver, Todd S.; Petersen, Steven E.

    2014-01-01

    Summary Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic”, standard architecture of functional brain organization. Further, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity – areas of neuroscientific inquiry typically considered separately. PMID:24991964

  4. Hierarchical Neural Network (HNN) for Closed Loop Decision Making: Designing the Architecture of a Hierarchical Neural Network to Model Attention, Learning and Goal Oriented Behavior

    Science.gov (United States)

    1990-12-01

    other useful tasks. Simulation results of a 2 degrees of freedom (DOF) manipulator are given. Rigid Robot Dinamics The Lagrange-Euler formulation of...cells. In distributed models, the strength of patterns of activity over many units determines the degree of participation of these entities in functional

  5. Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.

    Science.gov (United States)

    Mohammadzadeh, Ardashir; Ghaemi, Sehraneh

    2015-09-01

    This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks

    Energy Technology Data Exchange (ETDEWEB)

    Deng, De-Ming; Chang, Cheng-Hung [Institute of Physics, National Chiao Tung University, Hsinchu 300, Taiwan (China)

    2015-05-14

    Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

  7. Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks.

    Science.gov (United States)

    Deng, De-Ming; Chang, Cheng-Hung

    2015-05-14

    Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

  8. On Energy-Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Poor HVincent

    2007-01-01

    Full Text Available A hierarchical cross-layer design approach is proposed to increase energy efficiency in ad hoc networks through joint adaptation of nodes' transmitting powers and route selection. The design maintains the advantages of the classic OSI model, while accounting for the cross-coupling between layers, through information sharing. The proposed joint power control and routing algorithm is shown to increase significantly the overall energy efficiency of the network, at the expense of a moderate increase in complexity. Performance enhancement of the joint design using multiuser detection is also investigated, and it is shown that the use of multiuser detection can increase the capacity of the ad hoc network significantly for a given level of energy consumption.

  9. On Energy-Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Cristina Comaniciu

    2007-03-01

    Full Text Available A hierarchical cross-layer design approach is proposed to increase energy efficiency in ad hoc networks through joint adaptation of nodes' transmitting powers and route selection. The design maintains the advantages of the classic OSI model, while accounting for the cross-coupling between layers, through information sharing. The proposed joint power control and routing algorithm is shown to increase significantly the overall energy efficiency of the network, at the expense of a moderate increase in complexity. Performance enhancement of the joint design using multiuser detection is also investigated, and it is shown that the use of multiuser detection can increase the capacity of the ad hoc network significantly for a given level of energy consumption.

  10. Modeling of frequency agile devices: development of PKI neuromodeling library based on hierarchical network structure

    Science.gov (United States)

    Sanchez, P.; Hinojosa, J.; Ruiz, R.

    2005-06-01

    Recently, neuromodeling methods of microwave devices have been developed. These methods are suitable for the model generation of novel devices. They allow fast and accurate simulations and optimizations. However, the development of libraries makes these methods to be a formidable task, since they require massive input-output data provided by an electromagnetic simulator or measurements and repeated artificial neural network (ANN) training. This paper presents a strategy reducing the cost of library development with the advantages of the neuromodeling methods: high accuracy, large range of geometrical and material parameters and reduced CPU time. The library models are developed from a set of base prior knowledge input (PKI) models, which take into account the characteristics common to all the models in the library, and high-level ANNs which give the library model outputs from base PKI models. This technique is illustrated for a microwave multiconductor tunable phase shifter using anisotropic substrates. Closed-form relationships have been developed and are presented in this paper. The results show good agreement with the expected ones.

  11. Task vs. rest-different network configurations between the coactivation and the resting-state brain networks.

    Science.gov (United States)

    Di, Xin; Gohel, Suril; Kim, Eun H; Biswal, Bharat B

    2013-01-01

    There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

  12. Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

    Science.gov (United States)

    Di, Xin; Gohel, Suril; Kim, Eun H.; Biswal, Bharat B.

    2013-01-01

    There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest. PMID:24062654

  13. Mapping the Hierarchical Layout of the Structural Network of the Macaque Prefrontal Cortex

    NARCIS (Netherlands)

    Goulas, A.; Uylings, H.B.M.; Stiers, P.

    2014-01-01

    A consensus on the prefrontal cortex (PFC) holds that it is pivotal for flexible behavior and the integration of the cognitive, affective, and motivational domains. Certain models have been put forth and a dominant model postulates a hierarchical anterior-posterior gradient. The structural

  14. Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

    Directory of Open Access Journals (Sweden)

    Zhensheng Wang

    2017-02-01

    Full Text Available The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

  15. Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance?

    Science.gov (United States)

    Čeko, Marta; Gracely, John L; Fitzcharles, Mary-Ann; Seminowicz, David A; Schweinhardt, Petra; Bushnell, M Catherine

    2015-08-19

    In studies of cognitive processing using tasks with externally directed attention, regions showing increased (external-task-positive) and decreased or "negative" [default-mode network (DMN)] fMRI responses during task performance are dynamically responsive to increasing task difficulty. Responsiveness (modulation of fMRI signal by increasing load) has been linked directly to successful cognitive task performance in external-task-positive regions but not in DMN regions. To investigate whether a responsive DMN is required for successful cognitive performance, we compared healthy human subjects (n = 23) with individuals shown to have decreased DMN engagement (chronic pain patients, n = 28). Subjects performed a multilevel working-memory task (N-back) during fMRI. If a responsive DMN is required for successful performance, patients having reduced DMN responsiveness should show worsened performance; if performance is not reduced, their brains should show compensatory activation in external-task-positive regions or elsewhere. All subjects showed decreased accuracy and increased reaction times with increasing task level, with no significant group differences on either measure at any level. Patients had significantly reduced negative fMRI response (deactivation) of DMN regions (posterior cingulate/precuneus, medial prefrontal cortex). Controls showed expected modulation of DMN deactivation with increasing task difficulty. Patients showed significantly reduced modulation of DMN deactivation by task difficulty, despite their successful task performance. We found no evidence of compensatory neural recruitment in external-task-positive regions or elsewhere. Individual responsiveness of the external-task-positive ventrolateral prefrontal cortex, but not of DMN regions, correlated with task accuracy. These findings suggest that a responsive DMN may not be required for successful cognitive performance; a responsive external-task-positive network may be sufficient. We studied the

  16. Hierarchical spatial segregation of two Mediterranean vole species: the role of patch-network structure and matrix composition.

    Science.gov (United States)

    Pita, Ricardo; Lambin, Xavier; Mira, António; Beja, Pedro

    2016-09-01

    According to ecological theory, the coexistence of competitors in patchy environments may be facilitated by hierarchical spatial segregation along axes of environmental variation, but empirical evidence is limited. Cabrera and water voles show a metapopulation-like structure in Mediterranean farmland, where they are known to segregate along space, habitat, and time axes within habitat patches. Here, we assess whether segregation also occurs among and within landscapes, and how this is influenced by patch-network and matrix composition. We surveyed 75 landscapes, each covering 78 ha, where we mapped all habitat patches potentially suitable for Cabrera and water voles, and the area effectively occupied by each species (extent of occupancy). The relatively large water vole tended to be the sole occupant of landscapes with high habitat amount but relatively low patch density (i.e., with a few large patches), and with a predominantly agricultural matrix, whereas landscapes with high patch density (i.e., many small patches) and low agricultural cover, tended to be occupied exclusively by the small Cabrera vole. The two species tended to co-occur in landscapes with intermediate patch-network and matrix characteristics, though their extents of occurrence were negatively correlated after controlling for environmental effects. In combination with our previous studies on the Cabrera-water vole system, these findings illustrated empirically the occurrence of hierarchical spatial segregation, ranging from within-patches to among-landscapes. Overall, our study suggests that recognizing the hierarchical nature of spatial segregation patterns and their major environmental drivers should enhance our understanding of species coexistence in patchy environments.

  17. Activity flow over resting-state networks shapes cognitive task activations.

    Science.gov (United States)

    Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

    2016-12-01

    Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

  18. Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance?

    Science.gov (United States)

    Čeko, Marta; Gracely, John L.; Fitzcharles, Mary-Ann; Seminowicz, David A.; Schweinhardt, Petra

    2015-01-01

    In studies of cognitive processing using tasks with externally directed attention, regions showing increased (external-task-positive) and decreased or “negative” [default-mode network (DMN)] fMRI responses during task performance are dynamically responsive to increasing task difficulty. Responsiveness (modulation of fMRI signal by increasing load) has been linked directly to successful cognitive task performance in external-task-positive regions but not in DMN regions. To investigate whether a responsive DMN is required for successful cognitive performance, we compared healthy human subjects (n = 23) with individuals shown to have decreased DMN engagement (chronic pain patients, n = 28). Subjects performed a multilevel working-memory task (N-back) during fMRI. If a responsive DMN is required for successful performance, patients having reduced DMN responsiveness should show worsened performance; if performance is not reduced, their brains should show compensatory activation in external-task-positive regions or elsewhere. All subjects showed decreased accuracy and increased reaction times with increasing task level, with no significant group differences on either measure at any level. Patients had significantly reduced negative fMRI response (deactivation) of DMN regions (posterior cingulate/precuneus, medial prefrontal cortex). Controls showed expected modulation of DMN deactivation with increasing task difficulty. Patients showed significantly reduced modulation of DMN deactivation by task difficulty, despite their successful task performance. We found no evidence of compensatory neural recruitment in external-task-positive regions or elsewhere. Individual responsiveness of the external-task-positive ventrolateral prefrontal cortex, but not of DMN regions, correlated with task accuracy. These findings suggest that a responsive DMN may not be required for successful cognitive performance; a responsive external-task-positive network may be sufficient

  19. Hierarchical and Matrix Structures in a Large Organizational Email Network: Visualization and Modeling Approaches

    OpenAIRE

    Sims, Benjamin H.; Sinitsyn, Nikolai; Eidenbenz, Stephan J.

    2014-01-01

    This paper presents findings from a study of the email network of a large scientific research organization, focusing on methods for visualizing and modeling organizational hierarchies within large, complex network datasets. In the first part of the paper, we find that visualization and interpretation of complex organizational network data is facilitated by integration of network data with information on formal organizational divisions and levels. By aggregating and visualizing email traffic b...

  20. Mobile Sensor Networks for Inspection Tasks in Harsh Industrial Environments

    NARCIS (Netherlands)

    Mulder, Jacob; Wang, Xinyu; Ferwerda, Franke; Cao, Ming

    Recent advances in sensor technology have enabled the fast development of mobile sensor networks operating in various unknown and sometimes hazardous environments. In this paper, we introduce one integrative approach to design, analyze and test distributed control algorithms to coordinate a network

  1. Persistency and flexibility of complex brain networks underlie dual-task interference.

    Science.gov (United States)

    Alavash, Mohsen; Hilgetag, Claus C; Thiel, Christiane M; Gießing, Carsten

    2015-09-01

    Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules. © 2015 Wiley

  2. Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients.

    Directory of Open Access Journals (Sweden)

    Da-Hye Kim

    Full Text Available Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal

  3. A Novel Connectionist Network for Solving Long Time-Lag Prediction Tasks

    Science.gov (United States)

    Johnson, Keith; MacNish, Cara

    Traditional Recurrent Neural Networks (RNNs) perform poorly on learning tasks involving long time-lag dependencies. More recent approaches such as LSTM and its variants significantly improve on RNNs ability to learn this type of problem. We present an alternative approach to encoding temporal dependencies that associates temporal features with nodes rather than state values, where the nodes explicitly encode dependencies over variable time delays. We show promising results comparing the network's performance to LSTM variants on an extended Reber grammar task.

  4. 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.

  5. Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

    Science.gov (United States)

    Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

    2016-08-01

    Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

  6. Sustained oscillations, irregular firing and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types

    Directory of Open Access Journals (Sweden)

    Petar eTomov

    2014-09-01

    Full Text Available The cerebral cortex exhibits neural activity even in the absence of externalstimuli. This self-sustained activity is characterized by irregular firing ofindividual neurons and population oscillations with a broad frequency range.Questions that arise in this context, are: What are the mechanismsresponsible for the existence of neuronal spiking activity in the cortexwithout external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend onintrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composedof combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS, chattering (CH, intrinsically bursting (IB, low threshold spiking (LTS and fast spiking (FS. The population of excitatory neurons is built of RS cells(always present and either CH or IB cells. Inhibitoryneurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our networksimulations display irregular single neuron firing and oscillatoryactivity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions,suggesting a transient chaotic regime. Extensive analysis of the self-sustainedactivity states showed that their lifetime expectancy increases with the numberof network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

  7. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Xiaowei Li

    2017-01-01

    Full Text Available A large number of studies demonstrated that major depressive disorder (MDD is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

  8. Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

    Science.gov (United States)

    Malekmohammadi, Bahram; Tayebzadeh Moghadam, Negar

    2018-04-13

    Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.

  9. Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network

    Directory of Open Access Journals (Sweden)

    Silvia Tommasin

    2017-07-01

    Full Text Available Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN, are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task.

  10. Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network

    Science.gov (United States)

    Tommasin, Silvia; Mascali, Daniele; Gili, Tommaso; Assan, Ibrahim Eid; Moraschi, Marta; Fratini, Michela; Wise, Richard G.; Macaluso, Emiliano; Mangia, Silvia; Giove, Federico

    2017-01-01

    Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task. PMID:28845420

  11. Task-related modulations of BOLD low-frequency fluctuations within the default mode network

    Science.gov (United States)

    Tommasin, Silvia; Mascali, Daniele; Gili, Tommaso; Eid Assan, Ibrahim; Moraschi, Marta; Fratini, Michela; Wise, Richard G.; Macaluso, Emiliano; Mangia, Silvia; Giove, Federico

    2017-07-01

    Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33±6 years, 8F/12M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the steady-state execution of a sustained working memory n-back task. We found that the steady state execution of such a task impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to steady-state task execution, can contribute to a better understanding of how brain networks rearrange themselves in response of a task.

  12. Distracted in a Demanding Task : A Classification Study with Artificial Neural Networks

    NARCIS (Netherlands)

    Huijser, Stefan; Taatgen, Niels; van Vugt, Marieke; Verheij, Bart; Wiering, Marco

    An important issue in cognitive science research is to know what your subjects are thinking about. In this paper, we trained multiple artificial Neural Network (ANN) classifiers to predict whether subjects’ thoughts were focused on the task (i.e., on-task) or if they were distracted (i.e.,

  13. Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

    OpenAIRE

    Lehký , David; Slowik , Ondřej; Novák , Drahomír

    2014-01-01

    Part 7: Genetic Algorithms; International audience; The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficie...

  14. Same task, different strategies: How brain networks can be influenced by memory strategy

    OpenAIRE

    Sanfratello, Lori; Caprihan, Arvind; Stephen, Julia M.; Knoefel, Janice E.; Adair, John C.; Qualls, Clifford; Lundy, S. Laura; Aine, Cheryl J.

    2014-01-01

    Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a working memory task is defined as verbal or spatial, different types of memory strategies may be employed to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type ...

  15. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

    Directory of Open Access Journals (Sweden)

    Parvin Jeffrey

    2010-12-01

    Full Text Available Abstract Background Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. Results In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2 were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM. A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes. We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes and targeted TFs (25% of common TFs. The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. Conclusions Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to

  16. RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks

    NARCIS (Netherlands)

    Labraoui, Nabila; Gueroui, Mourad; Aliouat, Makhlouf; Petit, Jonathan

    2011-01-01

    In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge

  17. Brain networks for confidence weighting and hierarchical inference during probabilistic learning.

    Science.gov (United States)

    Meyniel, Florent; Dehaene, Stanislas

    2017-05-09

    Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.

  18. Brain networks for confidence weighting and hierarchical inference during probabilistic learning

    Science.gov (United States)

    Meyniel, Florent; Dehaene, Stanislas

    2017-01-01

    Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This “confidence weighting” implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain’s learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences. PMID:28439014

  19. Two-user opportunistic scheduling using hierarchical modulations in wireless networks with heterogenous average link gains

    KAUST Repository

    Hossain, Md Jahangir

    2010-03-01

    Our contribution, in this paper, is two-fold. First, we analyze the performance of a hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) scheme, which was proposed by the authors, in a fading environment where different users have different average link gains. Specifically, we present a new expression for the spectral efficiency (SE) of the users and using this expression, we compare the degrees of fairness (DOF) of the TBS scheme with that of classical single user opportunistic scheduling schemes, namely, absolute carrier-to-noise ratio (CNR) based single-best user scheduling (SBS) and normalized CNR based proportional fair scheduling (PFS) schemes. The second contribution is that we propose a new hybrid two-user opportunistic scheduling (HTS) scheme based on our earlier proposed TBS scheme. This HTS scheme selects the first user based on the largest absolute CNR value among all the users while the second user is selected based on the ratios of the absolute CNRs to the corresponding average CNRs of the remaining users. The total transmission rate i.e., the constellation size is selected according to the absolute CNR of the first best user. The total transmission rate is then allocated among these selected users by joint consideration of their absolute CNRs and allocated number of information bit(s) are transmitted to them using hierarchical modulations. Numerical results are presented for a fading environment where different users experience independent but non-identical (i.n.d.) channel fading. These selected numerical results show that the proposed HTS scheme can considerably increase the system\\'s fairness without any degradation of the link spectral efficiency (LSE) i.e., the multiuser diversity gain compared to the classical SBS scheme. These results also show that the proposed HTS scheme has a lower fairness in comparison to the PFS scheme which suffers from a considerable degradation in LSE. © 2010 IEEE.

  20. Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

    Science.gov (United States)

    Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S

    2017-03-08

    Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging. SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across

  1. Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting

    Science.gov (United States)

    Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.

    2016-01-01

    Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048

  2. Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

    Science.gov (United States)

    Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

    2017-02-01

    Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

  3. Ischemia Detection Using Supervised Learning for Hierarchical Neural Networks Based on Kohonen-Maps

    National Research Council Canada - National Science Library

    Vladutu, L

    2001-01-01

    .... The motivation for developing the Supervising Network - Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications...

  4. A data management proposal to connect in a hierarchical way nodes of the Spanish Long Term Ecological Research (LTER) network

    Science.gov (United States)

    Fuentes, Daniel; Pérez-Luque, Antonio J.; Bonet García, Francisco J.; Moreno-LLorca, Ricardo A.; Sánchez-Cano, Francisco M.; Suárez-Muñoz, María

    2017-04-01

    The Long Term Ecological Research (LTER) network aims to provide the scientific community, policy makers, and society with the knowledge and predictive understanding necessary to conserve, protect, and manage the ecosystems. LTER is organized into networks ranging from the global to national scale. In the top of network, the International Long Term Ecological Research (ILTER) Network coordinates among ecological researchers and LTER research networks at local, regional and global scales. In Spain, the Spanish Long Term Ecological Research (LTER-Spain) network was built to foster the collaboration and coordination between longest-lived ecological researchers and networks on a local scale. Currently composed by nine nodes, this network facilitates the data exchange, documentation and preservation encouraging the development of cross-disciplinary works. However, most nodes have no specific information systems, tools or qualified personnel to manage their data for continued conservation and there are no harmonized methodologies for long-term monitoring protocols. Hence, the main challenge is to place the nodes in its correct position in the network, providing the best tools that allow them to manage their data autonomously and make it easier for them to access information and knowledge in the network. This work proposes a connected structure composed by four LTER nodes located in southern Spain. The structure is built considering hierarchical approach: nodes that create information which is documented using metadata standards (such as Ecological Metadata Language, EML); and others nodes that gather metadata and information. We also take into account the capacity of each node to manage their own data and the premise that the data and metadata must be maintained where it is generated. The current state of the nodes is a follows: two of them have their own information management system (Sierra Nevada-Granada and Doñana Long-Term Socio-ecological Research Platform) and

  5. IPTV traffic management using topology-based hierarchical scheduling in Carrier Ethernet transport networks

    DEFF Research Database (Denmark)

    Yu, Hao; Yan, Ying; Berger, Michael Stubert

    2009-01-01

    Carrier Ethernet is becoming a favorable access technology for Next Generation Network (NGN). The features of cost-efficiency, operation flexibility and high bandwidth have a great attraction to service providers. However, to achieve these characteristics, Carrier Ethernet needs to have Quality o....... This work has been carried out as a part of the research project HIPT (High quality IP network for IPTV and VoIP) founded by Danish Advanced Technology Foundation....

  6. Swarm intelligence techniques for optimization and management tasks insensor networks

    OpenAIRE

    Hernández Pibernat, Hugo

    2012-01-01

    Premi extraordinari doctorat curs 2011-2012, àmbit Enginyeria de les TIC The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted...

  7. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  8. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  9. Bioactive glass-poly (ε-caprolactone) composite scaffolds with 3 dimensionally hierarchical pore networks

    International Nuclear Information System (INIS)

    Yun, Hui-suk; Kim, Seung-eon; Park, Eui Kyun

    2011-01-01

    Hierarchically mesoporous-macroporous-giant-porous bioactive glass/poly ε-caprolactone (PCL) composite scaffolds were prepared using a combination of the sol-gel method, evaporation-induced self-assembly process in the presence of nonionic triblock copolymer, EO 100 PO 65 EO 100 (F127), as template, salt leaching method, and rapid prototyping techniques. F127 acts as a template, inducing the formation of mesopores, NaCl with sizes between 25 and 33 μm provides macro-pores after leaching, and rapid prototyping produces giant-pores. The structure and morphology of the scaffolds were characterized by the field emission scanning electron microscopy, transmission electron microscopy, and Hg porosimetry. The mechanical properties of the scaffolds were examined by the dynamic mechanical analysis. Their in vitro bioactivities were confirmed by immersing the scaffolds in simulated body fluid. Their biocompatibilities were also evaluated by culturing human bone marrow stromal cells on the scaffolds. The scaffolds show good molding capabilities, mechanical properties, 3 dimensionally well-interconnected pore structures, bioactivities, and biocompatibilities in vitro. Depending on the amount of NaCl, the scaffolds also show unique sponge-like properties, but still retain better mechanical properties than general salt leaching derived PCL scaffolds. All of the data provide good evidence that the obtained scaffolds possess excellent potential for applications in the fields of tissue engineering and drug storage.

  10. Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network

    Directory of Open Access Journals (Sweden)

    Yueyue Deng

    2013-01-01

    Full Text Available Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Both the LAAF and GMOOP algorithms are well suited in poor acoustic network condition and dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind flooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than the well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path-planning technique provides an efficient method for executing multiobjective tasks by cooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods that are limited to networks where constant, reliable communication is assumed to be available.

  11. Evaluation of options for energy recovery from municipal solid waste in India using the hierarchical analytical network process

    International Nuclear Information System (INIS)

    Nixon, J.D.; Dey, P.K.; Ghosh, S.K.; Davies, P.A.

    2013-01-01

    In this paper a Hierarchical Analytical Network Process (HANP) model is demonstrated for evaluating alternative technologies for generating electricity from MSW in India. The technological alternatives and evaluation criteria for the HANP study are characterised by reviewing the literature and consulting experts in the field of waste management. Technologies reviewed in the context of India include landfill, anaerobic digestion, incineration, pelletisation and gasification. To investigate the sensitivity of the result, we examine variations in expert opinions and carry out an Analytical Hierarchy Process (AHP) analysis for comparison. We find that anaerobic digestion is the preferred technology for generating electricity from MSW in India. Gasification is indicated as the preferred technology in an AHP model due to the exclusion of criteria dependencies and in an HANP analysis when placing a high priority on net output and retention time. We conclude that HANP successfully provides a structured framework for recommending which technologies to pursue in India, and the adoption of such tools is critical at a time when key investments in infrastructure are being made. Therefore the presented methodology is thought to have a wider potential for investors, policy makers, researchers and plant developers in India and elsewhere. - Highlights: • We evaluate alternative technologies for generating electricity from waste. • The methodology we develop is based on the analytical network process. • Assessment is made against technical, financial, environmental and risk criteria. • Anaerobic digestion and gasification are the preferred technologies for India. • The method reduces risk and ensures sustainability in energy project planning

  12. Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

    Science.gov (United States)

    Thye, Melissa D; Ammons, Carla J; Murdaugh, Donna L; Kana, Rajesh K

    2018-07-16

    Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind (ToM). However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization. Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis (ICA), is a data-driven technique used to isolate intrinsic connectivity networks, and this approach provides insight into network-level regional recruitment. In this fMRI study, three complementary, but distinct ToM tasks assessing mental state detection (e.g. RMIE: Reading the Mind in the Eyes; RMIV: Reading the Mind in the Voice) and intentional attribution (Causality task) were each analyzed using ICA in order to separately characterize the recruitment and functional connectivity of core nodes in the ToM network in response to the sub-processes of ToM. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions. The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing. Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated

  13. Meditation leads to reduced default mode network activity beyond an active task.

    Science.gov (United States)

    Garrison, Kathleen A; Zeffiro, Thomas A; Scheinost, Dustin; Constable, R Todd; Brewer, Judson A

    2015-09-01

    Meditation has been associated with relatively reduced activity in the default mode network, a brain network implicated in self-related thinking and mind wandering. However, previous imaging studies have typically compared meditation to rest, despite other studies having reported differences in brain activation patterns between meditators and controls at rest. Moreover, rest is associated with a range of brain activation patterns across individuals that has only recently begun to be better characterized. Therefore, in this study we compared meditation to another active cognitive task, both to replicate the findings that meditation is associated with relatively reduced default mode network activity and to extend these findings by testing whether default mode activity was reduced during meditation, beyond the typical reductions observed during effortful tasks. In addition, prior studies had used small groups, whereas in the present study we tested these hypotheses in a larger group. The results indicated that meditation is associated with reduced activations in the default mode network, relative to an active task, for meditators as compared to controls. Regions of the default mode network showing a Group × Task interaction included the posterior cingulate/precuneus and anterior cingulate cortex. These findings replicate and extend prior work indicating that the suppression of default mode processing may represent a central neural process in long-term meditation, and they suggest that meditation leads to relatively reduced default mode processing beyond that observed during another active cognitive task.

