Access Selection Algorithm of Heterogeneous Wireless Networks for Smart Distribution Grid Based on Entropy-Weight and Rough Set

Science.gov (United States)

Xiang, Min; Qu, Qinqin; Chen, Cheng; Tian, Li; Zeng, Lingkang

2017-11-01

To improve the reliability of communication service in smart distribution grid (SDG), an access selection algorithm based on dynamic network status and different service types for heterogeneous wireless networks was proposed. The network performance index values were obtained in real time by multimode terminal and the variation trend of index values was analyzed by the growth matrix. The index weights were calculated by entropy-weight and then modified by rough set to get the final weights. Combining the grey relational analysis to sort the candidate networks, and the optimum communication network is selected. Simulation results show that the proposed algorithm can implement dynamically access selection in heterogeneous wireless networks of SDG effectively and reduce the network blocking probability.

Synchronization in networks with heterogeneous coupling delays

Science.gov (United States)

Otto, Andreas; Radons, Günter; Bachrathy, Dániel; Orosz, Gábor

2018-01-01

Synchronization in networks of identical oscillators with heterogeneous coupling delays is studied. A decomposition of the network dynamics is obtained by block diagonalizing a newly introduced adjacency lag operator which contains the topology of the network as well as the corresponding coupling delays. This generalizes the master stability function approach, which was developed for homogenous delays. As a result the network dynamics can be analyzed by delay differential equations with distributed delay, where different delay distributions emerge for different network modes. Frequency domain methods are used for the stability analysis of synchronized equilibria and synchronized periodic orbits. As an example, the synchronization behavior in a system of delay-coupled Hodgkin-Huxley neurons is investigated. It is shown that the parameter regions where synchronized periodic spiking is unstable expand when increasing the delay heterogeneity.

Spatially correlated heterogeneous aspirations to enhance network reciprocity

Science.gov (United States)

Tanimoto, Jun; Nakata, Makoto; Hagishima, Aya; Ikegaya, Naoki

2012-02-01

Perc & Wang demonstrated that aspiring to be the fittest under conditions of pairwise strategy updating enhances network reciprocity in structured populations playing 2×2 Prisoner's Dilemma games (Z. Wang, M. Perc, Aspiring to the fittest and promoted of cooperation in the Prisoner's Dilemma game, Physical Review E 82 (2010) 021115; M. Perc, Z. Wang, Heterogeneous aspiration promotes cooperation in the Prisoner's Dilemma game, PLOS one 5 (12) (2010) e15117). Through numerical simulations, this paper shows that network reciprocity is even greater if heterogeneous aspirations are imposed. We also suggest why heterogeneous aspiration fosters network reciprocity. It distributes strategy updating speed among agents in a manner that fortifies the initially allocated cooperators' clusters against invasion. This finding prompted us to further enhance the usual heterogeneous aspiration cases for heterogeneous network topologies. We find that a negative correlation between degree and aspiration level does extend cooperation among heterogeneously structured agents.

Global asymptotic stability analysis of bidirectional associative memory neural networks with distributed delays and impulse

International Nuclear Information System (INIS)

Huang Zaitang; Luo Xiaoshu; Yang Qigui

2007-01-01

Many systems existing in physics, chemistry, biology, engineering and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be model by impulsive differential system or impulsive neural networks. This paper formulates and studies a new model of impulsive bidirectional associative memory (BAM) networks with finite distributed delays. Several fundamental issues, such as global asymptotic stability and existence and uniqueness of such BAM neural networks with impulse and distributed delays, are established

Heterogeneous cellular networks

CERN Document Server

Hu, Rose Qingyang

2013-01-01

A timely publication providing coverage of radio resource management, mobility management and standardization in heterogeneous cellular networks The topic of heterogeneous cellular networks has gained momentum in industry and the research community, attracting the attention of standardization bodies such as 3GPP LTE and IEEE 802.16j, whose objectives are looking into increasing the capacity and coverage of the cellular networks. This book focuses on recent progresses,  covering the related topics including scenarios of heterogeneous network deployment, interference management i

Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

Science.gov (United States)

Ly, Cheng

2015-12-01

Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.

Convergence dynamics of hybrid bidirectional associative memory neural networks with distributed delays

International Nuclear Information System (INIS)

Liao Xiaofeng; Wong, K.-W.; Yang Shizhong

2003-01-01

In this Letter, the characteristics of the convergence dynamics of hybrid bidirectional associative memory neural networks with distributed transmission delays are studied. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the Lyapunov functionals are constructed and the generalized Halanay-type inequalities are employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Some examples are given to illustrate the correctness of our results

A Secure Scheme for Distributed Consensus Estimation against Data Falsification in Heterogeneous Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Shichao Mi

2016-02-01

Full Text Available Heterogeneous wireless sensor networks (HWSNs can achieve more tasks and prolong the network lifetime. However, they are vulnerable to attacks from the environment or malicious nodes. This paper is concerned with the issues of a consensus secure scheme in HWSNs consisting of two types of sensor nodes. Sensor nodes (SNs have more computation power, while relay nodes (RNs with low power can only transmit information for sensor nodes. To address the security issues of distributed estimation in HWSNs, we apply the heterogeneity of responsibilities between the two types of sensors and then propose a parameter adjusted-based consensus scheme (PACS to mitigate the effect of the malicious node. Finally, the convergence property is proven to be guaranteed, and the simulation results validate the effectiveness and efficiency of PACS.

Parallelization of applications for networks with homogeneous and heterogeneous processors; Parallelisation d`applications pour des reseaux de processeurs homogenes ou heterogenes

Energy Technology Data Exchange (ETDEWEB)

Colombet, L

1994-10-07

The aim of this thesis is to study and develop efficient methods for parallelization of scientific applications on parallel computers with distributed memory. The first part presents two libraries of PVM (Parallel Virtual Machine) and MPI (Message Passing Interface) communication tools. They allow implementation of programs on most parallel machines, but also on heterogeneous computer networks. This chapter illustrates the problems faced when trying to evaluate performances of networks with heterogeneous processors. To evaluate such performances, the concepts of speed-up and efficiency have been modified and adapted to account for heterogeneity. The second part deals with a study of parallel application libraries such as ScaLAPACK and with the development of communication masking techniques. The general concept is based on communication anticipation, in particular by pipelining message sending operations. Experimental results on Cray T3D and IBM SP1 machines validates the theoretical studies performed on basic algorithms of the libraries discussed above. Two examples of scientific applications are given: the first is a model of young stars for astrophysics and the other is a model of photon trajectories in the Compton effect. (J.S.). 83 refs., 65 figs., 24 tabs.