  14. Efficient Utilization of Hierarchical iJTAG Networks for Interrupts Management

    NARCIS (Netherlands)

    Ibrahim, Ahmed Mohammed Youssef; Kerkhoff, Hans G.

    2016-01-01

    Modern systems-on-chips rely on embedded instruments for testing and debugging, the same instruments could be used for managing the lifetime dependability of the chips. The IEEE 1687 (iJTAG) standard introduces an access network to the instruments based on reconfigurable scan paths. During lifetime,

  15. A Novel Hierarchical Semi-centralized Telemedicine Network Architecture Proposition for Bangladesh

    DEFF Research Database (Denmark)

    Choudhury, Samiul; Peterson, Carrie Beth; Kyriazakos, Sofoklis

    2011-01-01

    . The model utilizes the existing fiber optic backbone and wireless telecommunication infrastructures to connect the remote healthcare centers with the urban special-ized hospitals. The proposed network is of low cost, flexible and faster as well as more concrete than the existing healthcare organogram...

  16. A policy-based hierarchical approach for management of grids and networks

    NARCIS (Netherlands)

    Fioreze, Tiago; Neisse, R.; Granville, L.; Almeida, M.J.; Pras, Aiko

    2006-01-01

    Grids are distributed infrastructures that have been used as an important and powerful resource for distributed computing. Since the nodes of a grid can potentially be located in different administrative domains, the underlying network infrastructure that supports grid communications has to be

  17. Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network

    Science.gov (United States)

    Muhammad, Umar B.; Ezugwu, Absalom E.; Ofem, Paulinus O.; Rajamäki, Jyri; Aderemi, Adewumi O.

    2017-06-01

    Recently, researchers in the field of wireless sensor networks have resorted to energy harvesting techniques that allows energy to be harvested from the ambient environment to power sensor nodes. Using such Energy harvesting techniques together with proper routing protocols, an Energy Neutral state can be achieved so that sensor nodes can run perpetually. In this paper, we propose an Energy Neutral LEACH routing protocol which is an extension to the traditional LEACH protocol. The goal of the proposed protocol is to use Gateway node in each cluster so as to reduce the data transmission ranges of cluster head nodes. Simulation results show that the proposed routing protocol achieves a higher throughput and ensure the energy neutral status of the entire network.

  18. The Global Invasive Species Information Network: contributing to GEO Task BI-07-01b

    Science.gov (United States)

    Graham, J.; Morisette, J. T.; Simpson, A.

    2009-12-01

    Invasive alien species (IAS) threaten biodiversity and exert a tremendous cost on society for IAS prevention and eradication. They endanger natural ecosystem functioning and seriously impact biodiversity and agricultural production. The task definition for the GEO task BI-07-01b: Invasive Species Monitoring System is to characterize, monitor, and predict changes in the distribution of invasive species. This includes characterizing the current requirements and capacity for invasive species monitoring and developing strategies for implementing cross-search functionality among existing online invasive species information systems from around the globe. The Task is being coordinated by members of the Global Invasive Species Information Network (GISIN) and their partners. Information on GISIN and a prototype of the network is available at www.gisin.org. This talk will report on the current status of GISIN and review how researchers can either contribute to or utilize data from this network.

  19. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

    Science.gov (United States)

    Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

  20. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

    Science.gov (United States)

    Born, Jannis; Stringer, Simon M.

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

  1. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

    Directory of Open Access Journals (Sweden)

    Jannis Born

    Full Text Available A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior

  2. Non-Linear Behaviour Of Gelatin Networks Reveals A Hierarchical Structure

    KAUST Repository

    Yang, Zhi; Hemar, Yacine; Hilliou, loic; Gilbert, Elliot P.; McGillivray, Duncan James; Williams, Martin A. K.; Chaieb, Saharoui

    2015-01-01

    We investigate the strain hardening behaviour of various gelatin networks - namely physically-crosslinked gelatin gel, chemically-crosslinked gelatin gels, and a hybrid gels made of a combination of the former two - under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillation shear protocols. Further, the internal structures of physically-crosslinked gelatin gel and chemically-crosslinked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically-crosslinked network, whereas in the physically-crosslinked gels a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as correlation length (ξ), cross-sectional polymer chain radius (Rc), and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physically-crosslinked and chemically crosslinked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized non-linear elastic theory we used to fit our stress-strain curves. The chemical crosslinking that generates coils and aggregates hinders the free stretching of the triple helices bundles in the physically-crosslinked gels.

  3. Admission Control for Multiservices Traffic in Hierarchical Mobile IPv6 Networks by Using Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Jung-Shyr Wu

    2012-01-01

    Full Text Available CAC (Call Admission Control plays a significant role in providing QoS (Quality of Service in mobile wireless networks. In addition to much research that focuses on modified Mobile IP to get better efficient handover performance, CAC should be introduced to Mobile IP-based network to guarantee the QoS for users. In this paper, we propose a CAC scheme which incorporates multiple traffic types and adjusts the admission threshold dynamically using fuzzy control logic to achieve better usage of resources. The method can provide QoS in Mobile IPv6 networks with few modifications on MAP (Mobility Anchor Point functionality and slight change in BU (Binding Update message formats. According to the simulation results, the proposed scheme presents good performance of voice and video traffic at the expenses of poor performance on data traffic. It is evident that these CAC schemes can reduce the probability of the handoff dropping and the cell overload and limit the probability of the new call blocking.

  4. Non-Linear Behaviour Of Gelatin Networks Reveals A Hierarchical Structure

    KAUST Repository

    Yang, Zhi

    2015-12-14

    We investigate the strain hardening behaviour of various gelatin networks - namely physically-crosslinked gelatin gel, chemically-crosslinked gelatin gels, and a hybrid gels made of a combination of the former two - under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillation shear protocols. Further, the internal structures of physically-crosslinked gelatin gel and chemically-crosslinked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically-crosslinked network, whereas in the physically-crosslinked gels a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as correlation length (ξ), cross-sectional polymer chain radius (Rc), and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physically-crosslinked and chemically crosslinked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized non-linear elastic theory we used to fit our stress-strain curves. The chemical crosslinking that generates coils and aggregates hinders the free stretching of the triple helices bundles in the physically-crosslinked gels.

  5. Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

    Science.gov (United States)

    Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

    2017-01-15

    Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the

  6. Relevant problems in collaborative processes of non-hierarchical manufacturing networks

    Directory of Open Access Journals (Sweden)

    Beatriz Andrés

    2013-07-01

    Full Text Available Purpose: The purpose of this paper is to identify some of existing problems associated with collaboration among SMEs of the same network. Concretely, the problems are focused. The research objective is to identify the most relevant problems existing when SMEs have to deal with decentralized decisions (DDM. Design/methodology/approach: Through the literature review there have been collected collaborative problems caused by inter-organizational barriers. The approach taken is a qualitative study and analysis that classifies collaborative problems from less important to very important. In light of this, we are able to identify what are the most relevant problems to study in the NHN collaborative context. Findings and Originality/value: The developed methodology allows researchers to indentify amongst the collaborative problems those that are most relevant to solve in the NHN context, with the main aim of providing solutions in the future. The research aim is to provide the expert in the collaborative field a starting point to address the collaborative problems SMEs can find when belonging to collaborative networks. Research limitations/implications: Not all the problems that appear when an SME establish collaborative relationships, in a NHN, are considered. The identified problems have been arisen because there are discussed in the literature for addressing collaborative problems among networked partners. Identified problems are also considered because there are relevant to achieve collaboration among SMEs. Originality/value: The degree of coverage and the degree of significance is the taxonomy criteria used to identify the importance of solution degree of the encountered collaborative problems, in NHN context, in order to provide a future research of solutions to overcome them.

  7. Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

    Directory of Open Access Journals (Sweden)

    Klaus Moessner

    2013-10-01

    Full Text Available This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

  8. Reward-based training of recurrent neural networks for cognitive and value-based tasks.

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2017-01-13

    Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.

  9. Self-powered information measuring wireless networks using the distribution of tasks within multicore processors

    Science.gov (United States)

    Zhuravska, Iryna M.; Koretska, Oleksandra O.; Musiyenko, Maksym P.; Surtel, Wojciech; Assembay, Azat; Kovalev, Vladimir; Tleshova, Akmaral

    2017-08-01

    The article contains basic approaches to develop the self-powered information measuring wireless networks (SPIM-WN) using the distribution of tasks within multicore processors critical applying based on the interaction of movable components - as in the direction of data transmission as wireless transfer of energy coming from polymetric sensors. Base mathematic model of scheduling tasks within multiprocessor systems was modernized to schedule and allocate tasks between cores of one-crystal computer (SoC) to increase energy efficiency SPIM-WN objects.

  10. Enhanced Deployment Strategy for Role-based Hierarchical Application Agents in Wireless Sensor Networks with Established Clusterheads

    Science.gov (United States)

    Gendreau, Audrey

    Efficient self-organizing virtual clusterheads that supervise data collection based on their wireless connectivity, risk, and overhead costs, are an important element of Wireless Sensor Networks (WSNs). This function is especially critical during deployment when system resources are allocated to a subsequent application. In the presented research, a model used to deploy intrusion detection capability on a Local Area Network (LAN), in the literature, was extended to develop a role-based hierarchical agent deployment algorithm for a WSN. The resulting model took into consideration the monitoring capability, risk, deployment distribution cost, and monitoring cost associated with each node. Changing the original LAN methodology approach to model a cluster-based sensor network depended on the ability to duplicate a specific parameter that represented the monitoring capability. Furthermore, other parameters derived from a LAN can elevate costs and risk of deployment, as well as jeopardize the success of an application on a WSN. A key component of the approach presented in this research was to reduce the costs when established clusterheads in the network were found to be capable of hosting additional detection agents. In addition, another cost savings component of the study addressed the reduction of vulnerabilities associated with deployment of agents to high volume nodes. The effectiveness of the presented method was validated by comparing it against a type of a power-based scheme that used each node's remaining energy as the deployment value. While available energy is directly related to the model used in the presented method, the study deliberately sought out nodes that were identified with having superior monitoring capability, cost less to create and sustain, and are at low-risk of an attack. This work investigated improving the efficiency of an intrusion detection system (IDS) by using the proposed model to deploy monitoring agents after a temperature sensing

  11. Hierarchical tree-structured control network for the Antares laser facility

    Energy Technology Data Exchange (ETDEWEB)

    McGirt, F.

    1979-01-01

    The design and implementation of a distributed, computer-based control system for the Antares 100-kJ gas laser fusion facility is presented. Control system requirements and their operational interrelationships that consider both integrated system control and individual subsystem control are described. Several configurations of minicomputers are established to provide direct control of sets of microcomputers and to provide points of operator-laser interaction. Over 100 microcomputers are located very close to the laser device control points or sources of data and perform the real-time functions of the control system, such as data and control signal multiplexing, stepping motor control, and vacuum and gas system control. These microcomputers are designed to be supported as an integral part of the control network and to be software compatible with the larger minicomputers.

  12. Hierarchical tree-structured control network for the Antares laser facility

    International Nuclear Information System (INIS)

    McGirt, F.

    1979-01-01

    The design and implementation of a distributed, computer-based control system for the Antares 100-kJ gas laser fusion facility is presented. Control system requirements and their operational interrelationships that consider both integrated system control and individual subsystem control are described. Several configurations of minicomputers are established to provide direct control of sets of microcomputers and to provide points of operator-laser interaction. Over 100 microcomputers are located very close to the laser device control points or sources of data and perform the real-time functions of the control system, such as data and control signal multiplexing, stepping motor control, and vacuum and gas system control. These microcomputers are designed to be supported as an integral part of the control network and to be software compatible with the larger minicomputers

  13. A Hierarchical Optimal Operation Strategy of Hybrid Energy Storage System in Distribution Networks with High Photovoltaic Penetration

    Directory of Open Access Journals (Sweden)

    Jian Chen

    2018-02-01

    Full Text Available In this paper, a hierarchical optimal operation strategy for a hybrid energy storage system (HESS is proposed, which is suitable to be utilized in distribution networks (DNs with high photovoltaic (PV penetration to achieve PV power smoothing, voltage regulation and price arbitrage. Firstly, a fuzzy-logic based variable step-size control strategy for an ultracapacitor (UC with the improvement of the lifetime of UC and tracking performance is adopted to smooth PV power fluctuations. The impact of PV forecasting errors is eliminated by adjusting the UC power in real time. Secondly, a coordinated control strategy, which includes centralized and local controls, is proposed for lithium-ion batteries. The centralized control is structured to determine the optimal battery unit for voltage regulation or price arbitrage according to lithium-ion battery performance indices. A modified lithium-ion battery aging model with better accuracy is proposed and the coupling relationship between the lifetime and the effective capacity is also considered. Additionally, the local control of the selected lithium-ion battery unit determines the charging/discharging power. A case study is used to validate the operation strategy and the results show that the lifetime equilibrium among different lithium-ion battery units can be achieved using the proposed strategy.

  14. A study on optimal task decomposition of networked parallel computing using PVM

    International Nuclear Information System (INIS)

    Seong, Kwan Jae; Kim, Han Gyoo

    1998-01-01

    A numerical study is performed to investigate the effect of task decomposition on networked parallel processes using Parallel Virtual Machine (PVM). In our study, a PVM program distributed over a network of workstations is used in solving a finite difference version of a one dimensional heat equation, where natural choice of PVM programming structure would be the master-slave paradigm, with the aim of finding an optimal configuration resulting in least computing time including communication overhead among machines. Given a set of PVM tasks comprised of one master and five slave programs, it is found that there exists a pseudo-optimal number of machines, which does not necessarily coincide with the number of tasks, that yields the best performance when the network is under a light usage. Increasing the number of machines beyond this optimal one does not improve computing performance since increase in communication overhead among the excess number of machines offsets the decrease in CPU time obtained by distributing the PVM tasks among these machines. However, when the network traffic is heavy, the results exhibit a more random characteristic that is explained by the random nature of data transfer time

  15. Florida Model Task Force on Diabetic Retinopathy: Development of an Interagency Network.

    Science.gov (United States)

    Groff, G.; And Others

    1990-01-01

    This article describes the development of a mechanism to organize a network in Florida for individuals who are at risk for diabetic retinopathy. The task force comprised representatives from governmental, academic, professional, and voluntary organizations. It worked to educate professionals, patients, and the public through brochures, resource…

  16. An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan; Qin, Jin

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

  17. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2014-01-01

    Full Text Available This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users’ demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators’ service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

  18. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

  19. Socio-contextual Network Mining for User Assistance in Web-based Knowledge Gathering Tasks

    Science.gov (United States)

    Rajendran, Balaji; Kombiah, Iyakutti

    Web-based Knowledge Gathering (WKG) is a specialized and complex information seeking task carried out by many users on the web, for their various learning, and decision-making requirements. We construct a contextual semantic structure by observing the actions of the users involved in WKG task, in order to gain an understanding of their task and requirement. We also build a knowledge warehouse in the form of a master Semantic Link Network (SLX) that accommodates and assimilates all the contextual semantic structures. This master SLX, which is a socio-contextual network, is then mined to provide contextual inputs to the current users through their agents. We validated our approach through experiments and analyzed the benefits to the users in terms of resource explorations and the time saved. The results are positive enough to motivate us to implement in a larger scale.

  20. Dual Arm Work Package performance estimates and telerobot task network simulation

    International Nuclear Information System (INIS)

    Draper, J.V.

    1997-01-01

    This paper describes the methodology and results of a network simulation study of the Dual Arm Work Package (DAWP), to be employed for dismantling the Argonne National Laboratory CP-5 reactor. The development of the simulation model was based upon the results of a task analysis for the same system. This study was performed by the Oak Ridge National Laboratory (ORNL), in the Robotics and Process Systems Division. Funding was provided the US Department of Energy's Office of Technology Development, Robotics Technology Development Program (RTDP). The RTDP is developing methods of computer simulation to estimate telerobotic system performance. Data were collected to provide point estimates to be used in a task network simulation model. Three skilled operators performed six repetitions of a pipe cutting task representative of typical teleoperation cutting operations

  1. Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation.

    Directory of Open Access Journals (Sweden)

    Salvador Dura-Bernal

    Full Text Available Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance. Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom

  2. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

    Directory of Open Access Journals (Sweden)

    Pavel Sanda

    2017-09-01

    Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

  3. Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.

    Science.gov (United States)

    Wang, Yue; Halper, Michael; Wei, Duo; Gu, Huanying; Perl, Yehoshua; Xu, Junchuan; Elhanan, Gai; Chen, Yan; Spackman, Kent A; Case, James T; Hripcsak, George

    2012-02-01

    Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. "Complex" concepts, which by their very nature are more difficult to model, fall neatly into this category. A special kind of grouping, called a partial-area, is utilized in the characterization of complex concepts. In particular, the complex concepts that are the focus of this work are those appearing in intersections of multiple partial-areas and are thus referred to as overlapping concepts. In a companion paper, an automatic methodology for identifying and partitioning the entire collection of overlapping concepts into disjoint, singly-rooted groups, that are more manageable to work with and comprehend, has been presented. The partitioning methodology formed the foundation for the development of an abstraction network for the overlapping concepts called a disjoint partial-area taxonomy. This new disjoint partial-area taxonomy offers a collection of semantically uniform partial-areas and is exploited herein as the basis for a novel auditing methodology. The review of the overlapping concepts is done in a top-down order within semantically uniform groups. These groups are themselves reviewed in a top-down order, which proceeds from the less complex to the more complex overlapping concepts. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. Hypotheses regarding error ratios for overlapping concepts and between different kinds of overlapping concepts are formulated. Two phases of auditing the Specimen hierarchy for two releases of SNOMED are reported on. With the use of the double bootstrap and Fisher's exact test (two-tailed), the auditing of concepts and especially roots of overlapping partial-areas is shown to yield a statistically significant higher proportion of errors. Copyright © 2011 Elsevier Inc. All rights

  4. Auditing Complex Concepts of SNOMED using a Refined Hierarchical Abstraction Network

    Science.gov (United States)

    Wang, Yue; Halper, Michael; Wei, Duo; Gu, Huanying; Perl, Yehoshua; Xu, Junchuan; Elhanan, Gai; Chen, Yan; Spackman, Kent A.; Case, James T.; Hripcsak, George

    2012-01-01

    Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. “Complex” concepts, which by their very nature are more difficult to model, fall neatly into this category. A special kind of grouping, called a partial-area, is utilized in the characterization of complex concepts. In particular, the complex concepts that are the focus of this work are those appearing in intersections of multiple partial-areas and are thus referred to as overlapping concepts. In a companion paper, an automatic methodology for identifying and partitioning the entire collection of overlapping concepts into disjoint, singly-rooted groups, that are more manageable to work with and comprehend, has been presented. The partitioning methodology formed the foundation for the development of an abstraction network for the overlapping concepts called a disjoint partial-area taxonomy. This new disjoint partial-area taxonomy offers a collection of semantically uniform partial-areas and is exploited herein as the basis for a novel auditing methodology. The review of the overlapping concepts is done in a top-down order within semantically uniform groups. These groups are themselves reviewed in a top-down order, which proceeds from the less complex to the more complex overlapping concepts. The results of applying the methodology to SNOMED’s Specimen hierarchy are presented. Hypotheses regarding error ratios for overlapping concepts and between different kinds of overlapping concepts are formulated. Two phases of auditing the Specimen hierarchy for two releases of SNOMED are reported on. With the use of the double bootstrap and Fisher’s exact test (two-tailed), the auditing of concepts and especially roots of overlapping partial-areas is shown to yield a statistically significant higher proportion of errors. PMID:21907827

  5. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  6. Optimizing the Number of Cooperating Terminals for Energy Aware Task Computing in Wireless Networks

    DEFF Research Database (Denmark)

    Olsen, Anders Brødløs; Fitzek, Frank H. P.; Koch, Peter

    2005-01-01

    It is generally accepted that energy consumption is a significant design constraint for mobile handheld systems, therefore motivations for methods optimizing the energy consumption making better use of the restricted battery resources are evident. A novel concept of distributed task computing...... is previously proposed (D2VS), where the overall idea of selective distribution of tasks among terminals is made. In this paper the optimal number of terminals for cooperative task computing in a wireless network will be investigated. The paper presents an energy model for the proposed scheme. Energy...... consumption of the terminals with respect to their workload and the overhead of distributing tasks among terminals are taken into account. The paper shows, that the number of cooperating terminals is in general limited to a few, though alternating with respect to the various system parameters....

  7. Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

    Science.gov (United States)

    Yin, Xi; Liu, Xiaoming

    2018-02-01

    This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

  8. Proposal of Constraints Analysis Method Based on Network Model for Task Planning

    Science.gov (United States)

    Tomiyama, Tomoe; Sato, Tatsuhiro; Morita, Toyohisa; Sasaki, Toshiro

    Deregulation has been accelerating several activities toward reengineering business processes, such as railway through service and modal shift in logistics. Making those activities successful, business entities have to regulate new business rules or know-how (we call them ‘constraints’). According to the new constraints, they need to manage business resources such as instruments, materials, workers and so on. In this paper, we propose a constraint analysis method to define constraints for task planning of the new business processes. To visualize each constraint's influence on planning, we propose a network model which represents allocation relations between tasks and resources. The network can also represent task ordering relations and resource grouping relations. The proposed method formalizes the way of defining constraints manually as repeatedly checking the network structure and finding conflicts between constraints. Being applied to crew scheduling problems shows that the method can adequately represent and define constraints of some task planning problems with the following fundamental features, (1) specifying work pattern to some resources, (2) restricting the number of resources for some works, (3) requiring multiple resources for some works, (4) prior allocation of some resources to some works and (5) considering the workload balance between resources.

  9. Shared "core" areas between the pain and other task-related networks.

    Directory of Open Access Journals (Sweden)

    Franco Cauda

    Full Text Available The idea of a 'pain matrix' specifically devoted to the processing of nociceptive inputs has been challenged. Alternative views now propose that the activity of the primary and secondary somatosensory cortices (SI, SII, the insula and cingulate cortex may be related to a basic defensive system through which significant potentially dangerous events for the body's integrity are detected. By reviewing the role of the SI, SII, the cingulate and the insular cortices in the perception of nociceptive and tactile stimuli, in attentional, emotional and reward tasks, and in interoception and memory, we found that all these task-related networks overlap in the dorsal anterior cingulate cortex, the anterior insula and the dorsal medial thalamus. A thorough analysis revealed that the 'pain-related' network shares important functional similarities with both somatomotor-somatosensory networks and emotional-interoceptive ones. We suggest that these shared areas constitute the central part of an adaptive control system involved in the processing and integration of salient information coming both from external and internal sources. These areas are activated in almost all fMRI tasks and have been indicated to play a pivotal role in switching between externally directed and internally directed brain networks.

  10. Effects of Stress and Task Difficulty on Working Memory and Cortical Networking.

    Science.gov (United States)

    Kim, Yujin; Woo, Jihwan; Woo, Minjung

    2017-12-01

    This study investigated interactive effects of stress and task difficulty on working memory and cortico-cortical communication during memory encoding. Thirty-eight adolescent participants (mean age of 15.7 ± 1.5 years) completed easy and hard working memory tasks under low- and high-stress conditions. We analyzed the accuracy and reaction time (RT) of working memory performance and inter- and intrahemispheric electroencephalogram coherences during memory encoding. Working memory accuracy was higher, and RT shorter, in the easy versus the hard task. RT was shorter under the high-stress (TENS) versus low-stress (no-TENS) condition, while there was no difference in memory accuracy between the two stress conditions. For electroencephalogram coherence, we found higher interhemispheric coherence in all bands but only at frontal electrode sites in the easy versus the hard task. On the other hand, intrahemispheric coherence was higher in the left hemisphere in the easy (versus hard task) and higher in the right hemisphere (with one exception) in the hard (versus easy task). Inter- and intracoherences were higher in the low- versus high-stress condition. Significant interactions between task difficulty and stress condition were observed in coherences of the beta frequency band. The difference in coherence between low- and high-stress conditions was greater in the hard compared with the easy task, with lower coherence under the high-stress condition relative to the low-stress condition. Stress seemed to cause a decrease in cortical network communications between memory-relevant cortical areas as task difficulty increased.

  11. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-05-24

    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.

  12. The Time Course of Task-Specific Memory Consolidation Effects in Resting State Networks

    Science.gov (United States)

    Sami, Saber; Robertson, Edwin M.

    2014-01-01

    Previous studies have reported functionally localized changes in resting-state brain activity following a short period of motor learning, but their relationship with memory consolidation and their dependence on the form of learning is unclear. We investigate these questions with implicit or explicit variants of the serial reaction time task (SRTT). fMRI resting-state functional connectivity was measured in human subjects before the tasks, and 0.1, 0.5, and 6 h after learning. There was significant improvement in procedural skill in both groups, with the group learning under explicit conditions showing stronger initial acquisition, and greater improvement at the 6 h retest. Immediately following acquisition, this group showed enhanced functional connectivity in networks including frontal and cerebellar areas and in the visual cortex. Thirty minutes later, enhanced connectivity was observed between cerebellar nuclei, thalamus, and basal ganglia, whereas at 6 h there was enhanced connectivity in a sensory-motor cortical network. In contrast, immediately after acquisition under implicit conditions, there was increased connectivity in a network including precentral and sensory-motor areas, whereas after 30 min a similar cerebello-thalamo-basal ganglionic network was seen as in explicit learning. Finally, 6 h after implicit learning, we found increased connectivity in medial temporal cortex, but reduction in precentral and sensory-motor areas. Our findings are consistent with predictions that two variants of the SRTT task engage dissociable functional networks, although there are also networks in common. We also show a converging and diverging pattern of flux between prefrontal, sensory-motor, and parietal areas, and subcortical circuits across a 6 h consolidation period. PMID:24623776

  13. Alternating Dynamics of Segregation and Integration in Human EEG Functional Networks During Working-memory Task.

    Science.gov (United States)

    Zippo, Antonio G; Della Rosa, Pasquale A; Castiglioni, Isabella; Biella, Gabriele E M

    2018-02-10

    Brain functional networks show high variability in short time windows but mechanisms governing these transient dynamics remain unknown. In this work, we studied the temporal evolution of functional brain networks involved in a working memory (WM) task while recording high-density electroencephalography (EEG) in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation faded after the prevailing the functional integration. Notably, wrong trials were associated with different or disrupted sequences of the segregation-integration profiles and measures of network centrality and modularity were able to identify crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a WM task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  14. Large-Scale Cooperative Task Distribution on Peer-to-Peer Networks

    Science.gov (United States)

    2012-01-01

    SUBTITLE Large-scale cooperative task distribution on peer-to-peer networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...disadvantages of ML- Chord are its fixed size (two layers), and limited scala - bility for large-scale systems. RC-Chord extends ML- D. Karrels et al...configurable before runtime. This can be improved by incorporating a distributed learning algorithm to tune the number and range of the DLoE tracking

  15. Differences between child and adult large-scale functional brain networks for reading tasks.

    Science.gov (United States)

    Liu, Xin; Gao, Yue; Di, Qiqi; Hu, Jiali; Lu, Chunming; Nan, Yun; Booth, James R; Liu, Li

    2018-02-01

    Reading is an important high-level cognitive function of the human brain, requiring interaction among multiple brain regions. Revealing differences between children's large-scale functional brain networks for reading tasks and those of adults helps us to understand how the functional network changes over reading development. Here we used functional magnetic resonance imaging data of 17 adults (19-28 years old) and 16 children (11-13 years old), and graph theoretical analyses to investigate age-related changes in large-scale functional networks during rhyming and meaning judgment tasks on pairs of visually presented Chinese characters. We found that: (1) adults had stronger inter-regional connectivity and nodal degree in occipital regions, while children had stronger inter-regional connectivity in temporal regions, suggesting that adults rely more on visual orthographic processing whereas children rely more on auditory phonological processing during reading. (2) Only adults showed between-task differences in inter-regional connectivity and nodal degree, whereas children showed no task differences, suggesting the topological organization of adults' reading network is more specialized. (3) Children showed greater inter-regional connectivity and nodal degree than adults in multiple subcortical regions; the hubs in children were more distributed in subcortical regions while the hubs in adults were more distributed in cortical regions. These findings suggest that reading development is manifested by a shift from reliance on subcortical to cortical regions. Taken together, our study suggests that Chinese reading development is supported by developmental changes in brain connectivity properties, and some of these changes may be domain-general while others may be specific to the reading domain. © 2017 Wiley Periodicals, Inc.

  16. Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?. -based on different credit correlations using hierarchical methods

    Science.gov (United States)

    He, Fang; Chen, Xi

    2016-11-01

    The accelerating accumulation and risk concentration of Chinese local financing platforms debts have attracted wide attention throughout the world. Due to the network of financial exposures among institutions, the failure of several platforms or regions of systemic importance will probably trigger systemic risk and destabilize the financial system. However, the complex network of credit relationships in Chinese local financing platforms at the state level remains unknown. To fill this gap, we presented the first complex networks and hierarchical cluster analysis of the credit market of Chinese local financing platforms using the ;bottom up; method from firm-level data. Based on balance-sheet channel, we analyzed the topology and taxonomy by applying the analysis paradigm of subdominant ultra-metric space to an empirical data in 2013. It is remarked that we chose to extract the network of co-financed financing platforms in order to evaluate the effect of risk contagion from platforms to bank system. We used the new credit similarity measure by combining the factor of connectivity and size, to extract minimal spanning trees (MSTs) and hierarchical trees (HTs). We found that: (1) the degree distributions of credit correlation backbone structure of Chinese local financing platforms are fat tailed, and the structure is unstable with respect to targeted failures; (2) the backbone is highly hierarchical, and largely explained by the geographic region; (3) the credit correlation backbone structure based on connectivity and size is significantly heterogeneous; (4) key platforms and regions of systemic importance, and contagion path of systemic risk are obtained, which are contributed to preventing systemic risk and regional risk of Chinese local financing platforms and preserving financial stability under the framework of macro prudential supervision. Our approach of credit similarity measure provides a means of recognizing ;systemically important; institutions and regions

  17. Brain functional network changes following Prelimbic area inactivation in a spatial memory extinction task.

    Science.gov (United States)

    Méndez-Couz, Marta; Conejo, Nélida M; Vallejo, Guillermo; Arias, Jorge L

    2015-01-01

    Several studies suggest a prefrontal cortex involvement during the acquisition and consolidation of spatial memory, suggesting an active modulating role at late stages of acquisition processes. Recently, we have reported that the prelimbic and infralimbic areas of the prefrontal cortex, among other structures, are also specifically involved in the late phases of spatial memory extinction. This study aimed to evaluate whether the inactivation of the prelimbic area of the prefrontal cortex impaired spatial memory extinction. For this purpose, male Wistar rats were implanted bilaterally with cannulae into the prelimbic region of the prefrontal cortex. Animals were trained during 5 consecutive days in a hidden platform task and tested for reference spatial memory immediately after the last training session. One day after completing the training task, bilateral infusion of the GABAA receptor agonist Muscimol was performed before the extinction protocol was carried out. Additionally, cytochrome c oxidase histochemistry was applied to map the metabolic brain activity related to the spatial memory extinction under prelimbic cortex inactivation. Results show that animals acquired the reference memory task in the water maze, and the extinction task was successfully completed without significant impairment. However, analysis of the functional brain networks involved by cytochrome oxidase activity interregional correlations showed changes in brain networks between the group treated with Muscimol as compared to the saline-treated group, supporting the involvement of the mammillary bodies at a the late stage in the memory extinction process. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Distributed Schemes for Crowdsourcing-Based Sensing Task Assignment in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Linbo Zhai

    2017-01-01

    Full Text Available Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence.

  19. Same task, different strategies: how brain networks can be influenced by memory strategy.

    Science.gov (United States)

    Sanfratello, Lori; Caprihan, Arvind; Stephen, Julia M; Knoefel, Janice E; Adair, John C; Qualls, Clifford; Lundy, S Laura; Aine, Cheryl J

    2014-10-01

    Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a WM task is defined as verbal or spatial, different types of memory strategies may be used to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type of strategy used by individual members in a group of 28 young healthy participants (18-25 years) during a spatial WM task. A cluster analysis was performed to differentiate groups. We acquired functional magnetoencephalography and structural diffusion tensor imaging measures to characterize the brain networks associated with the use of different strategies. We found two types of strategies were used during the spatial WM task, a visuospatial and a verbal strategy, and brain regions and time courses of activation differed between participants who used each. Task performance also varied by type of strategy used with verbal strategies showing an advantage. In addition, performance on neuropsychological tests (indices from Wechsler Adult Intelligence Scale-IV, Rey Complex Figure Test) correlated significantly with fractional anisotropy measures for the visuospatial strategy group in white matter tracts implicated in other WM and attention studies. We conclude that differences in memory strategy can have a pronounced effect on the locations and timing of brain activation and that these differences need further investigation as a possible confounding factor for studies using group averaging as a means for summarizing results. Copyright © 2014 Wiley Periodicals, Inc.

  20. The default mode network and the working memory network are not anti-correlated during all phases of a working memory task.

    Science.gov (United States)

    Piccoli, Tommaso; Valente, Giancarlo; Linden, David E J; Re, Marta; Esposito, Fabrizio; Sack, Alexander T; Di Salle, Francesco

    2015-01-01

    The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between "task-positive" and "task-negative" brain networks. Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network.

  1. Cellulose nanofiber-templated three-dimension TiO 2 hierarchical nanowire network for photoelectrochemical photoanode

    Science.gov (United States)

    Zhaodong Li; Chunhua Yao; Fei Wang; Zhiyong Cai; Xudong Wang

    2014-01-01

    Three dimensional (3D) nanostructures with extremely large porosity possess a great promise for the development of high-performance energy harvesting storage devices. In this paper, we developed a high-density 3D TiO2 fiber-nanorod (NR) heterostructure for photoelectrochemical (PEC) water splitting. The hierarchical structure was synthesized on a...

  2. Temporal motifs reveal collaboration patterns in online task-oriented networks

    Science.gov (United States)

    Xuan, Qi; Fang, Huiting; Fu, Chenbo; Filkov, Vladimir

    2015-05-01

    Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

  3. Optimization of Hierarchical System for Data Acquisition

    Directory of Open Access Journals (Sweden)

    V. Novotny

    2011-04-01

    Full Text Available Television broadcasting over IP networks (IPTV is one of a number of network applications that are except of media distribution also interested in data acquisition from group of information resources of variable size. IP-TV uses Real-time Transport Protocol (RTP protocol for media streaming and RTP Control Protocol (RTCP protocol for session quality feedback. Other applications, for example sensor networks, have data acquisition as the main task. Current solutions have mostly problem with scalability - how to collect and process information from large amount of end nodes quickly and effectively? The article deals with optimization of hierarchical system of data acquisition. Problem is mathematically described, delay minima are searched and results are proved by simulations.

  4. Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

    Science.gov (United States)

    Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

    2017-01-01

    This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

  5. Functional connectivity in task-negative network of the Deaf: effects of sign language experience

    Directory of Open Access Journals (Sweden)

    Evie Malaia

    2014-06-01

    Full Text Available Prior studies investigating cortical processing in Deaf signers suggest that life-long experience with sign language and/or auditory deprivation may alter the brain’s anatomical structure and the function of brain regions typically recruited for auditory processing (Emmorey et al., 2010; Pénicaud et al., 2013 inter alia. We report the first investigation of the task-negative network in Deaf signers and its functional connectivity—the temporal correlations among spatially remote neurophysiological events. We show that Deaf signers manifest increased functional connectivity between posterior cingulate/precuneus and left medial temporal gyrus (MTG, but also inferior parietal lobe and medial temporal gyrus in the right hemisphere- areas that have been found to show functional recruitment specifically during sign language processing. These findings suggest that the organization of the brain at the level of inter-network connectivity is likely affected by experience with processing visual language, although sensory deprivation could be another source of the difference. We hypothesize that connectivity alterations in the task negative network reflect predictive/automatized processing of the visual signal.

  6. gTBS: A green Task-Based Sensing for energy efficient Wireless Sensor Networks

    KAUST Repository

    Al-Halafi, Abdullah

    2016-09-08

    Wireless sensor networks (WSN) are widely used to sense and measure physical conditions for different purposes and within different regions. However due to the limited lifetime of the sensor\\'s energy source, many efforts are made to design energy efficient WSN. As a result, many techniques were presented in the literature such as power adaptation, sleep and wake-up, and scheduling in order to enhance WSN lifetime. These techniques where presented separately and shown to achieve some gain in terms of energy efficiency. In this paper, we present an energy efficient cross layer design for WSN that we named \\'green Task-Based Sensing\\' (gTBS) scheme. The gTBS design is a task based sensing scheme that not only prevents wasting power in unnecessary signaling, but also utilizes several techniques for achieving reliable and energy efficient WSN. The proposed gTBS combines the power adaptation with a sleep and wake-up technique that allows inactive nodes to sleep. Also, it adopts a gradient-oriented unicast approach to overcome the synchronization problem, minimize network traffic hurdles, and significantly reduce the overall power consumption of the network. We implement the gTBS on a testbed and we show that it reduces the power consumption by a factor of 20%-55% compared to traditional TBS. It also reduces the delay by 54%-145% and improves the delivery ratio by 24%-73%. © 2016 IEEE.

  7. Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

    NARCIS (Netherlands)

    González-Ferrer, A.; ten Teije, A.C.M.; Fdez-Olivares, J.; Milian, K.

    OBJECTIVE: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To

  8. Hierarchical Neural Regression Models for Customer Churn Prediction

    Directory of Open Access Journals (Sweden)

    Golshan Mohammadi

    2013-01-01

    Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.

  9. Modulations of the executive control network by stimulus onset asynchrony in a Stroop task

    Science.gov (United States)

    2013-01-01

    Background Manipulating task difficulty is a useful way of elucidating the functional recruitment of the brain’s executive control network. In a Stroop task, pre-exposing the irrelevant word using varying stimulus onset asynchronies (‘negative’ SOAs) modulates the amount of behavioural interference and facilitation, suggesting disparate mechanisms of cognitive processing in each SOA. The current study employed a Stroop task with three SOAs (−400, -200, 0 ms), using functional magnetic resonance imaging to investigate for the first time the neural effects of SOA manipulation. Of specific interest were 1) how SOA affects the neural representation of interference and facilitation; 2) response priming effects in negative SOAs; and 3) attentional effects of blocked SOA presentation. Results The results revealed three regions of the executive control network that were sensitive to SOA during Stroop interference; the 0 ms SOA elicited the greatest activation of these areas but experienced relatively smaller behavioural interference, suggesting that the enhanced recruitment led to more efficient conflict processing. Response priming effects were localized to the right inferior frontal gyrus, which is consistent with the idea that this region performed response inhibition in incongruent conditions to overcome the incorrectly-primed response, as well as more general action updating and response preparation. Finally, the right superior parietal lobe was sensitive to blocked SOA presentation and was most active for the 0 ms SOA, suggesting that this region is involved in attentional control. Conclusions SOA exerted both trial-specific and block-wide effects on executive processing, providing a unique paradigm for functional investigations of the cognitive control network. PMID:23902451

  10. Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

    Directory of Open Access Journals (Sweden)

    Yan-li Yang

    2015-01-01

    Full Text Available It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

  11. A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

    Science.gov (United States)

    Tsumori, Kenji; Ozawa, Seiichi

    When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

  12. Structural changes in the minimal spanning tree and the hierarchical network in the Korean stock market around the global financial crisis

    Science.gov (United States)

    Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

    2015-04-01

    This paper considers stock prices in the Korean stock market during the 2008 global financial crisis by focusing on three time periods: before, during, and after the crisis. Complex networks are extracted from cross-correlation coefficients between the normalized logarithmic return of the stock price time series of firms. The minimal spanning trees (MSTs) and the hierarchical network (HN) are generated from cross-correlation coefficients. Before and after the crisis, securities firms are located at the center of the MST. During the crisis, however, the center of the MST changes to a firm in heavy industry and construction. During the crisis, the MST shrinks in comparison to that before and that after the crisis. This topological change in the MST during the crisis reflects a distinct effect of the global financial crisis. The cophenetic correlation coefficient increases during the crisis, indicating an increase in the hierarchical structure during in this period. When crisis hits the market, firms behave synchronously, and their correlations are higher than those during a normal period.

  13. 3D Networked Tin Oxide/Graphene Aerogel with a Hierarchically Porous Architecture for High-Rate Performance Sodium-Ion Batteries.

    Science.gov (United States)

    Xie, Xiuqiang; Chen, Shuangqiang; Sun, Bing; Wang, Chengyin; Wang, Guoxiu

    2015-09-07

    Low-cost and sustainable sodium-ion batteries are regarded as a promising technology for large-scale energy storage and conversion. The development of high-rate anode materials is highly desirable for sodium-ion batteries. The optimization of mass transport and electron transfer is crucial in the discovery of electrode materials with good high-rate performances. Herein, we report the synthesis of 3 D interconnected SnO2 /graphene aerogels with a hierarchically porous structure as anode materials for sodium-ion batteries. The unique 3 D architecture was prepared by a facile in situ process, during which cross-linked 3 D conductive graphene networks with macro-/meso-sized hierarchical pores were formed and SnO2 nanoparticles were dispersed uniformly on the graphene surface simultaneously. Such a 3 D functional architecture not only facilitates the electrode-electrolyte interaction but also provides an efficient electron pathway within the graphene networks. When applied as anode materials in sodium-ion batteries, the as-prepared SnO2 /graphene aerogel exhibited high reversible capacity, improved cycling performance compared to SnO2 , and promising high-rate capability. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Self-assembly of nano/micro-structured Fe3O4 microspheres among 3D rGO/CNTs hierarchical networks with superior lithium storage performances

    International Nuclear Information System (INIS)

    Liu, Jinlong; Feng, Haibo; Wang, Xipeng; Qian, Dong; Jiang, Jianbo; Li, Junhua; Peng, Sanjun; Deng, Miao; Liu, Youcai

    2014-01-01

    Nano/micro-structured Fe 3 O 4 microspheres among three-dimensional (3D) reduced graphene oxide (rGO)/carbon nanotubes (CNTs) hierarchical networks (the ternary composite is denoted as rGCFs) have been synthesized using a facile, self-assembled and one-pot hydrothermal approach. The rGCFs composite exhibits superior lithium storage performances: initial discharge and charge capacities of 1452 and 1036 mAh g −1 , respectively, remarkable rate capability at current densities from 100 mA g −1 to 10 A g −1 and outstanding cycling performance up to 200 cycles. The highly enhanced electrochemical performances of rGCFs depend heavily on the robust 3D rGO/CNTs hierarchical networks, the stable nano/microstructures of active Fe 3 O 4 microspheres and the positive synergistic effects of building components. The systematic structure characterizations and electrochemical investigations provide insightful understanding towards the relationship between structure/morphology and lithium storage performances, which may pave the way for the rational design of composite materials with desirable goals. (papers)

  15. Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task.

    Science.gov (United States)

    Mochizuki, Kei; Funahashi, Shintaro

    2016-01-01

    While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area. Copyright © 2016 the American Physiological Society.

  16. Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task

    Science.gov (United States)

    Mochizuki, Kei

    2015-01-01

    While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area. PMID:26490287

  17. Changes in default mode network as automaticity develops in a categorization task.

    Science.gov (United States)

    Shamloo, Farzin; Helie, Sebastien

    2016-10-15

    The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions.

    Science.gov (United States)

    Barnes, Jessica J; Nobre, Anna Christina; Woolrich, Mark W; Baker, Kate; Astle, Duncan E

    2016-08-24

    Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called "phase amplitude coupling." Copyright © 2016 Barnes et al.

  19. Mathematics anxiety reduces default mode network deactivation in response to numerical tasks

    Directory of Open Access Journals (Sweden)

    Belinda ePletzer

    2015-04-01

    Full Text Available Mathematics anxiety is negatively related to mathematics performance, thereby threatening the professional success. Preoccupation with the emotional content of the stimuli may consume working memory resources, which may be reflected in decreased deactivation of areas associated with the default mode network (DMN activated during self-referential and emotional processing. The common problem is that math anxiety is usually associated with poor math performance, so that any group differences are difficult to interpret.Here we compared the BOLD-response of 18 participants with high (HMAs and 18 participants with low mathematics anxiety (LMAs matched for their mathematical performance to two numerical tasks (number comparison, number bisection. During both tasks, we found stronger deactivation within the DMN in LMAs compared to HMAs, while BOLD-response in task-related activation areas did not differ between HMAs and LMAs. The difference in DMN deactivation between the HMA and LMA group was more pronounced in stimuli with additional requirement on inhibitory functions, but did not differ between number magnitude processing and arithmetic fact retrieval.

  20. Mathematics anxiety reduces default mode network deactivation in response to numerical tasks.

    Science.gov (United States)

    Pletzer, Belinda; Kronbichler, Martin; Nuerk, Hans-Christoph; Kerschbaum, Hubert H

    2015-01-01

    Mathematics anxiety is negatively related to mathematics performance, thereby threatening the professional success. Preoccupation with the emotional content of the stimuli may consume working memory resources, which may be reflected in decreased deactivation of areas associated with the default mode network (DMN) activated during self-referential and emotional processing. The common problem is that math anxiety is usually associated with poor math performance, so that any group differences are difficult to interpret. Here we compared the BOLD-response of 18 participants with high (HMAs) and 18 participants with low mathematics anxiety (LMAs) matched for their mathematical performance to two numerical tasks (number comparison, number bisection). During both tasks, we found stronger deactivation within the DMN in LMAs compared to HMAs, while BOLD-response in task-related activation areas did not differ between HMAs and LMAs. The difference in DMN deactivation between the HMA and LMA group was more pronounced in stimuli with additional requirement on inhibitory functions, but did not differ between number magnitude processing and arithmetic fact retrieval.

  1. Altered Distant Synchronization of Background Network in Mild Cognitive Impairment during an Executive Function Task

    Directory of Open Access Journals (Sweden)

    Pengyun Wang

    2017-09-01

    Full Text Available Few studies to date have investigated the background network in the cognitive state relying on executive function in mild cognitive impairment (MCI patients. Using the index of degree of centrality (DC, we explored distant synchronization of background network in MCI during a hybrid delayed-match-to-sample task (DMST, which mainly relies on the working memory component of executive function. We observed significant interactions between group and cognitive state in the bilateral posterior cingulate cortex (PCC and the ventral subregion of precuneus. For normal control (NC group, the long distance functional connectivity (FC of the PCC/precuneus with the other regions of the brain was higher in rest state than that working memory state. For MCI patients, however, this pattern altered. There was no significant difference between rest and working memory state. The similar pattern was observed in the other cluster located in the right angular gyrus. To examine whether abnormal DC in PCC/precuneus and angular gyrus partially resulted from the deficit of FC between these regions and the other parts in the whole brain, we conducted a seed-based correlation analysis with these regions as seeds. The results indicated that the FC between bilateral PCC/precuneus and the right inferior parietal lobule (IPL increased from rest to working memory state for NC participants. For MCI patients, however, there was no significant change between rest and working memory state. The similar pattern was observed for the FC between right angular gyrus and right anterior insula. However, there was no difference between MCI and NC groups in global efficiency and modularity. It may indicate a lack of efficient reorganization from rest state to a working memory state in the brain network of MCI patients. The present study demonstrates the altered distant synchronization of background network in MCI during a task relying on executive function. The results provide a new

  2. The neural network involved in a bimanual tactile-tactile matching discrimination task: a functional imaging study at 3 T

    Energy Technology Data Exchange (ETDEWEB)

    Habas, Christophe; Cabanis, Emmanuel A. [UPMC Paris 6, Service de NeuroImagerie, Hopital des Quinze-Vingts, Paris (France)

    2007-08-15

    The cerebral and cerebellar network involved in a bimanual object recognition was studied in blood oxygenation dependent level functional magnetic resonance imaging (fMRI). Nine healthy right-handed volunteers were scanned (1) while performing bilateral finger movements (nondiscrimination motor task), and (2) while performing a bimanual tactile-tactile matching discrimination task using small chess pieces (tactile discrimination task). Extensive activations were specifically observed in the parietal (SII, superior lateral lobule), insular, prefrontal, cingulate and neocerebellar cortices (HVIII), with a left predominance in motor areas, during the tactile discrimination task in contrast to the findings during the nondiscrimination motor task. Bimanual tactile-tactile matching discrimination recruits multiple sensorimotor and associative cerebral and neocerebellar networks (including the cerebellar second homunculus, HVIII), comparable to the neural circuits involved in unimanual tactile object recognition. (orig.)

  3. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Directory of Open Access Journals (Sweden)

    H Francis Song

    2016-02-01

    Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

  4. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

    Science.gov (United States)

    Wang, Xiao-Jing

    2016-01-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

  5. Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

    Science.gov (United States)

    Wu, Xia; Yu, Xinyu; Yao, Li; Li, Rui

    2014-01-01

    Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states. PMID:25309414

  6. Working memory activation of neural networks in the elderly as a function of information processing phase and task complexity.