Short-Term Memory in Orthogonal Neural Networks

Science.gov (United States)

White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim

2004-04-01

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.

Short-term memory in orthogonal neural networks

International Nuclear Information System (INIS)

White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim

2004-01-01

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size

Content-Agnostic Malware Detection in Heterogeneous Malicious Distribution Graph

KAUST Repository

Alabdulmohsin, Ibrahim

2016-10-26

Malware detection has been widely studied by analysing either file dropping relationships or characteristics of the file distribution network. This paper, for the first time, studies a global heterogeneous malware delivery graph fusing file dropping relationship and the topology of the file distribution network. The integration offers a unique ability of structuring the end-to-end distribution relationship. However, it brings large heterogeneous graphs to analysis. In our study, an average daily generated graph has more than 4 million edges and 2.7 million nodes that differ in type, such as IPs, URLs, and files. We propose a novel Bayesian label propagation model to unify the multi-source information, including content-agnostic features of different node types and topological information of the heterogeneous network. Our approach does not need to examine the source codes nor inspect the dynamic behaviours of a binary. Instead, it estimates the maliciousness of a given file through a semi-supervised label propagation procedure, which has a linear time complexity w.r.t. the number of nodes and edges. The evaluation on 567 million real-world download events validates that our proposed approach efficiently detects malware with a high accuracy. © 2016 Copyright held by the owner/author(s).

Overload cascading failure on complex networks with heterogeneous load redistribution

Science.gov (United States)

Hou, Yueyi; Xing, Xiaoyun; Li, Menghui; Zeng, An; Wang, Yougui

2017-09-01

Many real systems including the Internet, power-grid and financial networks experience rare but large overload cascading failures triggered by small initial shocks. Many models on complex networks have been developed to investigate this phenomenon. Most of these models are based on the load redistribution process and assume that the load on a failed node shifts to nearby nodes in the networks either evenly or according to the load distribution rule before the cascade. Inspired by the fact that real power-grid tends to place the excess load on the nodes with high remaining capacities, we study a heterogeneous load redistribution mechanism in a simplified sandpile model in this paper. We find that weak heterogeneity in load redistribution can effectively mitigate the cascade while strong heterogeneity in load redistribution may even enlarge the size of the final failure. With a parameter θ to control the degree of the redistribution heterogeneity, we identify a rather robust optimal θ∗ = 1. Finally, we find that θ∗ tends to shift to a larger value if the initial sand distribution is homogeneous.

Heterogeneous Epidemic Model for Assessing Data Dissemination in Opportunistic Networks

DEFF Research Database (Denmark)

Rozanova, Liudmila; Alekseev, Vadim; Temerev, Alexander

2014-01-01

that amount of data transferred between network nodes possesses a Pareto distribution, implying scale-free properties. In this context, more heterogeneity in susceptibility means the less severe epidemic progression, and, on the contrary, more heterogeneity in infectivity leads to more severe epidemics...... — assuming that the other parameter (either heterogeneity or susceptibility) stays fixed. The results are general enough to be useful for estimating the epidemic progression with no significant acquired immunity — in the cases where Pareto distribution holds....

Jupiter: Peer-to-Peer Networking Platform over Heterogeneous Networks

Directory of Open Access Journals (Sweden)

Norihiro Ishikawa

2006-10-01

Full Text Available Peer-to-peer has entered the public limelight over the last few years. Several research projects are underway on peer-to-peer technologies, but no definitive conclusion is currently available. Compared with traditional Internet technologies, peer-to-peer has the potential to realize highly scalable, extensible, and efficient distributed applications. This is because its basic functions realize resource discovery, resource sharing, and load balancing in a highly distributed manner. An easy prediction is the emergence of an environment in which many sensors, people, and many different kinds of objects exist, move, and communicate with one another. Peer-to-peer is one of the most important and suitable technologies for such networking since it supports discovery mechanisms, simple one-to-one communication between devices, free and extensible distribution of resources, and distributed search to handle the enormous number of resources. The purpose of this study is to explore a universal peer-to-peer network architecture that will allow various devices to communicate with one another across various networks. We have been designing architecture and protocols for realizing peer-to-peer networking among various devices. We are currently designing APIs that are available for various peer-to-peer applications and are implementing a prototype called "Jupiter" as a peer-to-peer networking platform over heterogeneous networks.

Dispersal networks for enhancing bacterial degradation in heterogeneous environments

International Nuclear Information System (INIS)

Banitz, Thomas; Wick, Lukas Y.; Fetzer, Ingo; Frank, Karin; Harms, Hauke; Johst, Karin

2011-01-01

Successful biodegradation of organic soil pollutants depends on their bioavailability to catabolically active microorganisms. In particular, environmental heterogeneities often limit bacterial access to pollutants. Experimental and modelling studies revealed that fungal networks can facilitate bacterial dispersal and may thereby improve pollutant bioavailability. Here, we investigate the influence of such bacterial dispersal networks on biodegradation performance under spatially heterogeneous abiotic conditions using a process-based simulation model. To match typical situations in polluted soils, two types of abiotic conditions are studied: heterogeneous bacterial dispersal conditions and heterogeneous initial resource distributions. The model predicts that networks facilitating bacterial dispersal can enhance biodegradation performance for a wide range of these conditions. Additionally, the time horizon over which this performance is assessed and the network's spatial configuration are key factors determining the degree of biodegradation improvement. Our results support the idea of stimulating the establishment of fungal mycelia for enhanced bioremediation of polluted soils. - Highlights: → Bacterial dispersal networks can considerably improve biodegradation performance. → They facilitate bacterial access to dispersal-limited areas and remote resources. → Abiotic conditions, time horizon and network structure govern the improvements. → Stimulating the establishment of fungal mycelia promises enhanced soil remediation. - Simulation modelling demonstrates that fungus-mediated bacterial dispersal can considerably improve the bioavailability of organic pollutants under spatially heterogeneous abiotic conditions typical for water-unsaturated soils.