    Science.gov (United States)

    Charroud, Céline; Steffener, Jason; Le Bars, Emmanuelle; Deverdun, Jérémy; Bonafe, Alain; Abdennour, Meriem; Portet, Florence; Molino, François; Stern, Yaakov; Ritchie, Karen; Menjot de Champfleur, Nicolas; Akbaraly, Tasnime N

    2015-11-01

    Changes in working memory are sensitive indicators of both normal and pathological brain aging and associated disability. The present study aims to further understanding of working memory in normal aging using a large cohort of healthy elderly in order to examine three separate phases of information processing in relation to changes in task load activation. Using covariance analysis, increasing and decreasing neural activation was observed on fMRI in response to a delayed item recognition task in 337 cognitively healthy elderly persons as part of the CRESCENDO (Cognitive REServe and Clinical ENDOphenotypes) study. During three phases of the task (stimulation, retention, probe), increased activation was observed with increasing task load in bilateral regions of the prefrontal cortex, parietal lobule, cingulate gyrus, insula and in deep gray matter nuclei, suggesting an involvement of central executive and salience networks. Decreased activation associated with increasing task load was observed during the stimulation phase, in bilateral temporal cortex, parietal lobule, cingulate gyrus and prefrontal cortex. This spatial distribution of decreased activation is suggestive of the default mode network. These findings support the hypothesis of an increased activation in salience and central executive networks and a decreased activation in default mode network concomitant to increasing task load. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

    International Nuclear Information System (INIS)

    Amancio, Diego R; Oliveira Jr, Osvaldo N; Costa, Luciano da F

    2012-01-01

    The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabási–Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists. (paper)

  8. Functional mapping of language networks in the normal brain using a word-association task

    International Nuclear Information System (INIS)

    Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

    2010-01-01

    Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar–occipital–fusiform–thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic

  9. Functional mapping of language networks in the normal brain using a word-association task

    Directory of Open Access Journals (Sweden)

    Ghosh Shantanu

    2010-01-01

    Full Text Available Background: Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. Aim: The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI in normal human subjects. Materials and Methods: Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2FNx01-weighted gradient-echo echo-planar imaging (GE-EPI sequence (TR 4523 ms, TE 64 ms, flip angle 90º with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2 with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Results: Single subject analysis of the functional data (FWE-corrected, P≤0.001 revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG, superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG, anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001 revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Conclusions: Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these

  10. Assembling nitrogen and oxygen co-doped graphene quantum dots onto hierarchical carbon networks for all-solid-state flexible supercapacitors

    International Nuclear Information System (INIS)

    Li, Zhen; Li, Yanfeng; Wang, Liang; Cao, Ling; Liu, Xiang; Chen, Zhiwen; Pan, Dengyu; Wu, Minghong

    2017-01-01

    Highlights: • The all-carbon ternary flexible electrodes have been fabricated by the electrode deposition of nitrogen and oxygen co-doped single-crystalline GQDs. • The flexible electrodes deliver ultrahigh specific capacitance (461 mF cm"−"2) by inducing a high concentration of active nitrogen and oxygen at edge. • Symmetrical N-O-GQD/CNT/CC all-solid-state flexible supercapacitors offer energy density up to 32 μWh cm"−"2 and demonstrate the good stability, high flexibility, and folding ability under different deformations. • Nitrogen and oxygen co-doped GQDs can function as a highly active, solution-processable pseudocapacitive materials applicable to high-performance supercapacitors. - Abstract: We present a novel approach for hierarchical fabrication of high-performance, all-solid-state, flexible supercapacitors from environmentally friendly all-carbon materials. Three-dimensional carbon nanotube/carbon cloth network (CNT/CC) is used as a conductive, flexible and free-standing scaffold for the electro-deposition of highly N/O co-doped graphene quantum dots to form the high-activity, all-carbon electrodes. The hierarchical structure of the CNT/CC network with high electrical conductivity and high surface area provides improved conductive pathways for the efficient activation of GQDs with high pseudocapacitance and electrical double layer capacitance. The obtained N-O-GQD/CNT/CC electrodes for all-solid-state flexible supercapacitors exhibit an ultrahigh areal capacitance of up to 461 mF cm"−"2 at a current density of 0.5 mA cm"−"2, while keeping high rate and cyclic performances. This work highlights the great potential of highly active GQDs in the construction of high-performance flexible energy-storage devices.

  11. Dynamic spatial coding within the dorsal frontoparietal network during a visual search task.

    Directory of Open Access Journals (Sweden)

    Wieland H Sommer

    Full Text Available To what extent are the left and right visual hemifields spatially coded in the dorsal frontoparietal attention network? In many experiments with neglect patients, the left hemisphere shows a contralateral hemifield preference, whereas the right hemisphere represents both hemifields. This pattern of spatial coding is often used to explain the right-hemispheric dominance of lesions causing hemispatial neglect. However, pathophysiological mechanisms of hemispatial neglect are controversial because recent experiments on healthy subjects produced conflicting results regarding the spatial coding of visual hemifields. We used an fMRI paradigm that allowed us to distinguish two attentional subprocesses during a visual search task. Either within the left or right hemifield subjects first attended to stationary locations (spatial orienting and then shifted their attentional focus to search for a target line. Dynamic changes in spatial coding of the left and right hemifields were observed within subregions of the dorsal front-parietal network: During stationary spatial orienting, we found the well-known spatial pattern described above, with a bilateral hemifield representation in the right hemisphere and a contralateral preference in the left hemisphere. However, during search, the right hemisphere had a contralateral preference and the left hemisphere equally represented both hemifields. This finding leads to novel perspectives regarding models of visuospatial attention and hemispatial neglect.

  12. Psychopathic traits associated with abnormal hemodynamic activity in salience and default mode networks during auditory oddball task.

    Science.gov (United States)

    Anderson, Nathaniel E; Maurer, J Michael; Steele, Vaughn R; Kiehl, Kent A

    2018-06-01

    Psychopathy is a personality disorder accompanied by abnormalities in emotional processing and attention. Recent theoretical applications of network-based models of cognition have been used to explain the diverse range of abnormalities apparent in psychopathy. Still, the physiological basis for these abnormalities is not well understood. A significant body of work has examined psychopathy-related abnormalities in simple attention-based tasks, but these studies have largely been performed using electrocortical measures, such as event-related potentials (ERPs), and they often have been carried out among individuals with low levels of psychopathic traits. In this study, we examined neural activity during an auditory oddball task using functional magnetic resonance imaging (fMRI) during a simple auditory target detection (oddball) task among 168 incarcerated adult males, with psychopathic traits assessed via the Hare Psychopathy Checklist-Revised (PCL-R). Event-related contrasts demonstrated that the largest psychopathy-related effects were apparent between the frequent standard stimulus condition and a task-off, implicit baseline. Negative correlations with interpersonal-affective dimensions (Factor 1) of the PCL-R were apparent in regions comprising default mode and salience networks. These findings support models of psychopathy describing impaired integration across functional networks. They additionally corroborate reports which have implicated failures of efficient transition between default mode and task-positive networks. Finally, they demonstrate a neurophysiological basis for abnormal mobilization of attention and reduced engagement with stimuli that have little motivational significance among those with high psychopathic traits.

  13. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

    2017-12-01

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

  14. High Efficiency, Transparent, Reusable, and Active PM2.5 Filters by Hierarchical Ag Nanowire Percolation Network.

    Science.gov (United States)

    Jeong, Seongmin; Cho, Hyunmin; Han, Seonggeun; Won, Phillip; Lee, Habeom; Hong, Sukjoon; Yeo, Junyeob; Kwon, Jinhyeong; Ko, Seung Hwan

    2017-07-12

    Air quality has become a major public health issue in Asia including China, Korea, and India. Particulate matters are the major concern in air quality. We present the first environmental application demonstration of Ag nanowire percolation network for a novel, electrical type transparent, reusable, and active PM2.5 air filter although the Ag nanowire percolation network has been studied as a very promising transparent conductor in optoelectronics. Compared with previous particulate matter air filter study using relatively weaker short-range intermolecular force in polar polymeric nanofiber, Ag nanowire percolation network filters use stronger long-range electrostatic force to capture PM2.5, and they are highly efficient (>99.99%), transparent, working on an active mode, low power consumption, antibacterial, and reusable after simple washing. The proposed new particulate matter filter can be applied for a highly efficient, reusable, active and energy efficient filter for wearable electronics application.

  15. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

    Science.gov (United States)

    Amiri, Zohreh; Mohammad, Kazem; Mahmoudi, Mahmood; Parsaeian, Mahbubeh; Zeraati, Hojjat

    2013-01-01

    There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models. This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model. We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests. Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer

  16. Enhanced Deployment Strategy for Role-Based Hierarchical Application Agents in Wireless Sensor Networks with Established Clusterheads

    Science.gov (United States)

    Gendreau, Audrey

    2014-01-01

    Efficient self-organizing virtual clusterheads that supervise data collection based on their wireless connectivity, risk, and overhead costs, are an important element of Wireless Sensor Networks (WSNs). This function is especially critical during deployment when system resources are allocated to a subsequent application. In the presented research,…

  17. Compromises Between Quality of Service Metrics and Energy Consumption of Hierarchical and Flat Routing Protocols for Wireless Sensors Network

    Directory of Open Access Journals (Sweden)

    Abdelbari BEN YAGOUTA

    2016-11-01

    Full Text Available Wireless Sensor Network (WSN is wireless network composed of spatially distributed and tiny autonomous nodes, which cooperatively monitor physical or environmental conditions. Among the concerns of these networks is prolonging the lifetime by saving nodes energy. There are several protocols specially designed for WSNs based on energy conservation. However, many WSNs applications require QoS (Quality of Service criteria, such as latency, reliability and throughput. In this paper, we will compare three routing protocols for wireless sensors network LEACH (Low Energy Adaptive Clustering Hierarchy, AODV (Ad hoc on demand Distance Vector and LABILE (Link Quality-Based Lexical Routing using Castalia simulator in terms of energy consumption, throughput, reliability and latency time of packets received by sink under different conditions to determinate the best configurations that offers the most suitable compromises between energy conservation and all QoS metrics for each routing protocols. The results show that, the best configurations that offer the suitable compromises between energy conservation and all QoS metrics is a large number of deployed nodes with low packet rate for LEACH (300 nodes and 1 packet/s, a medium number of deployed nodes with low packet rate For AODV (100 nodes and 1 packet/s and a very low nodes density with low packet rate for LABILE (50 nodes and 1 packet/s.

  18. Social Network Analysis as an Analytic Tool for Task Group Research: A Case Study of an Interdisciplinary Community of Practice

    Science.gov (United States)

    Lockhart, Naorah C.

    2017-01-01

    Group counselors commonly collaborate in interdisciplinary settings in health care, substance abuse, and juvenile justice. Social network analysis is a methodology rarely used in counseling research yet has potential to examine task group dynamics in new ways. This case study explores the scholarly relationships among 36 members of an…

  19. Optimization of potential field method parameters through networks for swarm cooperative manipulation tasks

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    Rocco Furferi

    2016-10-01

    Full Text Available An interesting current research field related to autonomous robots is mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments. Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs is a complex and difficult application compared with the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication. A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs is proposed in this article. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the article, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided with a seven-degrees-of-freedom robotic arm. The simulation results are optimized thanks to the ANNs used for the potentials tuning.

  20. Beta and gamma oscillatory activities associated with olfactory memory tasks: different rhythms for different functional networks?

    Science.gov (United States)

    Martin, Claire; Ravel, Nadine

    2014-01-01

    Olfactory processing in behaving animals, even at early stages, is inextricable from top down influences associated with odor perception. The anatomy of the olfactory network (olfactory bulb, piriform, and entorhinal cortices) and its unique direct access to the limbic system makes it particularly attractive to study how sensory processing could be modulated by learning and memory. Moreover, olfactory structures have been early reported to exhibit oscillatory population activities easy to capture through local field potential recordings. An attractive hypothesis is that neuronal oscillations would serve to "bind" distant structures to reach a unified and coherent perception. In relation to this hypothesis, we will assess the functional relevance of different types of oscillatory activity observed in the olfactory system of behaving animals. This review will focus primarily on two types of oscillatory activities: beta (15-40 Hz) and gamma (60-100 Hz). While gamma oscillations are dominant in the olfactory system in the absence of odorant, both beta and gamma rhythms have been reported to be modulated depending on the nature of the olfactory task. Studies from the authors of the present review and other groups brought evidence for a link between these oscillations and behavioral changes induced by olfactory learning. However, differences in studies led to divergent interpretations concerning the respective role of these oscillations in olfactory processing. Based on a critical reexamination of those data, we propose hypotheses on the functional involvement of beta and gamma oscillations for odor perception and memory.

  1. Beta and gamma oscillatory activities associated with olfactory memory tasks: Different rhythms for different functional networks?

    Directory of Open Access Journals (Sweden)

    Claire eMartin

    2014-06-01

    Full Text Available Olfactory processing in behaving animals, even at early stages, is inextricable from top down influences associated with odor perception. The anatomy of the olfactory network (olfactory bulb, piriform and entorhinal cortices and its unique direct access to the limbic system makes it particularly attractive to study how sensory processing could be modulated by learning and memory. Moreover, olfactory structures have been early reported to exhibit oscillatory population activities easy to capture through local field potential recordings. An attractive hypothesis is that neuronal oscillations would serve to ‘bind’ distant structures to reach a unified and coherent perception. In relation to this hypothesis, we will assess the functional relevance of different types of oscillatory activity observed in the olfactory system of behaving animals. This review will focus primarily on two types of oscillatory activities: beta (15-40 Hz and gamma (60-100 Hz. While gamma oscillations are dominant in the olfactory system in the absence of odorant, both beta and gamma rhythms have been reported to be modulated depending on the nature of the olfactory task. Studies from the authors of the present review and other groups brought evidence for a link between these oscillations and behavioral changes induced by olfactory learning. However, differences in studies led to divergent interpretations concerning the respective role of these oscillations in olfactory processing. Based on a critical reexamination of those data, we propose hypotheses on the functional involvement of beta and gamma oscillations for odor perception and memory.

  2. Establishing the resting state default mode network derived from functional magnetic resonance imaging tasks as an endophenotype: A twins study.

    Science.gov (United States)

    Korgaonkar, Mayuresh S; Ram, Kaushik; Williams, Leanne M; Gatt, Justine M; Grieve, Stuart M

    2014-08-01

    The resting state default mode network (DMN) has been shown to characterize a number of neurological and psychiatric disorders. Evidence suggests an underlying genetic basis for this network and hence could serve as potential endophenotype for these disorders. Heritability is a defining criterion for endophenotypes. The DMN is measured either using a resting-state functional magnetic resonance imaging (fMRI) scan or by extracting resting state activity from task-based fMRI. The current study is the first to evaluate heritability of this task-derived resting activity. 250 healthy adult twins (79 monozygotic and 46 dizygotic same sex twin pairs) completed five cognitive and emotion processing fMRI tasks. Resting state DMN functional connectivity was derived from these five fMRI tasks. We validated this approach by comparing connectivity estimates from task-derived resting activity for all five fMRI tasks, with those obtained using a dedicated task-free resting state scan in an independent cohort of 27 healthy individuals. Structural equation modeling using the classic twin design was used to estimate the genetic and environmental contributions to variance for the resting-state DMN functional connectivity. About 9-41% of the variance in functional connectivity between the DMN nodes was attributed to genetic contribution with the greatest heritability found for functional connectivity between the posterior cingulate and right inferior parietal nodes (P<0.001). Our data provide new evidence that functional connectivity measures from the intrinsic DMN derived from task-based fMRI datasets are under genetic control and have the potential to serve as endophenotypes for genetically predisposed psychiatric and neurological disorders. Copyright © 2014 Wiley Periodicals, Inc.

  3. The Persistence of Experience: Prior Attentional and Emotional State Affects Network Functioning in a Target Detection Task.

    Science.gov (United States)

    Stern, Emily R; Muratore, Alexandra F; Taylor, Stephan F; Abelson, James L; Hof, Patrick R; Goodman, Wayne K

    2015-09-01

    Efficient, adaptive behavior relies on the ability to flexibly move between internally focused (IF) and externally focused (EF) attentional states. Despite evidence that IF cognitive processes such as event imagination comprise a significant amount of awake cognition, the consequences of internal absorption on the subsequent recruitment of brain networks during EF tasks are unknown. The present functional magnetic resonance imaging (fMRI) study employed a novel attentional state switching task. Subjects imagined positive and negative events (IF task) or performed a working memory task (EF task) before switching to a target detection (TD) task also requiring attention to external information, allowing for the investigation of neural functioning during external attention based on prior attentional state. There was a robust increase of activity in frontal, parietal, and temporal regions during TD when subjects were previously performing the EF compared with IF task, an effect that was most pronounced following negative IF. Additionally, dorsolateral prefrontal cortex was less negatively coupled with ventromedial prefrontal and posterior cingulate cortices during TD following IF compared with EF. These findings reveal the striking consequences for brain activity following immersion in an IF attentional state, which have strong implications for psychiatric disorders characterized by excessive internal focus. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Functional annotation of hierarchical modularity.

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    Kanchana Padmanabhan

    Full Text Available In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology and the association of individual genes or proteins with these concepts (e.g., GO terms, our method will assign a Hierarchical Modularity Score (HMS to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our

  5. Impaired Conflict Resolution and Alerting in Children with ADHD: Evidence from the Attention Network Task (ANT)

    Science.gov (United States)

    Johnson, Katherine A.; Robertson, Ian H.; Barry, Edwina; Mulligan, Aisling; Daibhis, Aoife; Daly, Michael; Watchorn, Amy; Gill, Michael; Bellgrove, Mark A.

    2008-01-01

    Background: An important theory of attention suggests that there are three separate networks that execute discrete cognitive functions. The "alerting" network acquires and maintains an alert state, the "orienting" network selects information from sensory input and the "conflict" network resolves conflict that arises between potential responses.…

  6. The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

    Science.gov (United States)

    Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

    2016-01-15

    Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the

  7. Hierarchical architecture of active knits

    International Nuclear Information System (INIS)

    Abel, Julianna; Luntz, Jonathan; Brei, Diann

    2013-01-01

    Nature eloquently utilizes hierarchical structures to form the world around us. Applying the hierarchical architecture paradigm to smart materials can provide a basis for a new genre of actuators which produce complex actuation motions. One promising example of cellular architecture—active knits—provides complex three-dimensional distributed actuation motions with expanded operational performance through a hierarchically organized structure. The hierarchical structure arranges a single fiber of active material, such as shape memory alloys (SMAs), into a cellular network of interlacing adjacent loops according to a knitting grid. This paper defines a four-level hierarchical classification of knit structures: the basic knit loop, knit patterns, grid patterns, and restructured grids. Each level of the hierarchy provides increased architectural complexity, resulting in expanded kinematic actuation motions of active knits. The range of kinematic actuation motions are displayed through experimental examples of different SMA active knits. The results from this paper illustrate and classify the ways in which each level of the hierarchical knit architecture leverages the performance of the base smart material to generate unique actuation motions, providing necessary insight to best exploit this new actuation paradigm. (paper)

  8. Effects of the Use of Social Network Sites on Task Performance: Toward a Sustainable Performance in a Distracting Work Environment

    Directory of Open Access Journals (Sweden)

    Jinyoung Min

    2017-12-01

    Full Text Available As the use of social network sites (SNS has become increasingly prevalent, its effect on sustainable performance has received much attention. The existing literature has taken either a positive or negative view of SNS, arguing that it either decreases performance by taking time and effort away from work, or increases performance by providing social benefits for enhancing performance. In contrast, this experimental study, investigates how SNS use can disturb or enhance the performance of different types of tasks differently, thus influencing the sustainability of task performance. Based on distraction-conflict theory, this study distinguishes between simple and complex tasks, examines the role of SNS, and analyzes data including electroencephalography data captured by a brain-computer interface. The results show that task performance can be sustainable such that SNS use positively influences performance when participants are engaged in a simple task and influences performance neither positively nor negatively when participants are engaged in a complex task. The study finds the former result is attributable to the positive effect of the psychological arousal induced by SNS use and the latter result to the negative effect of the psychological arousal offsetting the positive effect of reduced stress resulting from SNS use.

  9. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Mathilde de Taffin

    Full Text Available Collier, the single Drosophila COE (Collier/EBF/Olf-1 transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles.

  10. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks

    Science.gov (United States)

    Dubois, Laurence; Bataillé, Laetitia; Painset, Anaïs; Le Gras, Stéphanie; Jost, Bernard; Crozatier, Michèle; Vincent, Alain

    2015-01-01

    Collier, the single Drosophila COE (Collier/EBF/Olf-1) transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col) targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya) is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles. PMID:26204530

  11. A Validated Set of MIDAS V5 Task Network Model Scenarios to Evaluate Nextgen Closely Spaced Parallel Operations Concepts

    Science.gov (United States)

    Gore, Brian Francis; Hooey, Becky Lee; Haan, Nancy; Socash, Connie; Mahlstedt, Eric; Foyle, David C.

    2013-01-01

    The Closely Spaced Parallel Operations (CSPO) scenario is a complex, human performance model scenario that tested alternate operator roles and responsibilities to a series of off-nominal operations on approach and landing (see Gore, Hooey, Mahlstedt, Foyle, 2013). The model links together the procedures, equipment, crewstation, and external environment to produce predictions of operator performance in response to Next Generation system designs, like those expected in the National Airspaces NextGen concepts. The task analysis that is contained in the present report comes from the task analysis window in the MIDAS software. These tasks link definitions and states for equipment components, environmental features as well as operational contexts. The current task analysis culminated in 3300 tasks that included over 1000 Subject Matter Expert (SME)-vetted, re-usable procedural sets for three critical phases of flight; the Descent, Approach, and Land procedural sets (see Gore et al., 2011 for a description of the development of the tasks included in the model; Gore, Hooey, Mahlstedt, Foyle, 2013 for a description of the model, and its results; Hooey, Gore, Mahlstedt, Foyle, 2013 for a description of the guidelines that were generated from the models results; Gore, Hooey, Foyle, 2012 for a description of the models implementation and its settings). The rollout, after landing checks, taxi to gate and arrive at gate illustrated in Figure 1 were not used in the approach and divert scenarios exercised. The other networks in Figure 1 set up appropriate context settings for the flight deck.The current report presents the models task decomposition from the tophighest level and decomposes it to finer-grained levels. The first task that is completed by the model is to set all of the initial settings for the scenario runs included in the model (network 75 in Figure 1). This initialization process also resets the CAD graphic files contained with MIDAS, as well as the embedded

  12. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  13. COMPOSITE METHOD OF RELIABILITY RESEARCH FOR HIERARCHICAL MULTILAYER ROUTING SYSTEMS

    Directory of Open Access Journals (Sweden)

    R. B. Tregubov

    2016-09-01

    Full Text Available The paper deals with the idea of a research method for hierarchical multilayer routing systems. The method represents a composition of methods of graph theories, reliability, probabilities, etc. These methods are applied to the solution of different private analysis and optimization tasks and are systemically connected and coordinated with each other through uniform set-theoretic representation of the object of research. The hierarchical multilayer routing systems are considered as infrastructure facilities (gas and oil pipelines, automobile and railway networks, systems of power supply and communication with distribution of material resources, energy or information with the use of hierarchically nested functions of routing. For descriptive reasons theoretical constructions are considered on the example of task solution of probability determination for up state of specific infocommunication system. The author showed the possibility of constructive combination of graph representation of structure of the object of research and a logic probable analysis method of its reliability indices through uniform set-theoretic representation of its elements and processes proceeding in them.

  14. Augmented Teams -- Assembling Smart Sensors, Intelligent Networks and Humans into Agile Task Groups

    NARCIS (Netherlands)

    Neef, R.M.; Rijn, M. van; Marck, J.W.; Keus, D.

    2009-01-01

    Safety and security environments are full of networked devices. Despite ample research on sensor networks and network technology, there is little practical comprehensive work on how to incorporate such technologies effectively into human-centered teams. This paper discusses the challenge of

  15. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging.

    Science.gov (United States)

    Vidal-Piñeiro, Dídac; Martin-Trias, Pablo; Arenaza-Urquijo, Eider M; Sala-Llonch, Roser; Clemente, Imma C; Mena-Sánchez, Isaias; Bargalló, Núria; Falcón, Carles; Pascual-Leone, Álvaro; Bartrés-Faz, David

    2014-01-01

    Transcranial magnetic stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear. To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders. We applied a paradigm of repetitive TMS - intermittent theta-burst stimulation - over left inferior frontal gyrus in healthy elders (n = 24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans. In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks. The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks.

    Directory of Open Access Journals (Sweden)

    Ryan T Canolty

    Full Text Available Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC or under direct neural control through a brain-machine interface (Brain Control, BC. In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10-45 Hz during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to

  17. A Deep Neural Network Approach to the LifeCLEF 2014 bird task

    NARCIS (Netherlands)

    Koops, Hendrik Vincent; Van Balen, Jan; Wiering, Frans

    2014-01-01

    This paper describes the methods that are used in our submission to the LifeCLEF 2014 Bird task. A segmentation algorithm is created that is capable of segmenting the audio files of the Bird task dataset. These segments are used to select relevant Mel-Frequency Cepstral Coefficients (MFCC) frames

  18. Clustering of resting state networks.

    Directory of Open Access Journals (Sweden)

    Megan H Lee

    Full Text Available The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

  19. Hierarchical ordering with partial pairwise hierarchical relationships on the macaque brain data sets.

    Directory of Open Access Journals (Sweden)

    Woosang Lim

    Full Text Available Hierarchical organizations of information processing in the brain networks have been known to exist and widely studied. To find proper hierarchical structures in the macaque brain, the traditional methods need the entire pairwise hierarchical relationships between cortical areas. In this paper, we present a new method that discovers hierarchical structures of macaque brain networks by using partial information of pairwise hierarchical relationships. Our method uses a graph-based manifold learning to exploit inherent relationship, and computes pseudo distances of hierarchical levels for every pair of cortical areas. Then, we compute hierarchy levels of all cortical areas by minimizing the sum of squared hierarchical distance errors with the hierarchical information of few cortical areas. We evaluate our method on the macaque brain data sets whose true hierarchical levels are known as the FV91 model. The experimental results show that hierarchy levels computed by our method are similar to the FV91 model, and its errors are much smaller than the errors of hierarchical clustering approaches.

  20. Treatment strategies for coronary in-stent restenosis: systematic review and hierarchical Bayesian network meta-analysis of 24 randomised trials and 4880 patients

    Science.gov (United States)

    Giacoppo, Daniele; Gargiulo, Giuseppe; Aruta, Patrizia; Capranzano, Piera; Tamburino, Corrado

    2015-01-01

    Study question What is the most safe and effective interventional treatment for coronary in-stent restenosis? Methods In a hierarchical Bayesian network meta-analysis, PubMed, Embase, Scopus, Cochrane Library, Web of Science, ScienceDirect, and major scientific websites were screened up to 10 August 2015. Randomised controlled trials of patients with any type of coronary in-stent restenosis (either of bare metal stents or drug eluting stents; and either first or recurrent instances) were included. Trials including multiple treatments at the same time in the same group or comparing variants of the same intervention were excluded. Primary endpoints were target lesion revascularisation and late lumen loss, both at six to 12 months. The main analysis was complemented by network subanalyses, standard pairwise comparisons, and subgroup and sensitivity analyses. Study answer and limitations Twenty four trials (4880 patients), including seven interventional treatments, were identified. Compared with plain balloons, bare metal stents, brachytherapy, rotational atherectomy, and cutting balloons, drug coated balloons and drug eluting stents were associated with a reduced risk of target lesion revascularisation and major adverse cardiac events, and with reduced late lumen loss. Treatment ranking indicated that drug eluting stents had the highest probability (61.4%) of being the most effective for target lesion vascularisation; drug coated balloons were similarly indicated as the most effective treatment for late lumen loss (probability 70.3%). The comparative efficacy of drug coated balloons and drug eluting stents was similar for target lesion revascularisation (summary odds ratio 1.10, 95% credible interval 0.59 to 2.01) and late lumen loss reduction (mean difference in minimum lumen diameter 0.04 mm, 95% credible interval −0.20 to 0.10). Risks of death, myocardial infarction, and stent thrombosis were comparable across all treatments, but these analyses were limited by a

  1. An Exploratory Investigation of Functional Network Connectivity of Empathy and Default Mode Networks in a Free-Viewing Task.

    Science.gov (United States)

    Vemuri, Kavita; Surampudi, Bapi Raju

    2015-08-01

    This study reports dynamic functional network connectivity (dFNC) analysis on time courses of putative empathy networks-cognitive, emotional, and motor-and the default mode network (DMN) identified from independent components (ICs) derived by the group independent component analysis (ICA) method. The functional magnetic resonance imaging (fMRI) data were collected from 15 subjects watching movies of three genres, an animation (S1), Indian Hindi (S2), and a Hollywood English (S3) movie. The hypothesis of the study is that empathic engagement in a movie narrative would modulate the activation with the DMN. The clippings were individually rated for emotional expressions, context, and empathy self-response by the fMRI subjects post scanning and by 40 participants in an independent survey who rated at four time intervals in each clipping. The analysis illustrates the following: (a) the ICA method separated ICs with areas reported for empathy response and anterior/posterior DMNs. An IC indicating insula region activation reported to be crucial for the emotional empathy network was separated for S2 and S3 movies only, but not for S1, (b) the dFNC between DMN and ICs corresponding to cognitive empathy network showed higher positive periodical fluctuating correlations for all three movies, while ICs with areas crucial to motor or emotional empathy display lower positive or negative correlation values with no distinct periodicity. A possible explanation for the lower values and anticorrelation between the DMN and emotional empathy networks could possibly be inhibition due to internal self-reflections, attributed to DMN, while processing and preparing a response to external emotional content. The positive higher correlation values for cognitive empathy networks may reflect a functional overlap with DMN for enhanced internal self-reflections, inferring beliefs and intentions about the 'other', all triggered by the external stimuli. The findings are useful in the study of

  2. Hierarchical Rhetorical Sentence Categorization for Scientific Papers

    Science.gov (United States)

    Rachman, G. H.; Khodra, M. L.; Widyantoro, D. H.

    2018-03-01

    Important information in scientific papers can be composed of rhetorical sentences that is structured from certain categories. To get this information, text categorization should be conducted. Actually, some works in this task have been completed by employing word frequency, semantic similarity words, hierarchical classification, and the others. Therefore, this paper aims to present the rhetorical sentence categorization from scientific paper by employing TF-IDF and Word2Vec to capture word frequency and semantic similarity words and employing hierarchical classification. Every experiment is tested in two classifiers, namely Naïve Bayes and SVM Linear. This paper shows that hierarchical classifier is better than flat classifier employing either TF-IDF or Word2Vec, although it increases only almost 2% from 27.82% when using flat classifier until 29.61% when using hierarchical classifier. It shows also different learning model for child-category can be built by hierarchical classifier.

  3. Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

    Science.gov (United States)

    Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Heute, U; Deuschl, G; Raethjen, J; Muthuraman, Muthuraman

    2016-09-01

    Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a

  4. Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks.

    Science.gov (United States)

    Hyatt, Christopher J; Calhoun, Vince D; Pearlson, Godfrey D; Assaf, Michal

    2015-08-01

    The ability to attribute mental states to others, or "mentalizing," is posited to involve specific subnetworks within the overall default mode network (DMN), but this question needs clarification. To determine which default mode (DM) subnetworks are engaged by mentalizing processes, we assessed task-related recruitment of DM subnetworks. Spatial independent component analysis (sICA) applied to fMRI data using relatively high-order model (75 components). Healthy participants (n = 53, ages 17-60) performed two fMRI tasks: an interactive game involving mentalizing (Domino), a semantic memory task (SORT), and a resting state fMRI scan. sICA of the two tasks split the DMN into 10 subnetworks located in three core regions: medial prefrontal cortex (mPFC; five subnetworks), posterior cingulate/precuneus (PCC/PrC; three subnetworks), and bilateral temporoparietal junction (TPJ). Mentalizing events increased recruitment in five of 10 DM subnetworks, located in all three core DMN regions. In addition, three of these five DM subnetworks, one dmPFC subnetwork, one PCC/PrC subnetwork, and the right TPJ subnetwork, showed reduced recruitment by semantic memory task events. The opposing modulation by the two tasks suggests that these three DM subnetworks are specifically engaged in mentalizing. Our findings, therefore, suggest the unique involvement of mentalizing processes in only three of 10 DM subnetworks, and support the importance of the dmPFC, PCC/PrC, and right TPJ in mentalizing as described in prior studies. © 2015 Wiley Periodicals, Inc.

  5. Increased engagement of the cognitive control network associated with music training in children during an fMRI Stroop task.

    Science.gov (United States)

    Sachs, Matthew; Kaplan, Jonas; Der Sarkissian, Alissa; Habibi, Assal

    2017-01-01

    Playing a musical instrument engages various sensorimotor processes and draws on cognitive capacities collectively termed executive functions. However, while music training is believed to associated with enhancements in certain cognitive and language abilities, studies that have explored the specific relationship between music and executive function have yielded conflicting results. As part of an ongoing longitudinal study, we investigated the effects of music training on executive function using fMRI and several behavioral tasks, including the Color-Word Stroop task. Children involved in ongoing music training (N = 14, mean age = 8.67) were compared with two groups of comparable general cognitive abilities and socioeconomic status, one involved in sports ("sports" group, N = 13, mean age = 8.85) and another not involved in music or sports ("control" group, N = 17, mean age = 9.05). During the Color-Word Stroop task, children with music training showed significantly greater bilateral activation in the pre-SMA/SMA, ACC, IFG, and insula in trials that required cognitive control compared to the control group, despite no differences in performance on behavioral measures of executive function. No significant differences in brain activation or in task performance were found between the music and sports groups. The results suggest that systematic extracurricular training, particularly music-based training, is associated with changes in the cognitive control network in the brain even in the absence of changes in behavioral performance.

  6. Increased engagement of the cognitive control network associated with music training in children during an fMRI Stroop task.

    Directory of Open Access Journals (Sweden)

    Matthew Sachs

    Full Text Available Playing a musical instrument engages various sensorimotor processes and draws on cognitive capacities collectively termed executive functions. However, while music training is believed to associated with enhancements in certain cognitive and language abilities, studies that have explored the specific relationship between music and executive function have yielded conflicting results. As part of an ongoing longitudinal study, we investigated the effects of music training on executive function using fMRI and several behavioral tasks, including the Color-Word Stroop task. Children involved in ongoing music training (N = 14, mean age = 8.67 were compared with two groups of comparable general cognitive abilities and socioeconomic status, one involved in sports ("sports" group, N = 13, mean age = 8.85 and another not involved in music or sports ("control" group, N = 17, mean age = 9.05. During the Color-Word Stroop task, children with music training showed significantly greater bilateral activation in the pre-SMA/SMA, ACC, IFG, and insula in trials that required cognitive control compared to the control group, despite no differences in performance on behavioral measures of executive function. No significant differences in brain activation or in task performance were found between the music and sports groups. The results suggest that systematic extracurricular training, particularly music-based training, is associated with changes in the cognitive control network in the brain even in the absence of changes in behavioral performance.

  7. Sustainable Energy Solutions Task 1.0: Networked Monitoring and Control of Small Interconnected Wind Energy Systems

    Energy Technology Data Exchange (ETDEWEB)

    edu, Janet. twomey@wichita. [Wichita State Univ., KS (United States)

    2010-04-30

    This report presents accomplishments, results, and future work for one task of five in the Wichita State University Sustainable Energy Solutions Project: To develop a scale model laboratory distribution system for research into questions that arise from networked control and monitoring of low-wind energy systems connected to the AC distribution system. The lab models developed under this task are located in the Electric Power Quality Lab in the Engineering Research Building on the Wichita State University campus. The lab system consists of four parts: 1. A doubly-fed induction generator 2. A wind turbine emulator 3. A solar photovoltaic emulator, with battery energy storage 4. Distribution transformers, lines, and other components, and wireless and wired communications and control These lab elements will be interconnected and will function together to form a complete testbed for distributed resource monitoring and control strategies and smart grid applications testing. Development of the lab system will continue beyond this project.

  8. WHAT DOES A WORD ALTER? THE EFFECT OF CONCEPTUAL NETWORKS ON THE DIMENSIONAL CHANGE SORTING TASK

    Directory of Open Access Journals (Sweden)

    Tolga Yildiz

    2013-06-01

    Full Text Available The purpose of the research was to examine the cognitive flexibility of three-year-olds, who usually persevere in the Dimensional Change Card Sort task, when a constant representation was referred in the classic instruction. In accordance with this purpose, the Dimensional Change Pencil Sort task was developed and used in the current study that 13 three-year-olds participated in. Findings seemed to support partially the hypothesis predicted that the kids could achieve the task in terms of rule use and mental representational flexibilities between length and color at the post switch phase in which situation classification dimensions were referred over a real and constant object (pencil instead of a card. This result drew attention to the conceptual and perceptual mediation roles of language and objects respectively in terms of the cognitive flexibility literature.

  9. The influence of gender, age, matriline and hierarchical rank on individual social position, role and interactional patterns in Macaca sylvanus at ‘La Forêt des singes’: A multilevel social network approach

    Directory of Open Access Journals (Sweden)

    Sebastian OROZCO SOSA

    2016-04-01

    Full Text Available A society is a complex system composed of individuals that can be characterized by their own attributes that influence their behaviors. In this study, a specific analytical protocol based on social network analysis was adopted to investigate the influence of four attributes (gender, age, matriline, and hierarchical rank on affiliative (allogrooming and agonistic networks in a non-human primate species, Macaca sylvanus, at the park La Forêt des Singes in France. The results show significant differences with respect to the position (i.e. centric, peripheral and role (i.e. implication in the network cohesiveness of an individual within a social network and hence interactional patterns. Females are more central, more active, and have a denser ego network in the affiliative social network tan males; thus, they contribute in a greater way to the cohesive structure of the network. High-ranking individuals are likely to receive fewer agonistic behaviors than low-ranking individuals, and high-ranking females receive more allogrooming. I also observe homophily for affiliative interactions regarding all attributes and homophily for agonistic interactions regarding gender and age. Revealing the positions, the roles, and the interactional behavioral patterns of individuals can help understand the mechanisms that shape the overall structure of a social network.

  10. The Influence of Gender, Age, Matriline and Hierarchical Rank on Individual Social Position, Role and Interactional Patterns in Macaca sylvanus at 'La Forêt des Singes': A Multilevel Social Network Approach.

    Science.gov (United States)

    Sosa, Sebastian

    2016-01-01

    A society is a complex system composed of individuals that can be characterized by their own attributes that influence their behaviors. In this study, a specific analytical protocol based on social network analysis was adopted to investigate the influence of four attributes (gender, age, matriline, and hierarchical rank) on affiliative (allogrooming) and agonistic networks in a non-human primate species, Macaca sylvanus, at the park La Forêt des Singes in France. The results show significant differences with respect to the position (i.e., centric, peripheral) and role (i.e., implication in the network cohesiveness) of an individual within a social network and hence interactional patterns. Females are more central, more active, and have a denser ego network in the affiliative social network tan males; thus, they contribute in a greater way to the cohesive structure of the network. High-ranking individuals are likely to receive fewer agonistic behaviors than low-ranking individuals, and high-ranking females receive more allogrooming. I also observe homophily for affiliative interactions regarding all attributes and homophily for agonistic interactions regarding gender and age. Revealing the positions, the roles, and the interactional behavioral patterns of individuals can help understand the mechanisms that shape the overall structure of a social network.

  11. PROBLEMS OF ROUTE NETWORK AND AIRCRAFT FLEET OPTIMIZATION AS A SPECIFIC TASK OF AIRLINE STRATEGIC PLANNING

    Directory of Open Access Journals (Sweden)

    M. A. Karakuts

    2015-01-01

    Full Text Available The basic problems of route network and aircraft fleet optimization and its role in airline strategic planning are considered. Measures to improve the methods of its implementation are proposed.

  12. Defense Science Board Task Force on Military Satellite Communication and Tactical Networking. Executive Summary

    Science.gov (United States)

    2017-03-01

    Interface Processor BCT Brigade Combat Team BFT Blue Force Tracking BLOS Beyond Line-of-Sight C2 Command And Control C2E Communications in...Satellite Communications and Tactical Networking Appendix D-2 GIG Global Information Grid GMR Ground Mobile Radio GPS Global Positioning System...System SIPRNet Secret Internet Protocol Router Network SITREPS Situational Reports SMART -T Secure Mobile Anti-Jam Reliable Tactical Terminal SMC Space

  13. Attentional network task in schizophrenic patients and theirs unaffected first degree relatives: a potential endofenotype.

    Science.gov (United States)

    López, S Guerra; Fuster, J Iglesias; Reyes, M Martín; Collazo, T M Bravo; Quiñones, R Mendoza; Berazain, A Reyes; Rodríguez, M A Pedroso; Días de Villarvilla, T; Bobés, M Antonieta; Valdés-Sosa, M

    2011-01-01

    In recent years, reports of attentional deficits in schizophrenic patients and in their biological relatives have rapidly increased, including an important effort to search for the endophenotypes in order to link specific genes to this illness. Posner et al. developed a test, the Attention Network Test (ANT), to study the neural networks. This test provides a separate measure for each one of the three anatomically-defined attention networks (alerting, orienting and executive control). In this paper, we investigate the attentional performance in 32 schizophrenic patients, 29 unaffected first degree relatives and 29 healthy controls using the ANT through a study of family association. We have studied the efficiency of the segregated executive control, alerting and orienting networks by measuring how response latencies (reaction time) were modified by the cue position and the flanking stimuli. We also studied the familial association of these attentional alterations. The ANOVA revealed main effects of flanker and cue condition and a significant interaction effect between flanker and groups studied. The schizophrenic patients and their relatives had a longer median reaction time than the control group. The probands and their relatives significantly differed from the healthy controls in terms of their conflict resolution; however, the alerting network appeared to be conserved. Our results support the thesis of a specific attentional deficit in schizophrenia and show the segregation of the three attentional networks. The family association of these reported alterations supports the idea of a potential endophenotype in schizophrenia.

  14. Optimal usage of computing grid network in the fields of nuclear fusion computing task

    International Nuclear Information System (INIS)

    Tenev, D.

    2006-01-01

    Nowadays the nuclear power becomes the main source of energy. To make its usage more efficient, the scientists created complicated simulation models, which require powerful computers. The grid computing is the answer to powerful and accessible computing resources. The article observes, and estimates the optimal configuration of the grid environment in the fields of the complicated nuclear fusion computing tasks. (author)

  15. Hierarchical nonlinear dynamics of human attention.

    Science.gov (United States)

    Rabinovich, Mikhail I; Tristan, Irma; Varona, Pablo

    2015-08-01

    Attention is the process of focusing mental resources on a specific cognitive/behavioral task. Such brain dynamics involves different partially overlapping brain functional networks whose interconnections change in time according to the performance stage, and can be stimulus-driven or induced by an intrinsically generated goal. The corresponding activity can be described by different families of spatiotemporal discrete patterns or sequential dynamic modes. Since mental resources are finite, attention modalities compete with each other at all levels of the hierarchy, from perception to decision making and behavior. Cognitive activity is a dynamical process and attention possesses some universal dynamical characteristics. Thus, it is time to apply nonlinear dynamical theory for the description and prediction of hierarchical attentional tasks. Such theory has to include the analyses of attentional control stability, the time cost of attention switching, the finite capacity of informational resources in the brain, and the normal and pathological bifurcations of attention sequential dynamics. In this paper we have integrated today's knowledge, models and results in these directions. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Real-time Human Activity Recognition using a Body Sensor Network

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Chen, Hanhua

    2010-01-01

    Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this mo...

  17. Hierarchical Broadcasting in the Future Mobile Internet

    NARCIS (Netherlands)

    Hesselman, C.E.W.; Eertink, E.H.; Fernandez, Milagros; Crnkovic, Ivica; Fohler, Gerhard; Griwodz, Carsten; Plagemann, Thomas; Gruenbacher, Paul

    2002-01-01

    We describe an architecture for the hierarchical distribution of multimedia broadcasts in the future mobile Internet. The architecture supports network as well as application-layer mobility solutions, and uses stream control functions that are influenced by available network resources, user-defined

  18. Summary report on the Solar Consumer Assurance Network (SOLCAN) Program Planning Task in the southern region

    Energy Technology Data Exchange (ETDEWEB)

    Browne, M. B. [comp.

    1981-03-15

    The goal of the SOLCAN Program Planning Task is to assist in the development, at the state and local levels, of consumer assurance approaches that will support the accelerated adoption and effective use of new products promoted by government incentives to consumers to meet our nation's energy needs. The task includes state-conducted evaluations and state SOLCAN meetings to identify consumer assurance mechanisms, assess their effectiveness, and identify and describe alternative means for strengthening consumer and industry assurance in each state. Results of the SOLCAN process are presented, including: a Solar Consumer Protection State Assessment Guide; State Solar Consumer Assurance Resources for Selected States; State Solar Consumer Protection Assessment Interviews for Florida; and state SOLCAN meeting summaries and participants. (LEW)

  19. Functional network centrality in obesity: A resting-state and task fMRI study.

    Science.gov (United States)

    García-García, Isabel; Jurado, María Ángeles; Garolera, Maite; Marqués-Iturria, Idoia; Horstmann, Annette; Segura, Bàrbara; Pueyo, Roser; Sender-Palacios, María José; Vernet-Vernet, Maria; Villringer, Arno; Junqué, Carme; Margulies, Daniel S; Neumann, Jane

    2015-09-30

    Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  20. Mission-Centered Network Models: Defending Mission-Critical Tasks From Deception

    Science.gov (United States)

    2015-09-29

    celebrities ). In military applications, networked operations offer an effective way to reduce the footprint of a force, but become a center of gravity...from,-used-by-trust-algorithms-to-assess-quality-and- trustworthiness - •  Technical&challenge:-Developing-standard-representa3ons-for-provenance-that

  1. Dysfunctional default mode network and executive control network in people with Internet gaming disorder: Independent component analysis under a probability discounting task.

    Science.gov (United States)

    Wang, L; Wu, L; Lin, X; Zhang, Y; Zhou, H; Du, X; Dong, G

    2016-04-01

    The present study identified the neural mechanism of risky decision-making in Internet gaming disorder (IGD) under a probability discounting task. Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects (22.2 ± 3.08 years) and 21 healthy controls (HC, 22.8 ± 3.5 years). For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network (DMN) and less engagement in the executive control network (ECN) than HC when making the risky decisions. Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates. The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  2. Using a million cell simulation of the cerebellum: network scaling and task generality.

    Science.gov (United States)

    Li, Wen-Ke; Hausknecht, Matthew J; Stone, Peter; Mauk, Michael D

    2013-11-01

    Several factors combine to make it feasible to build computer simulations of the cerebellum and to test them in biologically realistic ways. These simulations can be used to help understand the computational contributions of various cerebellar components, including the relevance of the enormous number of neurons in the granule cell layer. In previous work we have used a simulation containing 12000 granule cells to develop new predictions and to account for various aspects of eyelid conditioning, a form of motor learning mediated by the cerebellum. Here we demonstrate the feasibility of scaling up this simulation to over one million granule cells using parallel graphics processing unit (GPU) technology. We observe that this increase in number of granule cells requires only twice the execution time of the smaller simulation on the GPU. We demonstrate that this simulation, like its smaller predecessor, can emulate certain basic features of conditioned eyelid responses, with a slight improvement in performance in one measure. We also use this simulation to examine the generality of the computation properties that we have derived from studying eyelid conditioning. We demonstrate that this scaled up simulation can learn a high level of performance in a classic machine learning task, the cart-pole balancing task. These results suggest that this parallel GPU technology can be used to build very large-scale simulations whose connectivity ratios match those of the real cerebellum and that these simulations can be used guide future studies on cerebellar mediated tasks and on machine learning problems. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

    Science.gov (United States)

    2017-01-01

    Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

  4. The task of the Smart Grid Network. Summary and recommendations; Denmark; Smart Grid Netvaerkets arbejde. Sammenfatning og anbefalinger

    Energy Technology Data Exchange (ETDEWEB)

    Lidegaard, M.

    2011-10-15

    The Smart Grid Network was established in 2010 by the Danish climate and energy minister tasked with developing recommendations for future actions and initiatives that make it possible to handle up to 50% electricity from wind energy in the power system in 2020. The present report presents a summary of the network's main recommendations. Smart Grid will not be realized without ensuring reasonable conditions for actors in the system. It is essential to establish a clear market model with clear roles and responsibilities. Additionally there is a need for development and implementation of a future communication and control concept, which makes it possible to achieve the best possible interaction between the management of power system, power generation and electricity consumption. The future demands that both the commercial and technical data communications paths and systems will be expanded and supplemented with connections for significantly more renewable energy production at all levels in the grid. And most importantly there must be established entirely new interoperable communication structures for both commercial and technical utilization of the consumption part of the power system. In order to realize an effective deployment of Smart Grid in 2020 with up to 50 % of renewable energy production there is a need to implement a number of initiatives. The Smart Grid Network identifies nine main recommendations. (LN)

  5. Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study.

    Science.gov (United States)

    Lavigne, Katie M; Woodward, Todd S

    2018-04-01

    Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation. © 2017 Wiley Periodicals, Inc.

  6. Aberrant coupling within and across the default mode, task-positive, and salience network in subjects at risk for psychosis.

    Science.gov (United States)

    Wotruba, Diana; Michels, Lars; Buechler, Roman; Metzler, Sibylle; Theodoridou, Anastasia; Gerstenberg, Miriam; Walitza, Susanne; Kollias, Spyros; Rössler, Wulf; Heekeren, Karsten

    2014-09-01

    The task-positive network (TPN) is anticorrelated with activity in the default mode network (DMN), and possibly reflects competition between the processing of external and internal information, while the salience network (SN) is pivotal in regulating TPN and DMN activity. Because abnormal functional connectivity in these networks has been related to schizophrenia, we tested whether alterations are also evident in subjects at risk for psychosis. Resting-state functional magnetic resonance imaging was tested in 28 subjects with basic symptoms reporting subjective cognitive-perceptive symptoms; 19 with attenuated or brief, limited psychotic symptoms; and 29 matched healthy controls. We characterized spatial differences in connectivity patterns, as well as internetwork connectivity. Right anterior insula (rAI) was selected as seed region for identifying the SN; medioprefrontal cortex (MPFC) for the DMN and TPN. The 3 groups differed in connectivity patterns between the MPFC and right dorsolateral prefrontal cortex (rDLPFC), and between the rAI and posterior cingulate cortex (PCC). In particular, the typically observed antagonistic relationship in MPFC-rDLPFC, rAI-PCC, and internetwork connectivity of DMN-TPN was absent in both at-risk groups. Notably, those connectivity patterns were associated with symptoms related to reality distortions, whereas enhanced connectivity strengths of MPFC-rDLPFC and TPN-DMN were related to poor performance in cognitive functions. We propose that the loss of a TPN-DMN anticorrelation, accompanied by an aberrant spatial extent in the DMN, TPN, and SN in the psychosis risk state, reflects the confusion of internally and externally focused states and disturbance of cognition, as seen in psychotic disorders. © The Author 2013. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  7. Analysis hierarchical model for discrete event systems

    Science.gov (United States)

    Ciortea, E. M.

    2015-11-01

    The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

  8. SOME QUESTIONS OF THE GRID AND NEURAL NETWORK MODELING OF AIRPORT AVIATION SECURITY CONTROL TASKS

    Directory of Open Access Journals (Sweden)

    N. Elisov Lev

    2017-01-01

    Full Text Available The authors’ original problem-solution-approach concerning aviation security management in civil aviation apply- ing parallel calculation processes method and the usage of neural computers is considered in this work. The statement of secure environment modeling problems for grid models and with the use of neural networks is presented. The research sub- ject area of this article is airport activity in the field of civil aviation, considered in the context of aviation security, defined as the state of aviation security against unlawful interference with the aviation field. The key issue in this subject area is aviation safety provision at an acceptable level. In this case, airport security level management becomes one of the main objectives of aviation security. Aviation security management is organizational-regulation in modern systems that can no longer correspond to changing requirements, increasingly getting complex and determined by external and internal envi- ronment factors, associated with a set of potential threats to airport activity. Optimal control requires the most accurate identification of management parameters and their quantitative assessment. The authors examine the possibility of applica- tion of mathematical methods for the modeling of security management processes and procedures in their latest works. Par- allel computing methods and network neurocomputing for modeling of airport security control processes are examined in this work. It is shown that the methods’ practical application of the methods is possible along with the decision support system, where the decision maker plays the leading role.