Dispersal networks for enhancing bacterial degradation in heterogeneous environments

Energy Technology Data Exchange (ETDEWEB)

Banitz, Thomas, E-mail: thomas.banitz@ufz.de [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Wick, Lukas Y.; Fetzer, Ingo [Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Frank, Karin [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Harms, Hauke [Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Johst, Karin [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany)

2011-10-15

Successful biodegradation of organic soil pollutants depends on their bioavailability to catabolically active microorganisms. In particular, environmental heterogeneities often limit bacterial access to pollutants. Experimental and modelling studies revealed that fungal networks can facilitate bacterial dispersal and may thereby improve pollutant bioavailability. Here, we investigate the influence of such bacterial dispersal networks on biodegradation performance under spatially heterogeneous abiotic conditions using a process-based simulation model. To match typical situations in polluted soils, two types of abiotic conditions are studied: heterogeneous bacterial dispersal conditions and heterogeneous initial resource distributions. The model predicts that networks facilitating bacterial dispersal can enhance biodegradation performance for a wide range of these conditions. Additionally, the time horizon over which this performance is assessed and the network's spatial configuration are key factors determining the degree of biodegradation improvement. Our results support the idea of stimulating the establishment of fungal mycelia for enhanced bioremediation of polluted soils. - Highlights: > Bacterial dispersal networks can considerably improve biodegradation performance. > They facilitate bacterial access to dispersal-limited areas and remote resources. > Abiotic conditions, time horizon and network structure govern the improvements. > Stimulating the establishment of fungal mycelia promises enhanced soil remediation. - Simulation modelling demonstrates that fungus-mediated bacterial dispersal can considerably improve the bioavailability of organic pollutants under spatially heterogeneous abiotic conditions typical for water-unsaturated soils.

Green heterogeneous wireless networks

CERN Document Server

Ismail, Muhammad; Nee, Hans-Peter; Qaraqe, Khalid A; Serpedin, Erchin

2016-01-01

This book focuses on the emerging research topic "green (energy efficient) wireless networks" which has drawn huge attention recently from both academia and industry. This topic is highly motivated due to important environmental, financial, and quality-of-experience (QoE) considerations. Specifically, the high energy consumption of the wireless networks manifests in approximately 2% of all CO2 emissions worldwide. This book presents the authors’ visions and solutions for deployment of energy efficient (green) heterogeneous wireless communication networks. The book consists of three major parts. The first part provides an introduction to the "green networks" concept, the second part targets the green multi-homing resource allocation problem, and the third chapter presents a novel deployment of device-to-device (D2D) communications and its successful integration in Heterogeneous Networks (HetNets). The book is novel in that it specifically targets green networking in a heterogeneous wireless medium, which re...

A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment.

Science.gov (United States)

Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong

2015-12-10

In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network's running and the degree of candidate nodes' effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime.

Heterogeneity and Networks

OpenAIRE

Goyal, S.

2018-01-01

This chapter shows that networks can have large and differentiated effects on behavior and then argues that social and economic pressures facilitate the formation of heterogenous networks. Thus networks can play an important role in understanding the wide diversity in human behaviour and in economic outcomes.

On Optimal Geographical Caching in Heterogeneous Cellular Networks

NARCIS (Netherlands)

Serbetci, Berksan; Goseling, Jasper

2017-01-01

In this work we investigate optimal geographical caching in heterogeneous cellular networks where different types of base stations (BSs) have different cache capacities. Users request files from a content library according to a known probability distribution. The performance metric is the total hit

Heterogeneous game resource distributions promote cooperation in spatial prisoner's dilemma game

Science.gov (United States)

Cui, Guang-Hai; Wang, Zhen; Yang, Yan-Cun; Tian, Sheng-Wen; Yue, Jun

2018-01-01

In social networks, individual abilities to establish interactions are always heterogeneous and independent of the number of topological neighbors. We here study the influence of heterogeneous distributions of abilities on the evolution of individual cooperation in the spatial prisoner's dilemma game. First, we introduced a prisoner's dilemma game, taking into account individual heterogeneous abilities to establish games, which are determined by the owned game resources. Second, we studied three types of game resource distributions that follow the power-law property. Simulation results show that the heterogeneous distribution of individual game resources can promote cooperation effectively, and the heterogeneous level of resource distributions has a positive influence on the maintenance of cooperation. Extensive analysis shows that cooperators with large resource capacities can foster cooperator clusters around themselves. Furthermore, when the temptation to defect is high, cooperator clusters in which the central pure cooperators have larger game resource capacities are more stable than other cooperator clusters.

Contention Modeling for Multithreaded Distributed Shared Memory Machines: The Cray XMT

Energy Technology Data Exchange (ETDEWEB)

Secchi, Simone; Tumeo, Antonino; Villa, Oreste

2011-07-27

Distributed Shared Memory (DSM) machines are a wide class of multi-processor computing systems where a large virtually-shared address space is mapped on a network of physically distributed memories. High memory latency and network contention are two of the main factors that limit performance scaling of such architectures. Modern high-performance computing DSM systems have evolved toward exploitation of massive hardware multi-threading and fine-grained memory hashing to tolerate irregular latencies, avoid network hot-spots and enable high scaling. In order to model the performance of such large-scale machines, parallel simulation has been proved to be a promising approach to achieve good accuracy in reasonable times. One of the most critical factors in solving the simulation speed-accuracy trade-off is network modeling. The Cray XMT is a massively multi-threaded supercomputing architecture that belongs to the DSM class, since it implements a globally-shared address space abstraction on top of a physically distributed memory substrate. In this paper, we discuss the development of a contention-aware network model intended to be integrated in a full-system XMT simulator. We start by measuring the effects of network contention in a 128-processor XMT machine and then investigate the trade-off that exists between simulation accuracy and speed, by comparing three network models which operate at different levels of accuracy. The comparison and model validation is performed by executing a string-matching algorithm on the full-system simulator and on the XMT, using three datasets that generate noticeably different contention patterns.

Heterogeneous delay-induced asynchrony and resonance in a small-world neuronal network system

Science.gov (United States)

Yu, Wen-Ting; Tang, Jun; Ma, Jun; Yang, Xianqing

2016-06-01

A neuronal network often involves time delay caused by the finite signal propagation time in a given biological network. This time delay is not a homogenous fluctuation in a biological system. The heterogeneous delay-induced asynchrony and resonance in a noisy small-world neuronal network system are numerically studied in this work by calculating synchronization measure and spike interval distribution. We focus on three different delay conditions: double-values delay, triple-values delay, and Gaussian-distributed delay. Our results show the following: 1) the heterogeneity in delay results in asynchronous firing in the neuronal network, and 2) maximum synchronization could be achieved through resonance given that the delay values are integer or half-integer times of each other.

On Radio over Fiber for Heterogeneous Wireless Networks

DEFF Research Database (Denmark)

Riaz, M. Tahir; Nielsen, Rasmus Hjorth; Pedersen, Jens Myrup

2009-01-01

The paper provides an overview of the radio over fiber (RoF) technology and its potential use in heterogeneous wireless networks. Wireless communications have seen a huge growth in the last decade. It has been estimated that five in every six people in the entire world will have a mobile phone...... in 2010. The vast growing use of Internet on the mobile devices has also been increased significantly. In order to provide a broadband access for mobile communications, a new wireless infrastructure (fiber optic networks for distributed, extendible heterogeneous radio architectures and service...... provisioning - FUTON) based on RoF technology has been introduced. The project adopts centralized processing of radio signals for number of wireless base stations can enhance the network performance in terms of bandwidth, and QoS parameters. The simplified remote access units (RAU) are expected to not only...

Memory effects induce structure in social networks with activity-driven agents

International Nuclear Information System (INIS)

Medus, A D; Dorso, C O

2014-01-01

Activity-driven modelling has recently been proposed as an alternative growth mechanism for time varying networks,displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections with total disregard of their previous contacts. Thus, such an assumption leads to time-aggregated random networks that do not reproduce the positive degree-degree correlation and high clustering coefficient widely observed in real social networks. In this paper, we aim to study the incidence of the agents' long-term memory on the emergence of new social ties. To this end, we propose a dynamical network model assuming heterogeneous activity for agents, together with a triadic-closure step as main connectivity mechanism. We show that this simple mechanism provides some of the fundamental topological features expected for real social networks in their time-aggregated picture. We derive analytical results and perform extensive numerical simulations in regimes with and without population growth. Finally, we present an illustrative comparison with two case studies, one comprising face-to-face encounters in a closed gathering, while the other one corresponding to social friendship ties from an online social network. (paper)

Automation of multi-agent control for complex dynamic systems in heterogeneous computational network

Science.gov (United States)

Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan

2017-01-01

The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.

Cluster-based service discovery for heterogeneous wireless sensor networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2007-01-01

We propose an energy-efficient service discovery protocol for heterogeneous wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the

A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs).

Science.gov (United States)

Moradi, Saber; Qiao, Ning; Stefanini, Fabio; Indiveri, Giacomo

2018-02-01

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here, we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multicore neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems.

Science.gov (United States)

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

Directory of Open Access Journals (Sweden)

Danish Shehzad

2016-01-01

Full Text Available Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Global exponential stability of bidirectional associative memory neural networks with distributed delays

Science.gov (United States)

Song, Qiankun; Cao, Jinde

2007-05-01

A bidirectional associative memory neural network model with distributed delays is considered. By constructing a new Lyapunov functional, employing the homeomorphism theory, M-matrix theory and the inequality (a[greater-or-equal, slanted]0,bk[greater-or-equal, slanted]0,qk>0 with , and r>1), a sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the model. Moreover, the exponential converging velocity index is estimated, which depends on the delay kernel functions and the system parameters. The results generalize and improve the earlier publications, and remove the usual assumption that the activation functions are bounded . Two numerical examples are given to show the effectiveness of the obtained results.

A balanced memory network.

Directory of Open Access Journals (Sweden)

Yasser Roudi

2007-09-01

Full Text Available A fundamental problem in neuroscience is understanding how working memory--the ability to store information at intermediate timescales, like tens of seconds--is implemented in realistic neuronal networks. The most likely candidate mechanism is the attractor network, and a great deal of effort has gone toward investigating it theoretically. Yet, despite almost a quarter century of intense work, attractor networks are not fully understood. In particular, there are still two unanswered questions. First, how is it that attractor networks exhibit irregular firing, as is observed experimentally during working memory tasks? And second, how many memories can be stored under biologically realistic conditions? Here we answer both questions by studying an attractor neural network in which inhibition and excitation balance each other. Using mean-field analysis, we derive a three-variable description of attractor networks. From this description it follows that irregular firing can exist only if the number of neurons involved in a memory is large. The same mean-field analysis also shows that the number of memories that can be stored in a network scales with the number of excitatory connections, a result that has been suggested for simple models but never shown for realistic ones. Both of these predictions are verified using simulations with large networks of spiking neurons.

Effect of Heterogeneity in Initial Geographic Distribution on Opinions’ Competitiveness

Directory of Open Access Journals (Sweden)

Alexander S. Balankin

2015-05-01

Full Text Available Spin dynamics on networks allows us to understand how a global consensus emerges out of individual opinions. Here, we are interested in the effect of heterogeneity in the initial geographic distribution of a competing opinion on the competitiveness of its own opinion. Accordingly, in this work, we studied the effect of spatial heterogeneity on the majority rule dynamics using a three-state spin model, in which one state is neutral. Monte Carlo simulations were performed on square lattices divided into square blocks (cells. Accordingly, one competing opinion was distributed uniformly among cells, whereas the spatial distribution of the rival opinion was varied from the uniform to heterogeneous, with the median-to-mean ratio in the range from 1 to 0. When the size of discussion group is odd, the uncommitted agents disappear completely after  3.30 ± 0.05 update cycles, and then the system evolves in a two-state regime with complementary spatial distributions of two competing opinions. Even so, the initial heterogeneity in the spatial distribution of one of the competing opinions causes a decrease of this opinion competitiveness. That is, the opinion with initially heterogeneous spatial distribution has less probability to win, than the opinion with the initially uniform spatial distribution, even when the initial concentrations of both opinions are equal. We found that although the time to consensus , the opinion’s recession rate is determined during the first 3.3 update cycles. On the other hand, we found that the initial heterogeneity of the opinion spatial distribution assists the formation of quasi-stable regions, in which this opinion is dominant. The results of Monte Carlo simulations are discussed with regard to the electoral competition of political parties.

Robust quantum network architectures and topologies for entanglement distribution

Science.gov (United States)

Das, Siddhartha; Khatri, Sumeet; Dowling, Jonathan P.