  9. Corporate Data Network (CDN) data requirements task. Enterprise Model. Volume 1

    International Nuclear Information System (INIS)

    1985-11-01

    The NRC has initiated a multi-year program to centralize its information processing in a Corporate Data Network (CDN). The new information processing environment will include shared databases, telecommunications, office automation tools, and state-of-the-art software. Touche Ross and Company was contracted with to perform a general data requirements analysis for shared databases and to develop a preliminary plan for implementation of the CDN concept. The Enterprise Model (Vol. 1) provided the NRC with agency-wide information requirements in the form of data entities and organizational demand patterns as the basis for clustering the entities into logical groups. The Data Dictionary (Vol. 2) provided the NRC with definitions and example attributes and properties for each entity. The Data Model (Vol. 3) defined logical databases and entity relationships within and between databases. The Preliminary Strategic Data Plan (Vol. 4) prioritized the development of databases and included a workplan and approach for implementation of the shared database component of the Corporate Data Network

  10. A Network of Multi-Tasking Proteins at the DNA Replication Fork Preserves Genome Stability.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available To elucidate the network that maintains high fidelity genome replication, we have introduced two conditional mutant alleles of DNA2, an essential DNA replication gene, into each of the approximately 4,700 viable yeast deletion mutants and determined the fitness of the double mutants. Fifty-six DNA2-interacting genes were identified. Clustering analysis of genomic synthetic lethality profiles of each of 43 of the DNA2-interacting genes defines a network (consisting of 322 genes and 876 interactions whose topology provides clues as to how replication proteins coordinate regulation and repair to protect genome integrity. The results also shed new light on the functions of the query gene DNA2, which, despite many years of study, remain controversial, especially its proposed role in Okazaki fragment processing and the nature of its in vivo substrates. Because of the multifunctional nature of virtually all proteins at the replication fork, the meaning of any single genetic interaction is inherently ambiguous. The multiplexing nature of the current studies, however, combined with follow-up supporting experiments, reveals most if not all of the unique pathways requiring Dna2p. These include not only Okazaki fragment processing and DNA repair but also chromatin dynamics.

  11. Corporate Data Network (CDN). Data Requirements Task. Preliminary Strategic Data Plan. Volume 4

    International Nuclear Information System (INIS)

    1985-11-01

    The NRC has initiated a multi-year program to centralize its information processing in a Corporate Data Network (CDN). The new information processing environment will include shared databases, telecommunications, office automation tools, and state-of-the-art software. Touche Ross and Company was contracted with to perform a general data requirements analysis for shared databases and to develop a preliminary plan for implementation of the CDN concept. The Enterprise Model (Vol. 1) provided the NRC with agency-wide information requirements in the form of data entities and organizational demand patterns as the basis for clustering the entities into logical groups. The Data Dictionary (Vol.2) provided the NRC with definitions and example attributes and properties for each entity. The Data Model (Vol.3) defined logical databases and entity relationships within and between databases. The Preliminary Strategic Data Plan (Vol. 4) prioritized the development of databases and included a workplan and approach for implementation of the shared database component of the Corporate Data Network

  12. Resting State Default Mode Network Connectivity, Dual Task Performance, Gait Speed, and Postural Sway in Older Adults with Mild Cognitive Impairment.

    Science.gov (United States)

    Crockett, Rachel A; Hsu, Chun Liang; Best, John R; Liu-Ambrose, Teresa

    2017-01-01

    Aging is associated with an increased risk of falling. In particular, older adults with mild cognitive impairment (MCI) are more vulnerable to falling compared with their healthy counterparts. Major contributors to this increased falls risk include a decline in dual task performance, gait speed, and postural sway. Recent evidence highlights the potential influence of the default mode network (DMN), the frontoparietal network (FPN), and the supplementary motor area (SMA) on dual task performance, gait speed, and postural sway. The DMN is active during rest and deactivates during task-oriented processes, to maintain attention and stay on task. The FPN and SMA are involved in top-down attentional control, motor planning, and motor execution. The DMN shows less deactivation during task in older adults with MCI. This lack of deactivation is theorized to increase competition for resources between the DMN and task-related brain regions (e.g., the FPN and SMA), increasing distraction from the task and reducing task performance. However, no study has yet investigated the relationship between the between-network connectivity of the DMN with these regions and dual task walking, gait speed or postural sway. We hypothesized that greater functional connectivity both within the DMN and between DMN-FPN and DMN-SMA, will be associated with poorer performance during dual task walking, slower gait speed, and greater postural sway in older adults with MCI. Forty older adults with MCI were measured on a dual task-walking paradigm, gait speed over a 4-m walk, and postural sway using a sway-meter. Greater within-DMN connectivity was significantly correlated with poorer dual task performance. Furthermore, greater inter-network connectivity between the DMN and SMA was significantly correlated with slower gait speed and greater postural sway on the eyes open floor sway task. Thus, greater resting state DMN functional connectivity may be an underlying neural mechanism for reduced dual task

  13. Using of P2P Networks for Acceleration of RTE Tasks Solving

    Directory of Open Access Journals (Sweden)

    Adrian Iftene

    2008-07-01

    Full Text Available In the last years the computational Grids have become an important research area in large-scale scientific and engineering research. Our approach is based on Peer-to-peer (P2P networks, which are recognized as one of most used architectures in order to achieve scalability in key components of Grid systems. The main scope in using of a computational Grid was to improve the computational speed of systems that solve complex problems from Natural Language processing field. We will see how can be implemented a computational Grid using the P2P model, and how can be used SMB protocol for file transfer. After that we will see how we can use this computational Grid, in order to improve the computational speed of a system used in RTE competition [1], a new complex challenge from Natural Language processing field.

  14. Main report for the task of the Smart Grid Network; Denmark; Hovedrapport for Smart Grid Netvaerkets arbejde

    Energy Technology Data Exchange (ETDEWEB)

    Lidegaard, M.

    2011-07-01

    The Smart Grid Network was established in 2010 by the Danish climate and energy minister tasked with developing recommendations for future actions and initiatives that make it possible to handle up to 50% electricity from wind energy in the power system in 2020. Smart Grid will not be realized without ensuring reasonable conditions for actors in the system. It is essential to establish a clear market model with clear roles and responsibilities. Additionally there is a need for development and implementation of a future communication and control concept, which makes it possible to achieve the best possible interaction between the management of power system, power generation and electricity consumption. The future demands that both the commercial and technical data communications paths and systems will be expanded and supplemented with connections for significantly more renewable energy production at all levels in the grid. And most importantly there must be established entirely new interoperable communication structures for both commercial and technical utilization of the consumption part of the power system. In order to realize an effective deployment of Smart Grid in 2020 with up to 50 % of renewable energy production there is a need to implement a number of initiatives. The present report presents the network's nine main recommendations and 35 specific sub-recommendations. (LN)

  15. Patterns of task and network actions performed by navigators to facilitate cancer care.

    Science.gov (United States)

    Clark, Jack A; Parker, Victoria A; Battaglia, Tracy A; Freund, Karen M

    2014-01-01

    Patient navigation is a widely implemented intervention to facilitate access to care and reduce disparities in cancer care, but the activities of navigators are not well characterized. The aim of this study is to describe what patient navigators actually do and explore patterns of activity that clarify the roles they perform in facilitating cancer care. We conducted field observations of nine patient navigation programs operating in diverse health settings of the national patient navigation research program, including 34 patient navigators, each observed an average of four times. Trained observers used a structured observation protocol to code as they recorded navigator actions and write qualitative field notes capturing all activities in 15-minute intervals during observations ranging from 2 to 7 hours; yielding a total of 133 observations. Rates of coded activity were analyzed using numerical cluster analysis of identified patterns, informed by qualitative analysis of field notes. Six distinct patterns of navigator activity were identified, which differed most relative to how much time navigators spent directly interacting with patients and how much time they spent dealing with medical records and documentation tasks. Navigator actions reveal a complex set of roles in which navigators both provide the direct help to patients denoted by their title and also carry out a variety of actions that function to keep the health system operating smoothly. Working to navigate patients through complex health services entails working to repair the persistent challenges of health services that can render them inhospitable to patients. The organizations that deploy navigators might learn from navigators' efforts and explore alternative approaches, structures, or systems of care in addressing both the barriers patients face and the complex solutions navigators create in helping patients.

  16. Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

    Science.gov (United States)

    Fels, Meike; Bauer, Robert; Gharabaghi, Alireza

    2015-08-01

    Objective. Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these interventions and the related cortical activation patterns. Approach. We assessed the perceived workload with the NASA Task Load Index in twenty-one subjects who were exposed to two different feedback tasks in a cross-over design: (i) brain-robot interface (BRI) with haptic/proprioceptive feedback of sensorimotor oscillations related to motor imagery, and (ii) control of neuromuscular activity with feedback of the electromyography (EMG) of the same hand. We also used electroencephalography to examine the cortical activation patterns beforehand in resting state and during the training session of each task. Main results. The workload profile of BRI feedback differed from EMG feedback and was particularly characterized by the experience of frustration. The frustration level was highly correlated across tasks, suggesting subject-related relevance of this workload component. Those subjects who were specifically challenged by the respective tasks could be detected by an interhemispheric alpha-band network in resting state before the training and by their sensorimotor theta-band activation pattern during the exercise. Significance. Neurophysiological profiles in resting state and during the exercise may provide task-independent workload markers for monitoring and matching participants’ ability and task difficulty of neurofeedback interventions.

  17. Distributed hierarchical radiation monitoring system

    International Nuclear Information System (INIS)

    Barak, D.

    1985-01-01

    A solution to the problem of monitoring the radiation levels in and around a nuclear facility is presented in this paper. This is a private case of a large scale general purpose data acqisition system with high reliability, availability and short maintenance time. The physical layout of the detectors in the plant, and the strict control demands dictated a distributed and hierarchical system. The system is comprised of three levels, each level contains modules. Level one contains the Control modules which collects data from groups of detectors and executes emergency local control tasks. In level two are the Group controllers which concentrate data from the Control modules, and enable local display and communication. The system computer is in level three, enabling the plant operator to receive information from the detectors and execute control tasks. The described system was built and is operating successfully for about two years. (author)

  18. Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

    Science.gov (United States)

    Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

    2016-01-01

    The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

  19. Task-Related Edge Density (TED-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

    Directory of Open Access Journals (Sweden)

    Gabriele Lohmann

    Full Text Available The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED. TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

  20. Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

    Science.gov (United States)

    Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

    2016-01-01

    The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

  1. BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

    Science.gov (United States)

    Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

    2009-02-15

    BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

  2. Hierarchical classification with a competitive evolutionary neural tree.

    Science.gov (United States)

    Adams, R G.; Butchart, K; Davey, N

    1999-04-01

    A new, dynamic, tree structured network, the Competitive Evolutionary Neural Tree (CENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage that the CENT offers over other hierarchical competitive networks is its ability to self determine the number, and structure, of the competitive nodes in the network, without the need for externally set parameters. The network produces stable classificatory structures by halting its growth using locally calculated heuristics. The results of network simulations are presented over a range of data sets, including Anderson's IRIS data set. The CENT network demonstrates its ability to produce a representative hierarchical structure to classify a broad range of data sets.

  3. The effects of CPAP treatment on task positive and default mode networks in obstructive sleep apnea patients: an fMRI study.

    Directory of Open Access Journals (Sweden)

    Olga Prilipko

    Full Text Available INTRODUCTION: Functional magnetic resonance imaging studies enable the investigation of neural correlates underlying behavioral performance. We investigate the effect of active and sham Continuous Positive Airway Pressure (CPAP treatment on working memory function of patients with Obstructive Sleep Apnea Syndrome (OSAS considering Task Positive and Default Mode networks (TPN and DMN. METHODS: An experiment with 4 levels of visuospatial n-back task was used to investigate the pattern of cortical activation in 17 men with moderate or severe OSAS before and after 2 months of therapeutic (active or sub-therapeutic (sham CPAP treatment. RESULTS: Patients with untreated OSAS had significantly less deactivation in the temporal regions of the DMN as compared to healthy controls, but activation within TPN regions was comparatively relatively preserved. After 2 months of treatment, active and sham CPAP groups exhibited opposite trends of cerebral activation and deactivation. After treatment, the active CPAP group demonstrated an increase of cerebral activation in the TPN at all task levels and of task-related cerebral deactivation in the anterior midline and medial temporal regions of the DMN at the 3-back level, associated with a significant improvement of behavioral performance, whereas the sham CPAP group exhibited less deactivation in the temporal regions of Default Mode Network and less Task Positive Network activation associated to longer response times at the 3-back. CONCLUSION: OSAS has a significant negative impact primarily on task-related DMN deactivation, particularly in the medial temporal regions, possibly due to nocturnal hypoxemia, as well as TPN activation, particularly in the right ventral fronto-parietal network. After 2 months of active nasal CPAP treatment a positive response was noted in both TPN and DMN but without compete recovery of existing behavioral and neuronal deficits. Initiation of CPAP treatment early in the course of the

  4. Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

    2018-02-01

    We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

  5. Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

    Science.gov (United States)

    Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

    2015-07-28

    An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

  6. Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

    Energy Technology Data Exchange (ETDEWEB)

    Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

    2016-10-25

    An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

  7. Graphs for information security control in software defined networks

    Science.gov (United States)

    Grusho, Alexander A.; Abaev, Pavel O.; Shorgin, Sergey Ya.; Timonina, Elena E.

    2017-07-01

    Information security control in software defined networks (SDN) is connected with execution of the security policy rules regulating information accesses and protection against distribution of the malicious code and harmful influences. The paper offers a representation of a security policy in the form of hierarchical structure which in case of distribution of resources for the solution of tasks defines graphs of admissible interactions in a networks. These graphs define commutation tables of switches via the SDN controller.

  8. A real-time in-memory discovery service leveraging hierarchical packaging information in a unique identifier network to retrieve track and trace information

    CERN Document Server

    Müller, Jürgen

    2014-01-01

    This book examines how to efficiently retrieve track and trace information for an item that took a certain path through a complex network of manufacturers, wholesalers, retailers and consumers. It includes valuable tips on in-memory data management.

  9. Catalysis with hierarchical zeolites

    DEFF Research Database (Denmark)

    Holm, Martin Spangsberg; Taarning, Esben; Egeblad, Kresten

    2011-01-01

    Hierarchical (or mesoporous) zeolites have attracted significant attention during the first decade of the 21st century, and so far this interest continues to increase. There have already been several reviews giving detailed accounts of the developments emphasizing different aspects of this research...... topic. Until now, the main reason for developing hierarchical zeolites has been to achieve heterogeneous catalysts with improved performance but this particular facet has not yet been reviewed in detail. Thus, the present paper summaries and categorizes the catalytic studies utilizing hierarchical...... zeolites that have been reported hitherto. Prototypical examples from some of the different categories of catalytic reactions that have been studied using hierarchical zeolite catalysts are highlighted. This clearly illustrates the different ways that improved performance can be achieved with this family...

  10. Micromechanics of hierarchical materials

    DEFF Research Database (Denmark)

    Mishnaevsky, Leon, Jr.

    2012-01-01

    A short overview of micromechanical models of hierarchical materials (hybrid composites, biomaterials, fractal materials, etc.) is given. Several examples of the modeling of strength and damage in hierarchical materials are summarized, among them, 3D FE model of hybrid composites...... with nanoengineered matrix, fiber bundle model of UD composites with hierarchically clustered fibers and 3D multilevel model of wood considered as a gradient, cellular material with layered composite cell walls. The main areas of research in micromechanics of hierarchical materials are identified, among them......, the investigations of the effects of load redistribution between reinforcing elements at different scale levels, of the possibilities to control different material properties and to ensure synergy of strengthening effects at different scale levels and using the nanoreinforcement effects. The main future directions...

  11. Programming with Hierarchical Maps

    DEFF Research Database (Denmark)

    Ørbæk, Peter

    This report desribes the hierarchical maps used as a central data structure in the Corundum framework. We describe its most prominent features, ague for its usefulness and briefly describe some of the software prototypes implemented using the technology....

  12. Introduction into Hierarchical Matrices

    KAUST Repository

    Litvinenko, Alexander

    2013-12-05

    Hierarchical matrices allow us to reduce computational storage and cost from cubic to almost linear. This technique can be applied for solving PDEs, integral equations, matrix equations and approximation of large covariance and precision matrices.

  13. Introduction into Hierarchical Matrices

    KAUST Repository

    Litvinenko, Alexander

    2013-01-01

    Hierarchical matrices allow us to reduce computational storage and cost from cubic to almost linear. This technique can be applied for solving PDEs, integral equations, matrix equations and approximation of large covariance and precision matrices.

  14. A hierarchical fuzzy rule-based approach to aphasia diagnosis.

    Science.gov (United States)

    Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

    2007-10-01

    Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

  15. Interplay between spatially explicit sediment sourcing, hierarchical river-network structure, and in-channel bed material sediment transport and storage dynamics

    Science.gov (United States)

    Czuba, Jonathan A.; Foufoula-Georgiou, Efi; Gran, Karen B.; Belmont, Patrick; Wilcock, Peter R.

    2017-05-01

    Understanding how sediment moves along source to sink pathways through watersheds—from hillslopes to channels and in and out of floodplains—is a fundamental problem in geomorphology. We contribute to advancing this understanding by modeling the transport and in-channel storage dynamics of bed material sediment on a river network over a 600 year time period. Specifically, we present spatiotemporal changes in bed sediment thickness along an entire river network to elucidate how river networks organize and process sediment supply. We apply our model to sand transport in the agricultural Greater Blue Earth River Basin in Minnesota. By casting the arrival of sediment to links of the network as a Poisson process, we derive analytically (under supply-limited conditions) the time-averaged probability distribution function of bed sediment thickness for each link of the river network for any spatial distribution of inputs. Under transport-limited conditions, the analytical assumptions of the Poisson arrival process are violated (due to in-channel storage dynamics) where we find large fluctuations and periodicity in the time series of bed sediment thickness. The time series of bed sediment thickness is the result of dynamics on a network in propagating, altering, and amalgamating sediment inputs in sometimes unexpected ways. One key insight gleaned from the model is that there can be a small fraction of reaches with relatively low-transport capacity within a nonequilibrium river network acting as "bottlenecks" that control sediment to downstream reaches, whereby fluctuations in bed elevation can dissociate from signals in sediment supply.

  16. A proposal for a global task planning architecture using the RoboEarth cloud based framework

    OpenAIRE

    Janssen, R.J.M.; Molengraft, van de, M.J.G.; Steinbuch, M.; Di Marco, D.; Zweigle, O.; Levi, P.; Py, F.; Musliner, D.

    2012-01-01

    As robotic systems become more and more capable of assisting in human domains, methods are sought to compose robot executable plans from abstract human instructions. To cope with the semantically rich and highly expressive nature of human instructions, Hierarchical Task Network planning is often being employed along with domain knowledge to solve planning problems in a pragmatic way. Commonly, the domain knowledge is specific to the planning problem at hand, impeding re-use. Therefore this pa...

  17. Evidence for a Functional Hierarchy of Association Networks.

    Science.gov (United States)

    Choi, Eun Young; Drayna, Garrett K; Badre, David

    2018-05-01

    Patient lesion and neuroimaging studies have identified a rostral-to-caudal functional gradient in the lateral frontal cortex (LFC) corresponding to higher-order (complex or abstract) to lower-order (simple or concrete) cognitive control. At the same time, monkey anatomical and human functional connectivity studies show that frontal regions are reciprocally connected with parietal and temporal regions, forming parallel and distributed association networks. Here, we investigated the link between the functional gradient of LFC regions observed during control tasks and the parallel, distributed organization of association networks. Whole-brain fMRI task activity corresponding to four orders of hierarchical control [Badre, D., & D'Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19, 2082-2099, 2007] was compared with a resting-state functional connectivity MRI estimate of cortical networks [Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125-1165, 2011]. Critically, at each order of control, activity in the LFC and parietal cortex overlapped onto a common association network that differed between orders. These results are consistent with a functional organization based on separable association networks that are recruited during hierarchical control. Furthermore, corticostriatal functional connectivity MRI showed that, consistent with their participation in functional networks, rostral-to-caudal LFC and caudal-to-rostral parietal regions had similar, order-specific corticostriatal connectivity that agreed with a striatal gating model of hierarchical rule use. Our results indicate that hierarchical cognitive control is subserved by parallel and distributed association networks, together forming

  18. Hierarchical organisation of causal graphs

    International Nuclear Information System (INIS)

    Dziopa, P.

    1993-01-01

    This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs

  19. Visualization of Social Networks

    NARCIS (Netherlands)

    Boertjes, E.M.; Kotterink, B.; Jager, E.J.

    2011-01-01

    Current visualizations of social networks are mostly some form of node-link diagram. Depending on the type of social network, this can be some treevisualization with a strict hierarchical structure or a more generic network visualization.

  20. Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

    Science.gov (United States)

    Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2017-12-01

    The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

  1. Hierarchical Scheduling Framework Based on Compositional Analysis Using Uppaal

    DEFF Research Database (Denmark)

    Boudjadar, Jalil; David, Alexandre; Kim, Jin Hyun

    2014-01-01

    This paper introduces a reconfigurable compositional scheduling framework, in which the hierarchical structure, the scheduling policies, the concrete task behavior and the shared resources can all be reconfigured. The behavior of each periodic preemptive task is given as a list of timed actions, ...

  2. P-Adic Analog of Navier–Stokes Equations: Dynamics of Fluid’s Flow in Percolation Networks (from Discrete Dynamics with Hierarchic Interactions to Continuous Universal Scaling Model

    Directory of Open Access Journals (Sweden)

    Klaudia Oleschko

    2017-04-01

    Full Text Available Recently p-adic (and, more generally, ultrametric spaces representing tree-like networks of percolation, and as a special case of capillary patterns in porous media, started to be used to model the propagation of fluids (e.g., oil, water, oil-in-water, and water-in-oil emulsion. The aim of this note is to derive p-adic dynamics described by fractional differential operators (Vladimirov operators starting with discrete dynamics based on hierarchically-structured interactions between the fluids’ volumes concentrated at different levels of the percolation tree and coming to the multiscale universal topology of the percolating nets. Similar systems of discrete hierarchic equations were widely applied to modeling of turbulence. However, in the present work this similarity is only formal since, in our model, the trees are real physical patterns with a tree-like topology of capillaries (or fractures in random porous media (not cascade trees, as in the case of turbulence, which we will be discussed elsewhere for the spinner flowmeter commonly used in the petroleum industry. By going to the “continuous limit” (with respect to the p-adic topology we represent the dynamics on the tree-like configuration space as an evolutionary nonlinear p-adic fractional (pseudo- differential equation, the tree-like analog of the Navier–Stokes equation. We hope that our work helps to come closer to a nonlinear equation solution, taking into account the scaling, hierarchies, and formal derivations, imprinted from the similar properties of the real physical world. Once this coupling is resolved, the more problematic question of information scaling in industrial applications will be achieved.

  3. Hierarchical Control for Smart Grids

    DEFF Research Database (Denmark)

    Trangbæk, K; Bendtsen, Jan Dimon; Stoustrup, Jakob

    2011-01-01

    of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high level MPC controller, a second level of so-called aggregators, which reduces the computational and communication-related load on the high-level control, and a lower level...... on one hand from varying consumption, and on the other hand by natural variations in power production e.g. from wind turbines. The high-level MPC problem is solved using quadratic optimisation, while the aggregator level can either involve quadratic optimisation or simple sorting-based min-max solutions...

  4. Hierarchical control system of advanced robot manipulator

    International Nuclear Information System (INIS)

    Oomichi, Takeo; Okino, Akihisa; Nishihara, Masatoshi; Sakamoto, Taizou; Matsuda, Koichi; Ohnishi, Ken

    1990-01-01

    We introduce a double arm with 4-finger's manipulator system which process the large volume of information at high speed. This is under research/development many type of works in the harsh condition. Namely, hierarchization of instruction unit in which motion control system as real time processing unit, and task planning unit as non-real time processing unit, interface with operation through the task planning unit has been made. Also, high speed processing of large volume information has been realized by decentralizing the motion control unit by function, hierarchizing the high speed processing unit, and developing high speed transmission, IC which does not depend on computer OS to avoid the delay in transmission. (author)

  5. Parallel hierarchical radiosity rendering

    Energy Technology Data Exchange (ETDEWEB)

    Carter, Michael [Iowa State Univ., Ames, IA (United States)

    1993-07-01

    In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.

  6. Rapid Authoring of Task Knowledge for Training and Performance Support

    National Research Council Canada - National Science Library

    Mohammed, John L; Sorensen, Barbara; Ong, James C; Li, Jian

    2005-01-01

    .... These systems use hierarchical, object-oriented task representations that enable rapid authoring by non-programmers while supporting sophisticated job aiding and student performance evaluation...

  7. Ni nanoparticles@Ni-Mo nitride nanorod arrays: a novel 3D-network hierarchical structure for high areal capacitance hybrid supercapacitors.

    Science.gov (United States)

    Ruan, Yunjun; Lv, Lin; Li, Zhishan; Wang, Chundong; Jiang, Jianjun

    2017-11-23

    Because of the advanced nature of their high power density, fast charge/discharge time, excellent cycling stability, and safety, supercapacitors have attracted intensive attention for large-scale applications. Nevertheless, one of the obstacles for their further development is their low energy density caused by sluggish redox reaction kinetics, low electroactive electrode materials, and/or high internal resistance. Here, we develop a facile and simple nitridation process to successfully synthesize hierarchical Ni nanoparticle decorated Ni 0.2 Mo 0.8 N nanorod arrays on a nickel foam (Ni-Mo-N NRA/NF) from its NiMoO 4 precursor, which delivers a high areal capacity of 2446 mC cm -2 at a current density of 2 mA cm -2 and shows outstanding cycling stability. The superior performance of the Ni-Mo-N NRA/NF can be ascribed to the metallic conductive nature of the Ni-Mo nitride, the fast surface redox reactions for the electrolyte ions and electrode materials, and the low contacted resistance between the active materials and the current collectors. Furthermore, a hybrid supercapacitor (HSC) is assembled using the Ni-Mo-N NRA/NF as the positive electrode and reduced graphene oxide (RGO) as the negative electrode. The optimized HSC exhibits excellent electrochemical performance with a high energy density of 40.9 W h kg -1 at a power density of 773 W kg -1 and a retention of 80.1% specific capacitance after 6000 cycles. These results indicate that the Ni-Mo-N NRA/NF have a promising potential for use in high-performance supercapacitors.