2018-01-01

Entanglement distribution is a prerequisite for several important quantum information processing and computing tasks, such as quantum teleportation, quantum key distribution, and distributed quantum computing. In this work, we focus on two-dimensional quantum networks based on optical quantum technologies using dual-rail photonic qubits for the building of a fail-safe quantum internet. We lay out a quantum network architecture for entanglement distribution between distant parties using a Bravais lattice topology, with the technological constraint that quantum repeaters equipped with quantum memories are not easily accessible. We provide a robust protocol for simultaneous entanglement distribution between two distant groups of parties on this network. We also discuss a memory-based quantum network architecture that can be implemented on networks with an arbitrary topology. We examine networks with bow-tie lattice and Archimedean lattice topologies and use percolation theory to quantify the robustness of the networks. In particular, we provide figures of merit on the loss parameter of the optical medium that depend only on the topology of the network and quantify the robustness of the network against intermittent photon loss and intermittent failure of nodes. These figures of merit can be used to compare the robustness of different network topologies in order to determine the best topology in a given real-world scenario, which is critical in the realization of the quantum internet.

Quartile and Outlier Detection on Heterogeneous Clusters Using Distributed Radix Sort

International Nuclear Information System (INIS)

Meredith, Jeremy S.; Vetter, Jeffrey S.

2011-01-01

In the past few years, performance improvements in CPUs and memory technologies have outpaced those of storage systems. When extrapolated to the exascale, this trend places strict limits on the amount of data that can be written to disk for full analysis, resulting in an increased reliance on characterizing in-memory data. Many of these characterizations are simple, but require sorted data. This paper explores an example of this type of characterization - the identification of quartiles and statistical outliers - and presents a performance analysis of a distributed heterogeneous radix sort as well as an assessment of current architectural bottlenecks.

Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

Science.gov (United States)

Ly, Cheng; Marsat, Gary

2018-02-01

Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

Trade-offs in the distribution of neural networks in a wireless sensor network

NARCIS (Netherlands)

Holenderski, M.J.; Lukkien, J.J.; Tham, C.K.

2005-01-01

This article investigates the tradeoff between communication and memory usage in different methods of distributing neural networks in a Wireless Sensor Network. A structural approach is presented, categorized in two dimensions: horizontal and vertical decomposition. Horizontal decomposition turns

Secure Clustering and Symmetric Key Establishment in Heterogeneous Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Azarderskhsh Reza

2011-01-01

Full Text Available Information security in infrastructureless wireless sensor networks (WSNs is one of the most important research challenges. In these networks, sensor nodes are typically sprinkled liberally in the field in order to monitor, gather, disseminate, and provide the sensed data to the command node. Various studies have focused on key establishment schemes in homogeneous WSNs. However, recent research has shown that achieving survivability in WSNs requires a hierarchy and heterogeneous infrastructure. In this paper, to address security issues in the heterogeneous WSNs, we propose a secure clustering scheme along with a deterministic pairwise key management scheme based on public key cryptography. The proposed security mechanism guarantees that any two sensor nodes located in the same cluster and routing path can directly establish a pairwise key without disclosing any information to other nodes. Through security performance evaluation, it is shown that the proposed scheme guarantees node-to-node authentication, high resiliency against node capture, and minimum memory space requirement.

Comparison between sparsely distributed memory and Hopfield-type neural network models

Science.gov (United States)

Keeler, James D.

1986-01-01

The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

Smart Control of Energy Distribution Grids over Heterogeneous Communication Networks

DEFF Research Database (Denmark)

Schwefel, Hans-Peter; Silva, Nuno; Olsen, Rasmus Løvenstein

2018-01-01

Off-the shelf wireless communication technologies reduce infrastructure deployment costs and are thus attractive for distribution system control. Wireless communication however may lead to variable network performance. Hence the impact of this variability on overall distribution system control be...

Mechanisms of memory storage in a model perirhinal network.

Science.gov (United States)

Samarth, Pranit; Ball, John M; Unal, Gunes; Paré, Denis; Nair, Satish S

2017-01-01

The perirhinal cortex supports recognition and associative memory. Prior unit recording studies revealed that recognition memory involves a reduced responsiveness of perirhinal cells to familiar stimuli whereas associative memory formation is linked to increasing perirhinal responses to paired stimuli. Both effects are thought to depend on perirhinal plasticity but it is unclear how the same network could support these opposite forms of plasticity. However, a recent study showed that when neocortical inputs are repeatedly activated, depression or potentiation could develop, depending on the extent to which the stimulated neocortical activity recruited intrinsic longitudinal connections. We developed a biophysically realistic perirhinal model that reproduced these phenomena and used it to investigate perirhinal mechanisms of associative memory. These analyzes revealed that associative plasticity is critically dependent on a specific subset of neurons, termed conjunctive cells (CCs). When the model network was trained with spatially distributed but coincident neocortical inputs, CCs acquired excitatory responses to the paired inputs and conveyed them to distributed perirhinal sites via longitudinal projections. CC ablation during recall abolished expression of the associative memory. However, CC ablation during training did not prevent memory formation because new CCs emerged, revealing that competitive synaptic interactions governs the formation of CC assemblies.

Homogeneous and Heterogeneous MPSoC Architectures with Network-On-Chip Connectivity for Low-Power and Real-Time Multimedia Signal Processing

Directory of Open Access Journals (Sweden)

Sergio Saponara

2012-01-01

Full Text Available Two multiprocessor system-on-chip (MPSoC architectures are proposed and compared in the paper with reference to audio and video processing applications. One architecture exploits a homogeneous topology; it consists of 8 identical tiles, each made of a 32-bit RISC core enhanced by a 64-bit DSP coprocessor with local memory. The other MPSoC architecture exploits a heterogeneous-tile topology with on-chip distributed memory resources; the tiles act as application specific processors supporting a different class of algorithms. In both architectures, the multiple tiles are interconnected by a network-on-chip (NoC infrastructure, through network interfaces and routers, which allows parallel operations of the multiple tiles. The functional performances and the implementation complexity of the NoC-based MPSoC architectures are assessed by synthesis results in submicron CMOS technology. Among the large set of supported algorithms, two case studies are considered: the real-time implementation of an H.264/MPEG AVC video codec and of a low-distortion digital audio amplifier. The heterogeneous architecture ensures a higher power efficiency and a smaller area occupation and is more suited for low-power multimedia processing, such as in mobile devices. The homogeneous scheme allows for a higher flexibility and easier system scalability and is more suited for general-purpose DSP tasks in power-supplied devices.

Network Coding on Heterogeneous Multi-Core Processors for Wireless Sensor Networks

Science.gov (United States)

Kim, Deokho; Park, Karam; Ro, Won W.

2011-01-01

While network coding is well known for its efficiency and usefulness in wireless sensor networks, the excessive costs associated with decoding computation and complexity still hinder its adoption into practical use. On the other hand, high-performance microprocessors with heterogeneous multi-cores would be used as processing nodes of the wireless sensor networks in the near future. To this end, this paper introduces an efficient network coding algorithm developed for the heterogenous multi-core processors. The proposed idea is fully tested on one of the currently available heterogeneous multi-core processors referred to as the Cell Broadband Engine. PMID:22164053

Efficiency effects of observed and unobserved heterogeneity: Evidence from Norwegian electricity distribution networks

International Nuclear Information System (INIS)

Growitsch, Christian; Jamasb, Tooraj; Wetzel, Heike

2012-01-01

Since the 1990s, efficiency and benchmarking analysis has increasingly been used in network utilities research and regulation. A recurrent concern is the effect of observable environmental factors that are beyond the influence of firms and unobserved factors that are not identifiable on measured cost and quality performance of firms. This paper analyses the effect of observed geographic and weather factors and unobserved heterogeneity on a set of 128 Norwegian electricity distribution utilities for the 2001–2004 period. We utilise data on 78 geographic and weather variables to identify real economic inefficiency while controlling for observed and unobserved heterogeneity. We use the Factor Analysis technique to reduce the number of environmental factors into few composite variables and to avoid the problem of multicollinearity. In order to identify firm-specific inefficiency, we then estimate a pooled version of the established stochastic frontier model of Aigner et al. (1977) and the recent true random effects model of Greene (2004; 2005a,b) without and with environmental variables. The results indicate that the observed environmental factors have a rather limited influence on the utilities' average efficiency and the efficiency rankings. Moreover, the difference between the average efficiency scores and the efficiency rankings among the pooled and the true random effects models imply that the type of SFA model used is highly influencing the efficiency estimates.

Area Green Efficiency (AGE) of Two Tier Heterogeneous Cellular Networks

KAUST Repository

Tabassum, Hina

2012-10-03

Small cell networks are becoming standard part of the future heterogeneous networks. In this paper, we consider a two tier heterogeneous network which promises energy savings by integrating the femto and macro cellular networks and thereby reducing CO2 emissions, operational and capital expenditures (OPEX and CAPEX) whilst enhancing the area spectral efficiency (ASE) of the network. In this context, we define a performance metric which characterize the aggregate energy savings per unit macrocell area and is referred to as area green efficiency (AGE) of the two tier heterogeneous network where the femto base stations are arranged around the edge of the reference macrocell such that the configuration is referred to as femto-on-edge (FOE). The mobile users in macro and femto cellular networks are transmitting with the adaptive power while maintaining the desired link quality such that the energy aware FOE configuration mandates to (i) save energy, and (ii) reduce the co-channel interference. We present a mathematical analysis to incorporate the uplink power control mechanism adopted by the mobile users and calibrate the uplink ASE and AGE of the energy aware FOE configuration. Next, we derive analytical expressions to compute the bounds on the uplink ASE of energy aware FOE configuration and demonstrate that the derived bounds are useful in evaluating the ASE under worst and best case interference scenarios. Simulation results are produced to demonstrate the ASE and AGE improvements in comparison to macro-only and macro-femto configuration with uniformly distributed femtocells.

Individual heterogeneity generating explosive system network dynamics.

Science.gov (United States)

Manrique, Pedro D; Johnson, Neil F

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Individual heterogeneity generating explosive system network dynamics

Science.gov (United States)

Manrique, Pedro D.; Johnson, Neil F.

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Over-Distribution in Source Memory

Science.gov (United States)

Brainerd, C. J.; Reyna, V. F.; Holliday, R. E.; Nakamura, K.

2012-01-01

Semantic false memories are confounded with a second type of error, over-distribution, in which items are attributed to contradictory episodic states. Over-distribution errors have proved to be more common than false memories when the two are disentangled. We investigated whether over-distribution is prevalent in another classic false memory paradigm: source monitoring. It is. Conventional false memory responses (source misattributions) were predominantly over-distribution errors, but unlike semantic false memory, over-distribution also accounted for more than half of true memory responses (correct source attributions). Experimental control of over-distribution was achieved via a series of manipulations that affected either recollection of contextual details or item memory (concreteness, frequency, list-order, number of presentation contexts, and individual differences in verbatim memory). A theoretical model was used to analyze the data (conjoint process dissociation) that predicts that predicts that (a) over-distribution is directly proportional to item memory but inversely proportional to recollection and (b) item memory is not a necessary precondition for recollection of contextual details. The results were consistent with both predictions. PMID:21942494

Faithful qubit transmission in a quantum communication network with heterogeneous channels

Science.gov (United States)

Chen, Na; Zhang, Lin Xi; Pei, Chang Xing

2018-04-01

Quantum communication networks enable long-distance qubit transmission and distributed quantum computation. In this paper, a quantum communication network with heterogeneous quantum channels is constructed. A faithful qubit transmission scheme is presented. Detailed calculations and performance analyses show that even in a low-quality quantum channel with serious decoherence, only modest number of locally prepared target qubits are required to achieve near-deterministic qubit transmission.

Study and realisation of a gateway between a MIL1553 network and an Ethernet network. Implementation of the OS9NET homogeneous network and of the TCP/IP heterogeneous network on the OS-9/68000 real time operating system

International Nuclear Information System (INIS)

Tricot-Censier, Pascal

1989-01-01

This research thesis addresses the design of a homogeneous network for machines acquiring data in real time, and the interconnection by a heterogeneous network of this group with three groups of mini-computers, workstations, and personal computers. Thus, this work involved hardware as well as software designs and developments. After a recall of main notions related to networks, and a presentation of the OS9 real time core, the author reports the design and development of the Ethernet controller card. He reports the design and realisation of a gateway between Ethernet and MIL1553 which is notably based on the previously mentioned controller card. An Ethernet driver is then designed for the OS9 operating system. As the connection with other machines which do not support OS9, requires the development of applications using TCP/IP protocols, the author describes how software designed for PCs have been adapted for a use in real time and for the OS9 operating system. He presents an additional software layer to TCP/IP for an inter-process communication through a heterogeneous network. This layer allows the development of applications distributed among systems which possess different processors and operating systems. Finally, the author presents a test protocol which ensures the fast transfer of memory blocks between OS9 machines [fr

Sparse distributed memory overview

Science.gov (United States)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

Software defined networking applications in distributed datacenters

CERN Document Server

Qi, Heng

2016-01-01

This SpringerBrief provides essential insights on the SDN application designing and deployment in distributed datacenters. In this book, three key problems are discussed: SDN application designing, SDN deployment and SDN management. This book demonstrates how to design the SDN-based request allocation application in distributed datacenters. It also presents solutions for SDN controller placement to deploy SDN in distributed datacenters. Finally, an SDN management system is proposed to guarantee the performance of datacenter networks which are covered and controlled by many heterogeneous controllers. Researchers and practitioners alike will find this book a valuable resource for further study on Software Defined Networking. .