  8. Reduced dual-task gait speed is associated with visual Go/No-Go brain network activation in children and adolescents with concussion.

    Science.gov (United States)

    Howell, David R; Meehan, William P; Barber Foss, Kim D; Reches, Amit; Weiss, Michal; Myer, Gregory D

    2018-05-31

    To investigate the association between dual-task gait performance and brain network activation (BNA) using an electroencephalography (EEG)-based Go/No-Go paradigm among children and adolescents with concussion. Participants with a concussion completed a visual Go/No-Go task with collection of electroencephalogram brain activity. Data were treated with BNA analysis, which involves an algorithmic approach to EEG-ERP activation quantification. Participants also completed a dual-task gait assessment. The relationship between dual-task gait speed and BNA was assessed using multiple linear regression models. Participants (n = 20, 13.9 ± 2.3 years of age, 50% female) were tested at a mean of 7.0 ± 2.5 days post-concussion and were symptomatic at the time of testing (post-concussion symptom scale = 40.4 ± 21.9). Slower dual-task average gait speed (mean = 82.2 ± 21.0 cm/s) was significantly associated with lower relative time BNA scores (mean = 39.6 ± 25.8) during the No-Go task (β = 0.599, 95% CI = 0.214, 0.985, p = 0.005, R 2  = 0.405), while controlling for the effect of age and gender. Among children and adolescents with a concussion, slower dual-task gait speed was independently associated with lower BNA relative time scores during a visual Go/No-Go task. The relationship between abnormal gait behaviour and brain activation deficits may be reflective of disruption to multiple functional abilities after concussion.

  9. Examining a supramodal network for conflict processing: a systematic review and novel functional magnetic resonance imaging data for related visual and auditory stroop tasks.

    Science.gov (United States)

    Roberts, Katherine L; Hall, Deborah A

    2008-06-01

    Cognitive control over conflicting information has been studied extensively using tasks such as the color-word Stroop, flanker, and spatial conflict task. Neuroimaging studies typically identify a fronto-parietal network engaged in conflict processing, but numerous additional regions are also reported. Ascribing putative functional roles to these regions is problematic because some may have less to do with conflict processing per se, but could be engaged in specific processes related to the chosen stimulus modality, stimulus feature, or type of conflict task. In addition, some studies contrast activation on incongruent and congruent trials, even though a neutral baseline is needed to separate the effect of inhibition from that of facilitation. In the first part of this article, we report a systematic review of 34 neuroimaging publications, which reveals that conflict-related activity is reliably reported in the anterior cingulate cortex and bilaterally in the lateral prefrontal cortex, the anterior insula, and the parietal lobe. In the second part, we further explore these candidate "conflict" regions through a novel functional magnetic resonance imaging experiment, in which the same group of subjects perform related visual and auditory Stroop tasks. By carefully controlling for the same task (Stroop), the same to-be-ignored stimulus dimension (word meaning), and by separating out inhibitory processes from those of facilitation, we attempt to minimize the potential differences between the two tasks. The results provide converging evidence that the regions identified by the systematic review are reliably engaged in conflict processing. Despite carefully matching the Stroop tasks, some regions of differential activity remained, particularly in the parietal cortex. We discuss some of the task-specific processes which might account for this finding.

  10. Neutrosophic Hierarchical Clustering Algoritms

    Directory of Open Access Journals (Sweden)

    Rıdvan Şahin

    2014-03-01

    Full Text Available Interval neutrosophic set (INS is a generalization of interval valued intuitionistic fuzzy set (IVIFS, whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS is regarded as extension of intuitionistic fuzzy set (IFS. In this paper, we extend the hierarchical clustering techniques proposed for IFSs and IVIFSs to SVNSs and INSs respectively. Based on the traditional hierarchical clustering procedure, the single valued neutrosophic aggregation operator, and the basic distance measures between SVNSs, we define a single valued neutrosophic hierarchical clustering algorithm for clustering SVNSs. Then we extend the algorithm to classify an interval neutrosophic data. Finally, we present some numerical examples in order to show the effectiveness and availability of the developed clustering algorithms.

  11. Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI.

    Science.gov (United States)

    Sair, Haris I; Yahyavi-Firouz-Abadi, Noushin; Calhoun, Vince D; Airan, Raag D; Agarwal, Shruti; Intrapiromkul, Jarunee; Choe, Ann S; Gujar, Sachin K; Caffo, Brian; Lindquist, Martin A; Pillai, Jay J

    2016-03-01

    To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance. Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression. Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50. Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks. © 2015 Wiley Periodicals, Inc.

  12. Hierarchical wave functions revisited

    International Nuclear Information System (INIS)

    Li Dingping.

    1997-11-01

    We study the hierarchical wave functions on a sphere and on a torus. We simplify some wave functions on a sphere or a torus using the analytic properties of wave functions. The open question, the construction of the wave function for quasi electron excitation on a torus, is also solved in this paper. (author)

  13. Hierarchical Porous Structures

    Energy Technology Data Exchange (ETDEWEB)

    Grote, Christopher John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-06-07

    Materials Design is often at the forefront of technological innovation. While there has always been a push to generate increasingly low density materials, such as aero or hydrogels, more recently the idea of bicontinuous structures has gone more into play. This review will cover some of the methods and applications for generating both porous, and hierarchically porous structures.

  14. The Hierarchical Perspective

    Directory of Open Access Journals (Sweden)

    Daniel Sofron

    2015-05-01

    Full Text Available This paper is focused on the hierarchical perspective, one of the methods for representing space that was used before the discovery of the Renaissance linear perspective. The hierarchical perspective has a more or less pronounced scientific character and its study offers us a clear image of the way the representatives of the cultures that developed it used to perceive the sensitive reality. This type of perspective is an original method of representing three-dimensional space on a flat surface, which characterises the art of Ancient Egypt and much of the art of the Middle Ages, being identified in the Eastern European Byzantine art, as well as in the Western European Pre-Romanesque and Romanesque art. At the same time, the hierarchical perspective is also present in naive painting and infantile drawing. Reminiscences of this method can be recognised also in the works of some precursors of the Italian Renaissance. The hierarchical perspective can be viewed as a subjective ranking criterion, according to which the elements are visually represented by taking into account their relevance within the image while perception is ignored. This paper aims to show how the main objective of the artists of those times was not to faithfully represent the objective reality, but rather to emphasize the essence of the world and its perennial aspects. This may represent a possible explanation for the refusal of perspective in the Egyptian, Romanesque and Byzantine painting, characterised by a marked two-dimensionality.

  15. Network Centric Operations (NCO) Case Study: U.S. Navy's Fifth Fleet Task Force 50 in Operation ENDURING FREEDOM

    National Research Council Canada - National Science Library

    Garstka, John; Holloman, Kimberly; Balisle, Christine W; Adkins, Mark; Kruse, Jon

    2006-01-01

    .... The focus is on the background and creation of Task Force 50 (TF-50), and primarily on the evolution of the transformational capabilities that permitted TF-50 to succeed in the manner that it did...

  16. Age-related increase in brain activity during task-related and -negative networks and numerical inductive reasoning.

    Science.gov (United States)

    Sun, Li; Liang, Peipeng; Jia, Xiuqin; Qi, Zhigang; Li, Kuncheng

    2014-01-01

    Recent neuroimaging studies have shown that elderly adults exhibit increased and decreased activation on various cognitive tasks, yet little is known about age-related changes in inductive reasoning. To investigate the neural basis for the aging effect on inductive reasoning, 15 young and 15 elderly subjects performed numerical inductive reasoning while in a magnetic resonance (MR) scanner. Functional magnetic resonance imaging (fMRI) analysis revealed that numerical inductive reasoning, relative to rest, yielded multiple frontal, temporal, parietal, and some subcortical area activations for both age groups. In addition, the younger participants showed significant regions of task-induced deactivation, while no deactivation occurred in the elderly adults. Direct group comparisons showed that elderly adults exhibited greater activity in regions of task-related activation and areas showing task-induced deactivation (TID) in the younger group. Our findings suggest an age-related deficiency in neural function and resource allocation during inductive reasoning.

  17. BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

    Science.gov (United States)

    Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

    2016-01-01

    Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

  18. Task 5. Grid interconnection of building integrated and other dispersed photovoltaic power systems. Risk analysis of islanding of photovoltaic power systems within low voltage distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Cullen, N. [Freelance Consultant, Hillside House, Swindon SN1 3QA (United Kingdom); Thornycroft, J. [Halcrow Group Ltd, Burderop Park, Swindon SN4 0QD (United Kingdom); Collinson, A. [EA Technology, Capenhurst Technology Park, Chester CH1 6ES (United Kingdom)

    2002-03-15

    This report for the International Energy Agency (IEA) made by Task 5 of the Photovoltaic Power Systems (PVPS) programme presents the results of a risk analysis concerning photovoltaic power systems islanding in low-voltage distribution networks. The mission of the Photovoltaic Power Systems Programme is to enhance the international collaboration efforts which accelerate the development and deployment of photovoltaic solar energy. Task 5 deals with issues concerning grid-interconnection and distributed PV power systems. The purpose of this study was to apply formal risk analysis techniques to the issue of islanding of photovoltaic power systems within low voltage distribution networks. The aim was to determine the additional level of risk that islanding could present to the safety of customers and network maintenance staff. The study identified the reliability required for islanding detection and control systems based on standard procedures for developing a safety assurance strategy. The main conclusions are presented and discussed and recommendations are made. The report is concluded with an appendix that lists the relative risks involved.

  19. Hierarchical species distribution models

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.

    2016-01-01

    Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.

  20. Bayesian nonparametric hierarchical modeling.

    Science.gov (United States)

    Dunson, David B

    2009-04-01

    In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.

  1. Subclinical cognitive decline in middle-age is associated with reduced task-induced deactivation of the brain's default mode network

    DEFF Research Database (Denmark)

    Hansen, Naja Liv; Lauritzen, Martin; Mortensen, Erik Lykke

    2014-01-01

    range of neurodegenerative diseases involving cognitive symptoms, in conditions with increased risk of Alzheimer's disease, and even in advanced but healthy aging. Here, we investigated brain activation and deactivation during a visual-motor task in 185 clinically healthy males from a Danish birth......Cognitive abilities decline with age, but with considerable individual variation. The neurobiological correlate of this variation is not well described. Functional brain imaging studies have demonstrated reduced task-induced deactivation (TID) of the brain's default mode network (DMN) in a wide...... cohort, whose cognitive function was assessed in youth and midlife. Using each individual as his own control, we defined a group with a large degree of cognitive decline, and a control group. When correcting for effects of total cerebral blood flow and hemoglobin level, we found reduced TID...

  2. Hierarchically Structured Electrospun Fibers

    Science.gov (United States)

    2013-01-07

    in the natural lotus and silver ragwort leaves. Figure 4. Examples of electrospun bio-mimics of natural hierarchical structures. (A) Lotus leaf...B) pillared poly(methyl methacrylate) (PMMA) electrospun fiber mimic; (C) silver ragwort leaf; (D) electrospun fiber mimic made from nylon 6 and...domains containing the protein in the surrounding EVA fibers [115]. A wide variety of core-shell fibers have been generated, including PCL/ gelatin

  3. Shared and disorder-specific task-positive and default mode network dysfunctions during sustained attention in paediatric Attention-Deficit/Hyperactivity Disorder and obsessive/compulsive disorder

    Directory of Open Access Journals (Sweden)

    Luke J. Norman

    2017-01-01

    Full Text Available Patients with Attention-Deficit/Hyperactivity Disorder (ADHD and obsessive/compulsive disorder (OCD share problems with sustained attention, and are proposed to share deficits in switching between default mode and task positive networks. The aim of this study was to investigate shared and disorder-specific brain activation abnormalities during sustained attention in the two disorders. Twenty boys with ADHD, 20 boys with OCD and 20 age-matched healthy controls aged between 12 and 18 years completed a functional magnetic resonance imaging (fMRI version of a parametrically modulated sustained attention task with a progressively increasing sustained attention load. Performance and brain activation were compared between groups. Only ADHD patients were impaired in performance. Group by sustained attention load interaction effects showed that OCD patients had disorder-specific middle anterior cingulate underactivation relative to controls and ADHD patients, while ADHD patients showed disorder-specific underactivation in left dorsolateral prefrontal cortex/dorsal inferior frontal gyrus (IFG. ADHD and OCD patients shared left insula/ventral IFG underactivation and increased activation in posterior default mode network relative to controls, but had disorder-specific overactivation in anterior default mode regions, in dorsal anterior cingulate for ADHD and in anterior ventromedial prefrontal cortex for OCD. In sum, ADHD and OCD patients showed mostly disorder-specific patterns of brain abnormalities in both task positive salience/ventral attention networks with lateral frontal deficits in ADHD and middle ACC deficits in OCD, as well as in their deactivation patterns in medial frontal DMN regions. The findings suggest that attention performance in the two disorders is underpinned by disorder-specific activation patterns.

  4. Functional MRI Assessment of Task-Induced Deactivation of the Default Mode Network in Alzheimer’s Disease and At-Risk Older Individuals

    Directory of Open Access Journals (Sweden)

    Maija Pihlajamäki

    2009-01-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia in old age, and is characterized by prominent impairment of episodic memory. Recent functional imaging studies in AD have demonstrated alterations in a distributed network of brain regions supporting memory function, including regions of the default mode network. Previous positron emission tomography studies of older individuals at risk for AD have revealed hypometabolism of association cortical regions similar to the metabolic abnormalities seen in AD patients. In recent functional magnetic resonance imaging (fMRI studies of AD, corresponding brain default mode regions have also been found to demonstrate an abnormal fMRI task-induced deactivation response pattern. That is, the relative decreases in fMRI signal normally observed in the default mode regions in healthy subjects performing a cognitive task are not seen in AD patients, or may even be reversed to a paradoxical activation response. Our recent studies have revealed alterations in the pattern of deactivation also in elderly individuals at risk for AD by virtue of their APOE e4 genotype, or evidence of mild cognitive impairment (MCI. In agreement with recent reports from other groups, these studies demonstrate that the pattern of fMRI task-induced deactivation is progressively disrupted along the continuum from normal aging to MCI and to clinical AD and more impaired in e4 carriers compared to non-carriers. These findings will be discussed in the context of current literature regarding functional imaging of the default network in AD and at-risk populations.

  5. Hierarchical video summarization

    Science.gov (United States)

    Ratakonda, Krishna; Sezan, M. Ibrahim; Crinon, Regis J.

    1998-12-01

    We address the problem of key-frame summarization of vide in the absence of any a priori information about its content. This is a common problem that is encountered in home videos. We propose a hierarchical key-frame summarization algorithm where a coarse-to-fine key-frame summary is generated. A hierarchical key-frame summary facilitates multi-level browsing where the user can quickly discover the content of the video by accessing its coarsest but most compact summary and then view a desired segment of the video with increasingly more detail. At the finest level, the summary is generated on the basis of color features of video frames, using an extension of a recently proposed key-frame extraction algorithm. The finest level key-frames are recursively clustered using a novel pairwise K-means clustering approach with temporal consecutiveness constraint. We also address summarization of MPEG-2 compressed video without fully decoding the bitstream. We also propose efficient mechanisms that facilitate decoding the video when the hierarchical summary is utilized in browsing and playback of video segments starting at selected key-frames.

  6. Hierarchically Structured Electrospun Fibers

    Directory of Open Access Journals (Sweden)

    Nicole E. Zander

    2013-01-01

    Full Text Available Traditional electrospun nanofibers have a myriad of applications ranging from scaffolds for tissue engineering to components of biosensors and energy harvesting devices. The generally smooth one-dimensional structure of the fibers has stood as a limitation to several interesting novel applications. Control of fiber diameter, porosity and collector geometry will be briefly discussed, as will more traditional methods for controlling fiber morphology and fiber mat architecture. The remainder of the review will focus on new techniques to prepare hierarchically structured fibers. Fibers with hierarchical primary structures—including helical, buckled, and beads-on-a-string fibers, as well as fibers with secondary structures, such as nanopores, nanopillars, nanorods, and internally structured fibers and their applications—will be discussed. These new materials with helical/buckled morphology are expected to possess unique optical and mechanical properties with possible applications for negative refractive index materials, highly stretchable/high-tensile-strength materials, and components in microelectromechanical devices. Core-shell type fibers enable a much wider variety of materials to be electrospun and are expected to be widely applied in the sensing, drug delivery/controlled release fields, and in the encapsulation of live cells for biological applications. Materials with a hierarchical secondary structure are expected to provide new superhydrophobic and self-cleaning materials.

  7. Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI.

    Science.gov (United States)

    Magnuson, Matthew Evan; Thompson, Garth John; Schwarb, Hillary; Pan, Wen-Ju; McKinley, Andy; Schumacher, Eric H; Keilholz, Shella Dawn

    2015-12-01

    The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

  8. Task-dependent changes of motor cortical network excitability during precision grip compared to isolated finger contraction

    DEFF Research Database (Denmark)

    Kouchtir-Devanne, Nezha; Capaday, Charles; Cassim, François

    2012-01-01

    The purpose of this study was to determine whether task-dependent differences in corticospinal pathway excitability occur in going from isolated contractions of the index finger to its coordinated activity with the thumb. Focal transcranial magnetic stimulation (TMS) was used to measure input-out...

  9. Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

    Science.gov (United States)

    Huseyin Turan, Hasan; Kasap, Nihat; Savran, Huseyin

    2014-03-01

    Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.

  10. A Framework for Joint Optical-Wireless Resource Management in Multi-RAT, Heterogeneous Mobile Networks

    DEFF Research Database (Denmark)

    Zakrzewska, Anna; Popovska Avramova, Andrijana; Christiansen, Henrik Lehrmann

    2013-01-01

    Mobile networks are constantly evolving: new Radio Access Technologies (RATs) are being introduced, and backhaul architectures like Cloud-RAN (C-RAN) and distributed base stations are being proposed. Furthermore, small cells are being deployed to enhance network capacity. The end-users wish...... to be always connected to a high-quality service (high bit rates, low latency), thus causing a very complex network control task from an operator’s point of view. We thus propose a framework allowing joint overall network resource management. This scheme covers different types of network heterogeneity (multi......-RAT, multi-layer, multi-architecture) by introducing a novel, hierarchical approach to network resource management. Self-Organizing Networks (SON) and cognitive network behaviors are covered as well as more traditional mobile network features. The framework is applicable to all phases of network operation...

  11. Contributions of the SDR Task Network tool to Calibration and Validation of the NPOESS Preparatory Project instruments

    Science.gov (United States)

    Feeley, J.; Zajic, J.; Metcalf, A.; Baucom, T.

    2009-12-01

    The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) Calibration and Validation (Cal/Val) team is planning post-launch activities to calibrate the NPP sensors and validate Sensor Data Records (SDRs). The IPO has developed a web-based data collection and visualization tool in order to effectively collect, coordinate, and manage the calibration and validation tasks for the OMPS, ATMS, CrIS, and VIIRS instruments. This tool is accessible to the multi-institutional Cal/Val teams consisting of the Prime Contractor and Government Cal/Val leads along with the NASA NPP Mission team, and is used for mission planning and identification/resolution of conflicts between sensor activities. Visualization techniques aid in displaying task dependencies, including prerequisites and exit criteria, allowing for the identification of a critical path. This presentation will highlight how the information is collected, displayed, and used to coordinate the diverse instrument calibration/validation teams.

  12. Context updates are hierarchical

    Directory of Open Access Journals (Sweden)

    Anton Karl Ingason

    2016-10-01

    Full Text Available This squib studies the order in which elements are added to the shared context of interlocutors in a conversation. It focuses on context updates within one hierarchical structure and argues that structurally higher elements are entered into the context before lower elements, even if the structurally higher elements are pronounced after the lower elements. The crucial data are drawn from a comparison of relative clauses in two head-initial languages, English and Icelandic, and two head-final languages, Korean and Japanese. The findings have consequences for any theory of a dynamic semantics.

  13. Mapping lexical-semantic networks and determining hemispheric language dominance: Do task design, sex, age, and language performance make a difference?

    Science.gov (United States)

    Chang, Yu-Hsuan A; Javadi, Sogol S; Bahrami, Naeim; Uttarwar, Vedang S; Reyes, Anny; McDonald, Carrie R

    2018-04-01

    Blocked and event-related fMRI designs are both commonly used to localize language networks and determine hemispheric dominance in research and clinical settings. We compared activation profiles on a semantic monitoring task using one of the two designs in a total of 43 healthy individual to determine whether task design or subject-specific factors (i.e., age, sex, or language performance) influence activation patterns. We found high concordance between the two designs within core language regions, including the inferior frontal, posterior temporal, and basal temporal region. However, differences emerged within inferior parietal cortex. Subject-specific factors did not influence activation patterns, nor did they interact with task design. These results suggest that despite high concordance within perisylvian regions that are robust to subject-specific factors, methodological differences between blocked and event-related designs may contribute to parietal activations. These findings provide important information for researchers incorporating fMRI results into meta-analytic studies, as well as for clinicians using fMRI to guide pre-surgical planning. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Development of advanced MCR task analysis methods

    International Nuclear Information System (INIS)

    Na, J. C.; Park, J. H.; Lee, S. K.; Kim, J. K.; Kim, E. S.; Cho, S. B.; Kang, J. S.

    2008-07-01

    This report describes task analysis methodology for advanced HSI designs. Task analyses was performed by using procedure-based hierarchical task analysis and task decomposition methods. The results from the task analysis were recorded in a database. Using the TA results, we developed static prototype of advanced HSI and human factors engineering verification and validation methods for an evaluation of the prototype. In addition to the procedure-based task analysis methods, workload estimation based on the analysis of task performance time and analyses for the design of information structure and interaction structures will be necessary

  15. Topology of foreign exchange markets using hierarchical structure methods

    Science.gov (United States)

    Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

    2007-08-01

    This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

  16. Topology-based hierarchical scheduling using deficit round robin

    DEFF Research Database (Denmark)

    Yu, Hao; Yan, Ying; Berger, Michael Stubert

    2009-01-01

    according to the topology. The mapping process could be completed through the network management plane or by manual configuration. Based on the knowledge of the network, the scheduler can manage the traffic on behalf of other less advanced nodes, avoid potential traffic congestion, and provide flow...... protection and isolation. Comparisons between hierarchical scheduling, flow-based scheduling, and class-based scheduling schemes have been carried out under a symmetric tree topology. Results have shown that the hierarchical scheduling scheme provides better flow protection and isolation from attack...

  17. Hierarchically Organized Behavior and Its Neural Foundations: A Reinforcement Learning Perspective

    Science.gov (United States)

    Botvinick, Matthew M.; Niv, Yael; Barto, Andrew C.

    2009-01-01

    Research on human and animal behavior has long emphasized its hierarchical structure--the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely…

  18. Hierarchical quark mass matrices

    International Nuclear Information System (INIS)

    Rasin, A.

    1998-02-01

    I define a set of conditions that the most general hierarchical Yukawa mass matrices have to satisfy so that the leading rotations in the diagonalization matrix are a pair of (2,3) and (1,2) rotations. In addition to Fritzsch structures, examples of such hierarchical structures include also matrices with (1,3) elements of the same order or even much larger than the (1,2) elements. Such matrices can be obtained in the framework of a flavor theory. To leading order, the values of the angle in the (2,3) plane (s 23 ) and the angle in the (1,2) plane (s 12 ) do not depend on the order in which they are taken when diagonalizing. We find that any of the Cabbibo-Kobayashi-Maskawa matrix parametrizations that consist of at least one (1,2) and one (2,3) rotation may be suitable. In the particular case when the s 13 diagonalization angles are sufficiently small compared to the product s 12 s 23 , two special CKM parametrizations emerge: the R 12 R 23 R 12 parametrization follows with s 23 taken before the s 12 rotation, and vice versa for the R 23 R 12 R 23 parametrization. (author)

  19. Hierarchical partial order ranking

    International Nuclear Information System (INIS)

    Carlsen, Lars

    2008-01-01

    Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters

  20. Nested and Hierarchical Archimax copulas

    KAUST Repository

    Hofert, Marius; Huser, Raphaë l; Prasad, Avinash

    2017-01-01

    The class of Archimax copulas is generalized to nested and hierarchical Archimax copulas in several ways. First, nested extreme-value copulas or nested stable tail dependence functions are introduced to construct nested Archimax copulas based on a single frailty variable. Second, a hierarchical construction of d-norm generators is presented to construct hierarchical stable tail dependence functions and thus hierarchical extreme-value copulas. Moreover, one can, by itself or additionally, introduce nested frailties to extend Archimax copulas to nested Archimax copulas in a similar way as nested Archimedean copulas extend Archimedean copulas. Further results include a general formula for the density of Archimax copulas.

  1. Nested and Hierarchical Archimax copulas

    KAUST Repository

    Hofert, Marius

    2017-07-03

    The class of Archimax copulas is generalized to nested and hierarchical Archimax copulas in several ways. First, nested extreme-value copulas or nested stable tail dependence functions are introduced to construct nested Archimax copulas based on a single frailty variable. Second, a hierarchical construction of d-norm generators is presented to construct hierarchical stable tail dependence functions and thus hierarchical extreme-value copulas. Moreover, one can, by itself or additionally, introduce nested frailties to extend Archimax copulas to nested Archimax copulas in a similar way as nested Archimedean copulas extend Archimedean copulas. Further results include a general formula for the density of Archimax copulas.

  2. Privacy-Preserving Task Assignment in Spatial Crowdsourcing

    KAUST Repository

    Liu, An

    2017-09-20

    With the progress of mobile devices and wireless networks, spatial crowdsourcing (SC) is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao’s garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.