A link prediction method for heterogeneous networks based on BP neural network

Science.gov (United States)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

Handoff Rate and Coverage Analysis in Multi-tier Heterogeneous Networks

OpenAIRE

Sadr, Sanam; Adve, Raviraj S.

2015-01-01

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoff...

Energy-aware Thread and Data Management in Heterogeneous Multi-core, Multi-memory Systems

Energy Technology Data Exchange (ETDEWEB)

Su, Chun-Yi [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

2014-12-16

By 2004, microprocessor design focused on multicore scaling—increasing the number of cores per die in each generation—as the primary strategy for improving performance. These multicore processors typically equip multiple memory subsystems to improve data throughput. In addition, these systems employ heterogeneous processors such as GPUs and heterogeneous memories like non-volatile memory to improve performance, capacity, and energy efficiency. With the increasing volume of hardware resources and system complexity caused by heterogeneity, future systems will require intelligent ways to manage hardware resources. Early research to improve performance and energy efficiency on heterogeneous, multi-core, multi-memory systems focused on tuning a single primitive or at best a few primitives in the systems. The key limitation of past efforts is their lack of a holistic approach to resource management that balances the tradeoff between performance and energy consumption. In addition, the shift from simple, homogeneous systems to these heterogeneous, multicore, multi-memory systems requires in-depth understanding of efficient resource management for scalable execution, including new models that capture the interchange between performance and energy, smarter resource management strategies, and novel low-level performance/energy tuning primitives and runtime systems. Tuning an application to control available resources efficiently has become a daunting challenge; managing resources in automation is still a dark art since the tradeoffs among programming, energy, and performance remain insufficiently understood. In this dissertation, I have developed theories, models, and resource management techniques to enable energy-efficient execution of parallel applications through thread and data management in these heterogeneous multi-core, multi-memory systems. I study the effect of dynamic concurrent throttling on the performance and energy of multi-core, non-uniform memory access

Effect of Heterogeneity of Vertex Activation on Epidemic Spreading in Temporal Networks

Directory of Open Access Journals (Sweden)

Yixin Zhu

2014-01-01

Full Text Available Development of sensor technologies and the prevalence of electronic communication services provide us with a huge amount of data on human communication behavior, including face-to-face conversations, e-mail exchanges, phone calls, message exchanges, and other types of interactions in various online forums. These indirect or direct interactions form potential bridges of the virus spread. For a long time, the study of virus spread is based on the aggregate static network. However, the interaction patterns containing diverse temporal properties may affect dynamic processes as much as the network topology does. Some empirical studies show that the activation time and duration of vertices and links are highly heterogeneous, which means intense activity may be followed by longer intervals of inactivity. We take heterogeneous distribution of the node interactivation time as the research background to build an asynchronous communication model. The two sides of the communication do not have to be active at the same time. One derives the threshold of virus spreading on the communication mode and analyzes the reason the heterogeneous distribution of the vertex interactivation time suppresses the spread of virus. At last, the analysis and results from the model are verified on the BA network.

Modeling and Analysis of New Products Diffusion on Heterogeneous Networks

Directory of Open Access Journals (Sweden)

Shuping Li

2014-01-01

Full Text Available We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.

The CTTC 5G End-to-End Experimental Platform : Integrating Heterogeneous Wireless/Optical Networks, Distributed Cloud, and IoT Devices

OpenAIRE

Munoz, Raul; Mangues-Bafalluy, Josep; Vilalta, Ricard; Verikoukis, Christos; Alonso-Zarate, Jesus; Bartzoudis, Nikolaos; Georgiadis, Apostolos; Payaro, Miquel; Perez-Neira, Ana; Casellas, Ramon; Martinez, Ricardo; Nunez-Martinez, Jose; Requena Esteso, Manuel; Pubill, David; Font-Bach, Oriol

2016-01-01

The Internet of Things (IoT) will facilitate a wide variety of applications in different domains, such as smart cities, smart grids, industrial automation (Industry 4.0), smart driving, assistance of the elderly, and home automation. Billions of heterogeneous smart devices with different application requirements will be connected to the networks and will generate huge aggregated volumes of data that will be processed in distributed cloud infrastructures. On the other hand, there is also a gen...

Network and Service Monitoring in Heterogeneous Home Networks

NARCIS (Netherlands)

Delphinanto, A.

2012-01-01

Home networks are becoming dynamic and technologically heterogeneous. They consist of an increasing number of devices which offer several functionalities and can be used for many different services. In the home, these devices are interconnected using a mixture of networking technologies (for

Socially Aware Heterogeneous Wireless Networks.

Science.gov (United States)

Kosmides, Pavlos; Adamopoulou, Evgenia; Demestichas, Konstantinos; Theologou, Michael; Anagnostou, Miltiades; Rouskas, Angelos

2015-06-11

The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

An approach for heterogeneous and loosely coupled geospatial data distributed computing

Science.gov (United States)

Chen, Bin; Huang, Fengru; Fang, Yu; Huang, Zhou; Lin, Hui

2010-07-01

Most GIS (Geographic Information System) applications tend to have heterogeneous and autonomous geospatial information resources, and the availability of these local resources is unpredictable and dynamic under a distributed computing environment. In order to make use of these local resources together to solve larger geospatial information processing problems that are related to an overall situation, in this paper, with the support of peer-to-peer computing technologies, we propose a geospatial data distributed computing mechanism that involves loosely coupled geospatial resource directories and a term named as Equivalent Distributed Program of global geospatial queries to solve geospatial distributed computing problems under heterogeneous GIS environments. First, a geospatial query process schema for distributed computing as well as a method for equivalent transformation from a global geospatial query to distributed local queries at SQL (Structured Query Language) level to solve the coordinating problem among heterogeneous resources are presented. Second, peer-to-peer technologies are used to maintain a loosely coupled network environment that consists of autonomous geospatial information resources, thus to achieve decentralized and consistent synchronization among global geospatial resource directories, and to carry out distributed transaction management of local queries. Finally, based on the developed prototype system, example applications of simple and complex geospatial data distributed queries are presented to illustrate the procedure of global geospatial information processing.