  3. Hierarchical unilamellar vesicles of controlled compositional heterogeneity.

    Directory of Open Access Journals (Sweden)

    Maik Hadorn

    Full Text Available Eukaryotic life contains hierarchical vesicular architectures (i.e. organelles that are crucial for material production and trafficking, information storage and access, as well as energy production. In order to perform specific tasks, these compartments differ among each other in their membrane composition and their internal cargo and also differ from the cell membrane and the cytosol. Man-made structures that reproduce this nested architecture not only offer a deeper understanding of the functionalities and evolution of organelle-bearing eukaryotic life but also allow the engineering of novel biomimetic technologies. Here, we show the newly developed vesicle-in-water-in-oil emulsion transfer preparation technique to result in giant unilamellar vesicles internally compartmentalized by unilamellar vesicles of different membrane composition and internal cargo, i.e. hierarchical unilamellar vesicles of controlled compositional heterogeneity. The compartmentalized giant unilamellar vesicles were subsequently isolated by a separation step exploiting the heterogeneity of the membrane composition and the encapsulated cargo. Due to the controlled, efficient, and technically straightforward character of the new preparation technique, this study allows the hierarchical fabrication of compartmentalized giant unilamellar vesicles of controlled compositional heterogeneity and will ease the development of eukaryotic cell mimics that resemble their natural templates as well as the fabrication of novel multi-agent drug delivery systems for combination therapies and complex artificial microreactors.

  4. Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

    Science.gov (United States)

    Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

    Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

  5. "Churyumov Unified Network": New, Important Tasks for Astronomical Observatories to Protect Society in the Era of Modern Hybrid Wars

    Science.gov (United States)

    Churyumov, K. I.; Vidmachenko, A. P.; Steklov, A. F.; Dashkiev, N. G.; Romanyuk, Ya. O.; Stepakhno, I. V.

    2016-12-01

    Authors created and provided the operation of the first version of the "Bolide Network of Churyumov" for continuous recording of twilight and daytime traces of aerial and aerospace intrusions over Kiev and Kiev district during 2013-2016. A total of more than 36000 copyright photos was obtained, their classification was carried out and the first database was created. The authors recorded typical space invading meteoroids, comets nucleus fragments and traces of aerial intrusions, signs of which, as a rule, are observed at lower altitudes comparing with typical space invasions.

  6. Transmutations across hierarchical levels

    International Nuclear Information System (INIS)

    O'Neill, R.V.

    1977-01-01

    The development of large-scale ecological models depends implicitly on a concept known as hierarchy theory which views biological systems in a series of hierarchical levels (i.e., organism, population, trophic level, ecosystem). The theory states that an explanation of a biological phenomenon is provided when it is shown to be the consequence of the activities of the system's components, which are themselves systems in the next lower level of the hierarchy. Thus, the behavior of a population is explained by the behavior of the organisms in the population. The initial step in any modeling project is, therefore, to identify the system components and the interactions between them. A series of examples of transmutations in aquatic and terrestrial ecosystems are presented to show how and why changes occur. The types of changes are summarized and possible implications of transmutation for hierarchy theory, for the modeler, and for the ecological theoretician are discussed

  7. Trees and Hierarchical Structures

    CERN Document Server

    Haeseler, Arndt

    1990-01-01

    The "raison d'etre" of hierarchical dustering theory stems from one basic phe­ nomenon: This is the notorious non-transitivity of similarity relations. In spite of the fact that very often two objects may be quite similar to a third without being that similar to each other, one still wants to dassify objects according to their similarity. This should be achieved by grouping them into a hierarchy of non-overlapping dusters such that any two objects in ~ne duster appear to be more related to each other than they are to objects outside this duster. In everyday life, as well as in essentially every field of scientific investigation, there is an urge to reduce complexity by recognizing and establishing reasonable das­ sification schemes. Unfortunately, this is counterbalanced by the experience of seemingly unavoidable deadlocks caused by the existence of sequences of objects, each comparatively similar to the next, but the last rather different from the first.

  8. Optimisation by hierarchical search

    Science.gov (United States)

    Zintchenko, Ilia; Hastings, Matthew; Troyer, Matthias

    2015-03-01

    Finding optimal values for a set of variables relative to a cost function gives rise to some of the hardest problems in physics, computer science and applied mathematics. Although often very simple in their formulation, these problems have a complex cost function landscape which prevents currently known algorithms from efficiently finding the global optimum. Countless techniques have been proposed to partially circumvent this problem, but an efficient method is yet to be found. We present a heuristic, general purpose approach to potentially improve the performance of conventional algorithms or special purpose hardware devices by optimising groups of variables in a hierarchical way. We apply this approach to problems in combinatorial optimisation, machine learning and other fields.

  9. A hierarchical structure approach to MultiSensor Information Fusion

    Energy Technology Data Exchange (ETDEWEB)

    Maren, A.J. (Tennessee Univ., Tullahoma, TN (United States). Space Inst.); Pap, R.M.; Harston, C.T. (Accurate Automation Corp., Chattanooga, TN (United States))

    1989-01-01

    A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

  10. A hierarchical structure approach to MultiSensor Information Fusion

    Energy Technology Data Exchange (ETDEWEB)

    Maren, A.J. [Tennessee Univ., Tullahoma, TN (United States). Space Inst.; Pap, R.M.; Harston, C.T. [Accurate Automation Corp., Chattanooga, TN (United States)

    1989-12-31

    A major problem with image-based MultiSensor Information Fusion (MSIF) is establishing the level of processing at which information should be fused. Current methodologies, whether based on fusion at the pixel, segment/feature, or symbolic levels, are each inadequate for robust MSIF. Pixel-level fusion has problems with coregistration of the images or data. Attempts to fuse information using the features of segmented images or data relies an a presumed similarity between the segmentation characteristics of each image or data stream. Symbolic-level fusion requires too much advance processing to be useful, as we have seen in automatic target recognition tasks. Image-based MSIF systems need to operate in real-time, must perform fusion using a variety of sensor types, and should be effective across a wide range of operating conditions or deployment environments. We address this problem through developing a new representation level which facilitates matching and information fusion. The Hierarchical Scene Structure (HSS) representation, created using a multilayer, cooperative/competitive neural network, meets this need. The MSS is intermediate between a pixel-based representation and a scene interpretation representation, and represents the perceptual organization of an image. Fused HSSs will incorporate information from multiple sensors. Their knowledge-rich structure aids top-down scene interpretation via both model matching and knowledge-based,region interpretation.

  11. Adaptive Scheduling Applied to Non-Deterministic Networks of Heterogeneous Tasks for Peak Throughput in Concurrent Gaudi

    CERN Document Server

    AUTHOR|(CDS)2070032; Clemencic, Marco

    As much the e-Science revolutionizes the scientific method in its empirical research and scientific theory, as it does pose the ever growing challenge of accelerating data deluge. The high energy physics (HEP) is a prominent representative of the data intensive science and requires scalable high-throughput software to be able to cope with associated computational endeavors. One such striking example is $\\text G\\rm \\small{AUDI}$ -- an experiment independent software framework, used in several frontier HEP experiments. Among them stand ATLAS and LHCb -- two of four mainstream experiments at the Large Hadron Collider (LHC) at CERN, the European Laboratory for Particle Physics. The framework is currently undergoing an architectural revolution aiming at massively concurrent and adaptive data processing. In this work I explore new dimensions of performance improvement for the next generation $\\text G\\rm \\small{AUDI}$. I then propose a complex of generic task scheduling solutions for adaptive and non-intrusive throu...

  12. Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Traian; Pop, Paul; Eles, Petru

    2005-01-01

    We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource......, they are organized in a hierarchy. In this paper, we address design problems that are characteristic to such hierarchically scheduled systems: assignment of scheduling policies to tasks, mapping of tasks to hardware components, and the scheduling of the activities. We present algorithms for solving these problems....... Our heuristics are able to find schedulable implementations under limited resources, achieving an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  13. Mapping the brain's orchestration during speech comprehension: task-specific facilitation of regional synchrony in neural networks

    Directory of Open Access Journals (Sweden)

    Keil Andreas

    2004-10-01

    Full Text Available Abstract Background How does the brain convert sounds and phonemes into comprehensible speech? In the present magnetoencephalographic study we examined the hypothesis that the coherence of electromagnetic oscillatory activity within and across brain areas indicates neurophysiological processes linked to speech comprehension. Results Amplitude-modulated (sinusoidal 41.5 Hz auditory verbal and nonverbal stimuli served to drive steady-state oscillations in neural networks involved in speech comprehension. Stimuli were presented to 12 subjects in the following conditions (a an incomprehensible string of words, (b the same string of words after being introduced as a comprehensible sentence by proper articulation, and (c nonverbal stimulations that included a 600-Hz tone, a scale, and a melody. Coherence, defined as correlated activation of magnetic steady state fields across brain areas and measured as simultaneous activation of current dipoles in source space (Minimum-Norm-Estimates, increased within left- temporal-posterior areas when the sound string was perceived as a comprehensible sentence. Intra-hemispheric coherence was larger within the left than the right hemisphere for the sentence (condition (b relative to all other conditions, and tended to be larger within the right than the left hemisphere for nonverbal stimuli (condition (c, tone and melody relative to the other conditions, leading to a more pronounced hemispheric asymmetry for nonverbal than verbal material. Conclusions We conclude that coherent neuronal network activity may index encoding of verbal information on the sentence level and can be used as a tool to investigate auditory speech comprehension.

  14. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  15. Flow and transport in hierarchically fractured systems

    International Nuclear Information System (INIS)

    Karasaki, K.

    1993-01-01

    Preliminary results indicate that flow in the saturated zone at Yucca Mountain is controlled by fractures. A current conceptual model assumes that the flow in the fracture system can be approximately by a three-dimensionally interconnected network of linear conduits. The overall flow system of rocks at Yucca Mountain is considered to consist of hierarchically structured heterogeneous fracture systems of multiple scales. A case study suggests that it is more appropriate to use the flow parameters of the large fracture system for predicting the first arrival time, rather than using the bulk average parameters of the total system

  16. Statistical dynamics of ultradiffusion in hierarchical systems

    International Nuclear Information System (INIS)

    Gardner, S.

    1987-01-01

    In many types of disordered systems which exhibit frustration and competition, an ultrametric topology is found to exist in the space of allowable states. This ultrametric topology of states is associated with a hierarchical relaxation process called ultradiffusion. Ultradiffusion occurs in hierarchical non-linear (HNL) dynamical systems when constraints cause large scale, slow modes of motion to be subordinated to small scale, fast modes. Examples of ultradiffusion are found throughout condensed matter physics and critical phenomena (e.g. the states of spin glasses), in biophysics (e.g. the states of Hopfield networks) and in many other fields including layered computing based upon nonlinear dynamics. The statistical dynamics of ultradiffusion can be treated as a random walk on an ultrametric space. For reversible bifurcating ultrametric spaces the evolution equation governing the probability of a particle being found at site i at time t has a highly degenerate transition matrix. This transition matrix has a fractal geometry similar to the replica form proposed for spin glasses. The authors invert this fractal matrix using a recursive quad-tree (QT) method. Possible applications of hierarchical systems to communications and symbolic computing are discussed briefly

  17. How hierarchical is language use?

    Science.gov (United States)

    Frank, Stefan L.; Bod, Rens; Christiansen, Morten H.

    2012-01-01

    It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science. PMID:22977157

  18. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  19. Automatically assessing properties of dynamic cameras for camera selection and rapid deployment of video content analysis tasks in large-scale ad-hoc networks

    Science.gov (United States)

    den Hollander, Richard J. M.; Bouma, Henri; van Rest, Jeroen H. C.; ten Hove, Johan-Martijn; ter Haar, Frank B.; Burghouts, Gertjan J.

    2017-10-01

    Video analytics is essential for managing large quantities of raw data that are produced by video surveillance systems (VSS) for the prevention, repression and investigation of crime and terrorism. Analytics is highly sensitive to changes in the scene, and for changes in the optical chain so a VSS with analytics needs careful configuration and prompt maintenance to avoid false alarms. However, there is a trend from static VSS consisting of fixed CCTV cameras towards more dynamic VSS deployments over public/private multi-organization networks, consisting of a wider variety of visual sensors, including pan-tilt-zoom (PTZ) cameras, body-worn cameras and cameras on moving platforms. This trend will lead to more dynamic scenes and more frequent changes in the optical chain, creating structural problems for analytics. If these problems are not adequately addressed, analytics will not be able to continue to meet end users' developing needs. In this paper, we present a three-part solution for managing the performance of complex analytics deployments. The first part is a register containing meta data describing relevant properties of the optical chain, such as intrinsic and extrinsic calibration, and parameters of the scene such as lighting conditions or measures for scene complexity (e.g. number of people). A second part frequently assesses these parameters in the deployed VSS, stores changes in the register, and signals relevant changes in the setup to the VSS administrator. A third part uses the information in the register to dynamically configure analytics tasks based on VSS operator input. In order to support the feasibility of this solution, we give an overview of related state-of-the-art technologies for autocalibration (self-calibration), scene recognition and lighting estimation in relation to person detection. The presented solution allows for rapid and robust deployment of Video Content Analysis (VCA) tasks in large scale ad-hoc networks.

  20. Hierarchical resilience with lightweight threads

    International Nuclear Information System (INIS)

    Wheeler, Kyle Bruce

    2011-01-01

    This paper proposes methodology for providing robustness and resilience for a highly threaded distributed- and shared-memory environment based on well-defined inputs and outputs to lightweight tasks. These inputs and outputs form a failure 'barrier', allowing tasks to be restarted or duplicated as necessary. These barriers must be expanded based on task behavior, such as communication between tasks, but do not prohibit any given behavior. One of the trends in high-performance computing codes seems to be a trend toward self-contained functions that mimic functional programming. Software designers are trending toward a model of software design where their core functions are specified in side-effect free or low-side-effect ways, wherein the inputs and outputs of the functions are well-defined. This provides the ability to copy the inputs to wherever they need to be - whether that's the other side of the PCI bus or the other side of the network - do work on that input using local memory, and then copy the outputs back (as needed). This design pattern is popular among new distributed threading environment designs. Such designs include the Barcelona STARS system, distributed OpenMP systems, the Habanero-C and Habanero-Java systems from Vivek Sarkar at Rice University, the HPX/ParalleX model from LSU, as well as our own Scalable Parallel Runtime effort (SPR) and the Trilinos stateless kernels. This design pattern is also shared by CUDA and several OpenMP extensions for GPU-type accelerators (e.g. the PGI OpenMP extensions).

  1. Toward a new task assignment and path evolution (TAPE) for missile defense system (MDS) using intelligent adaptive SOM with recurrent neural networks (RNNs).

    Science.gov (United States)

    Wang, Chi-Hsu; Chen, Chun-Yao; Hung, Kun-Neng

    2015-06-01

    In this paper, a new adaptive self-organizing map (SOM) with recurrent neural network (RNN) controller is proposed for task assignment and path evolution of missile defense system (MDS). We address the problem of N agents (defending missiles) and D targets (incoming missiles) in MDS. A new RNN controller is designed to force an agent (or defending missile) toward a target (or incoming missile), and a monitoring controller is also designed to reduce the error between RNN controller and ideal controller. A new SOM with RNN controller is then designed to dispatch agents to their corresponding targets by minimizing total damaging cost. This is actually an important application of the multiagent system. The SOM with RNN controller is the main controller. After task assignment, the weighting factors of our new SOM with RNN controller are activated to dispatch the agents toward their corresponding targets. Using the Lyapunov constraints, the weighting factors for the proposed SOM with RNN controller are updated to guarantee the stability of the path evolution (or planning) system. Excellent simulations are obtained using this new approach for MDS, which show that our RNN has the lowest average miss distance among the several techniques.

  2. Classifying Secondary Task Driving Safety Using Method of F-ANP

    Directory of Open Access Journals (Sweden)

    Lisheng Jin

    2015-02-01

    Full Text Available This study was designed to build an evaluation system for secondary task driving safety by using method of Fuzzy Analytic Network Process (F-ANP. Forty drivers completed driving on driving simulator while interacting with or without a secondary task. Measures of fixations, saccades, and vehicle running status were analyzed. According to five experts' opinions, a hierarchical model for secondary task driving safety evaluation was built. The hierarchical model was divided into three levels: goal, assessment dimension, and criteria. Seven indexes make up the level of criteria, and the assessment dimension includes two clusters: vehicle control risk and driver eye movement risk. By method of F-ANP, the priorities of the criteria and the subcriteria were determined. Furthermore, to rank the driving safety, an approach based on the principle of maximum membership degree was adopted. At last, a case study of secondary task driving safety evaluation by forty drivers using the proposed method was done. The results indicated that the application of the proposed method is practically feasible and adoptable for secondary task driving safety evaluation.

  3. TopoGen: A Network Topology Generation Architecture with application to automating simulations of Software Defined Networks

    CERN Document Server

    Laurito, Andres; The ATLAS collaboration

    2017-01-01

    Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...

  4. TopoGen: A Network Topology Generation Architecture with application to automating simulations of Software Defined Networks

    CERN Document Server

    Laurito, Andres; The ATLAS collaboration

    2018-01-01

    Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...

  5. Algorithm of parallel: hierarchical transformation and its implementation on FPGA

    Science.gov (United States)

    Timchenko, Leonid I.; Petrovskiy, Mykola S.; Kokryatskay, Natalia I.; Barylo, Alexander S.; Dembitska, Sofia V.; Stepanikuk, Dmytro S.; Suleimenov, Batyrbek; Zyska, Tomasz; Uvaysova, Svetlana; Shedreyeva, Indira

    2017-08-01

    In this paper considers the algorithm of laser beam spots image classification in atmospheric-optical transmission systems. It discusses the need for images filtering using adaptive methods, using, for example, parallel-hierarchical networks. The article also highlights the need to create high-speed memory devices for such networks. Implementation and simulation results of the developed method based on the PLD are demonstrated, which shows that the presented method gives 15-20% better prediction results than similar methods.

  6. The academic library network

    Directory of Open Access Journals (Sweden)

    Jacek Wojciechowski

    2012-01-01

    Full Text Available The efficiency of libraries, academic libraries in particular, necessitates organizational changes facilitating or even imposing co-operation. Any structure of any university has to have an integrated network of libraries, with an appropriate division of work, and one that is consolidated as much as it is possible into medium-size or large libraries. Within thus created network, a chance arises to centralize the main library processes based on appropriate procedures in the main library, highly specialized, more effective and therefore cheaper in operation, including a co-ordination of all more important endeavours and tasks. Hierarchically subordinated libraries can be thus more focused on performing their routine service, more and more frequently providing for the whole of the university, and being able to adjust to changeable requirements and demands of patrons and of new tasks resulting from the new model of the university operation. Another necessary change seems to be a universal implementation of an ov rall programme framework that would include all services in the university’s library networks.

  7. Hierarchical Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Di Lu

    2018-01-01

    Full Text Available The Internet of Things (IoT generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA. It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

  8. Hierarchical Linked Views

    Energy Technology Data Exchange (ETDEWEB)

    Erbacher, Robert; Frincke, Deb

    2007-07-02

    Coordinated views have proven critical to the development of effective visualization environments. This results from the fact that a single view or representation of the data cannot show all of the intricacies of a given data set. Additionally, users will often need to correlate more data parameters than can effectively be integrated into a single visual display. Typically, development of multiple-linked views results in an adhoc configuration of views and associated interactions. The hierarchical model we are proposing is geared towards more effective organization of such environments and the views they encompass. At the same time, this model can effectively integrate much of the prior work on interactive and visual frameworks. Additionally, we expand the concept of views to incorporate perceptual views. This is related to the fact that visual displays can have information encoded at various levels of focus. Thus, a global view of the display provides overall trends of the data while focusing in on individual elements provides detailed specifics. By integrating interaction and perception into a single model, we show how one impacts the other. Typically, interaction and perception are considered separately, however, when interaction is being considered at a fundamental level and allowed to direct/modify the visualization directly we must consider them simultaneously and how they impact one another.

  9. Analysis of genetic association using hierarchical clustering and cluster validation indices.

    Science.gov (United States)

    Pagnuco, Inti A; Pastore, Juan I; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L

    2017-10-01

    It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Direct hierarchical assembly of nanoparticles

    Science.gov (United States)

    Xu, Ting; Zhao, Yue; Thorkelsson, Kari

    2014-07-22

    The present invention provides hierarchical assemblies of a block copolymer, a bifunctional linking compound and a nanoparticle. The block copolymers form one micro-domain and the nanoparticles another micro-domain.

  11. Hierarchical materials: Background and perspectives

    DEFF Research Database (Denmark)

    2016-01-01

    Hierarchical design draws inspiration from analysis of biological materials and has opened new possibilities for enhancing performance and enabling new functionalities and extraordinary properties. With the development of nanotechnology, the necessary technological requirements for the manufactur...

  12. Ways of looking ahead: hierarchical planning in language production.

    Science.gov (United States)

    Lee, Eun-Kyung; Brown-Schmidt, Sarah; Watson, Duane G

    2013-12-01

    It is generally assumed that language production proceeds incrementally, with chunks of linguistic structure planned ahead of speech. Extensive research has examined the scope of language production and suggests that the size of planned chunks varies across contexts (Ferreira & Swets, 2002; Wagner & Jescheniak, 2010). By contrast, relatively little is known about the structure of advance planning, specifically whether planning proceeds incrementally according to the surface structure of the utterance, or whether speakers plan according to the hierarchical relationships between utterance elements. In two experiments, we examine the structure and scope of lexical planning in language production using a picture description task. Analyses of speech onset times and word durations show that speakers engage in hierarchical planning such that structurally dependent lexical items are planned together and that hierarchical planning occurs for both direct and indirect dependencies. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. A note on hierarchical hubbing for a generalization of the VPN problem

    NARCIS (Netherlands)

    N.K. Olver (Neil)

    2014-01-01

    htmlabstractRobust network design refers to a class of optimization problems that occur when designing networks to efficiently handle variable demands. The notion of "hierarchical hubbing" was introduced (in the narrow context of a specific robust network design question), by Olver and Shepherd

  14. A note on hierarchical hubbing for a generalization of the VPN problem

    NARCIS (Netherlands)

    Olver, N.K.

    2014-01-01

    Robust network design refers to a class of optimization problems that occur when designing networks to efficiently handle variable demands. The notion of "hierarchical hubbing" was introduced (in the narrow context of a specific robust network design question), by Olver and Shepherd [2010].

  15. Urban pattern: Layout design by hierarchical domain splitting

    KAUST Repository

    Yang, Yongliang; Wang, Jun; Vouga, Etienne; Wonka, Peter

    2013-01-01

    We present a framework for generating street networks and parcel layouts. Our goal is the generation of high-quality layouts that can be used for urban planning and virtual environments. We propose a solution based on hierarchical domain splitting using two splitting types: streamline-based splitting, which splits a region along one or multiple streamlines of a cross field, and template-based splitting, which warps pre-designed templates to a region and uses the interior geometry of the template as the splitting lines. We combine these two splitting approaches into a hierarchical framework, providing automatic and interactive tools to explore the design space.

  16. Urban pattern: Layout design by hierarchical domain splitting

    KAUST Repository

    Yang, Yongliang

    2013-11-06

    We present a framework for generating street networks and parcel layouts. Our goal is the generation of high-quality layouts that can be used for urban planning and virtual environments. We propose a solution based on hierarchical domain splitting using two splitting types: streamline-based splitting, which splits a region along one or multiple streamlines of a cross field, and template-based splitting, which warps pre-designed templates to a region and uses the interior geometry of the template as the splitting lines. We combine these two splitting approaches into a hierarchical framework, providing automatic and interactive tools to explore the design space.

  17. Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

    OpenAIRE

    Li, Xiangang; Wu, Xihong

    2014-01-01

    Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed an...

  18. Dynamics and thermodynamics in hierarchically organized systems applications in physics, biology and economics

    CERN Document Server

    Auger, P

    2013-01-01

    One of the most fundamental and efficient ways of conceptualizing complex systems is to organize them hierarchically. A hierarchically organized system is represented by a network of interconnected subsystems, each of which has its own network of subsystems, and so on, until some elementary subsystems are reached that are not further decomposed. This original and important book proposes a general mathematical theory of a hierarchical system and shows how it can be applied to very different topics such as physics (Hamiltonian systems), biology (coupling the molecular and the cellular levels), e

  19. Visualization of hierarchically structured information for human-computer interaction

    Energy Technology Data Exchange (ETDEWEB)

    Cheon, Suh Hyun; Lee, J. K.; Choi, I. K.; Kye, S. C.; Lee, N. K. [Dongguk University, Seoul (Korea)

    2001-11-01

    Visualization techniques can be used to support operator's information navigation tasks on the system especially consisting of an enormous volume of information, such as operating information display system and computerized operating procedure system in advanced control room of nuclear power plants. By offering an easy understanding environment of hierarchically structured information, these techniques can reduce the operator's supplementary navigation task load. As a result of that, operators can pay more attention on the primary tasks and ultimately improve the cognitive task performance. In this report, an interface was designed and implemented using hyperbolic visualization technique, which is expected to be applied as a means of optimizing operator's information navigation tasks. 15 refs., 19 figs., 32 tabs. (Author)

  20. AUTOMATION PROGRAM FOR RECOGNITION OF ALGORITHM SOLUTION OF MATHEMATIC TASK

    Directory of Open Access Journals (Sweden)

    Denis N. Butorin

    2014-01-01

    Full Text Available In the article are been describing technology for manage of testing task in computer program. It was found for recognition of algorithm solution of mathematic task. There are been justifi ed the using hierarchical structure for a special set of testing questions. Also, there has been presented the release of the described tasks in the computer program openSEE.