Majority rule on heterogeneous networks

International Nuclear Information System (INIS)

Lambiotte, R

2008-01-01

We focus on the majority rule (MR) applied on heterogeneous networks. When the underlying topology is homogeneous, the system is shown to exhibit a transition from an ordered regime to a disordered regime when the noise is increased. When the network exhibits modular structures, in contrast, the system may also exhibit an asymmetric regime, where the nodes in each community reach an opposite average opinion. Finally, the node degree heterogeneity is shown to play an important role by displacing the location of the order-disorder transition and by making the system exhibit non-equipartition of the average spin

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

Science.gov (United States)

Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

Meeting the memory challenges of brain-scale network simulation

Directory of Open Access Journals (Sweden)

Susanne eKunkel

2012-01-01

Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

Real-world experimentation of distributed DSA network algorithms

DEFF Research Database (Denmark)

Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão

2013-01-01

such as a dynamic propagation environment, human presence impact and terminals mobility. This chapter focuses on the practical aspects related to the real world-experimentation with distributed DSA network algorithms over a testbed network. Challenges and solutions are extensively discussed, from the testbed design......The problem of spectrum scarcity in uncoordinated and/or heterogeneous wireless networks is the key aspect driving the research in the field of flexible management of frequency resources. In particular, distributed dynamic spectrum access (DSA) algorithms enable an efficient sharing...... to the setup of experiments. A practical example of experimentation process with a DSA algorithm is also provided....

Distributed terascale volume visualization using distributed shared virtual memory

KAUST Repository

Beyer, Johanna

2011-10-01

Table 1 illustrates the impact of different distribution unit sizes, different screen resolutions, and numbers of GPU nodes. We use two and four GPUs (NVIDIA Quadro 5000 with 2.5 GB memory) and a mouse cortex EM dataset (see Figure 2) of resolution 21,494 x 25,790 x 1,850 = 955GB. The size of the virtual distribution units significantly influences the data distribution between nodes. Small distribution units result in a high depth complexity for compositing. Large distribution units lead to a low utilization of GPUs, because in the worst case only a single distribution unit will be in view, which is rendered by only a single node. The choice of an optimal distribution unit size depends on three major factors: the output screen resolution, the block cache size on each node, and the number of nodes. Currently, we are working on optimizing the compositing step and network communication between nodes. © 2011 IEEE.

Management of Distributed and Extendible Heterogeneous Radio Architectures

DEFF Research Database (Denmark)

Ramkumar, Venkata; Mihovska, Albena D.; Prasad, Neeli R.

2009-01-01

Wireless communication systems are dynamic by nature, which comes from several factors, namely: radio propagation impairments, traffic changes, interference conditions, user mobility, etc. In a heterogeneous environment, , the dynamic network behavior calls for a dynamic management of the radio...... resources; a process that associates a large number of parameters and quality/performance indicators that need to be set, measured, analyzed, and optimized. Radio-over-fiber (RoF) technology involves the use of optical fiber links to distribute radio frequency (RF) signals from a central location to remote...

Optical interconnection network for parallel access to multi-rank memory in future computing systems.

Science.gov (United States)

Wang, Kang; Gu, Huaxi; Yang, Yintang; Wang, Kun

2015-08-10

With the number of cores increasing, there is an emerging need for a high-bandwidth low-latency interconnection network, serving core-to-memory communication. In this paper, aiming at the goal of simultaneous access to multi-rank memory, we propose an optical interconnection network for core-to-memory communication. In the proposed network, the wavelength usage is delicately arranged so that cores can communicate with different ranks at the same time and broadcast for flow control can be achieved. A distributed memory controller architecture that works in a pipeline mode is also designed for efficient optical communication and transaction address processes. The scaling method and wavelength assignment for the proposed network are investigated. Compared with traditional electronic bus-based core-to-memory communication, the simulation results based on the PARSEC benchmark show that the bandwidth enhancement and latency reduction are apparent.

Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

Science.gov (United States)

Siddiqui, Maheen; Wedemann, Roseli S.; Jensen, Henrik Jeldtoft

2018-01-01

We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

Dual connectivity for LTE-advanced heterogeneous networks

DEFF Research Database (Denmark)

Wang, Hua; Rosa, Claudio; Pedersen, Klaus I.

2016-01-01

Dual connectivity (DC) allows user equipments (UEs) to receive data simultaneously from different eNodeBs (eNBs) in order to boost the performance in a heterogeneous network with dedicated carrier deployment. Yet, how to efficiently operate with DC opens a number of research questions. In this pa......Dual connectivity (DC) allows user equipments (UEs) to receive data simultaneously from different eNodeBs (eNBs) in order to boost the performance in a heterogeneous network with dedicated carrier deployment. Yet, how to efficiently operate with DC opens a number of research questions...... aggregation (CA) and virtually zerolatency fronthaul connections, and in any case it is significantly higher compared to the case without DC. Keywords: Dual connectivity Heterogeneous network LTE-advanced Radio resource management Performance evaluation...

Short term memory in echo state networks

OpenAIRE

Jaeger, H.

2001-01-01

The report investigates the short-term memory capacity of echo state recurrent neural networks. A quantitative measure MC of short-term memory capacity is introduced. The main result is that MC 5 N for networks with linear Output units and i.i.d. input, where N is network size. Conditions under which these maximal memory capacities are realized are described. Several theoretical and practical examples demonstrate how the short-term memory capacities of echo state networks can be exploited for...

A Complex Network Theory Approach for the Spatial Distribution of Fire Breaks in Heterogeneous Forest Landscapes for the Control of Wildland Fires.

Science.gov (United States)

Russo, Lucia; Russo, Paola; Siettos, Constantinos I

2016-01-01

Based on complex network theory, we propose a computational methodology which addresses the spatial distribution of fuel breaks for the inhibition of the spread of wildland fires on heterogeneous landscapes. This is a two-level approach where the dynamics of fire spread are modeled as a random Markov field process on a directed network whose edge weights are determined by a Cellular Automata model that integrates detailed GIS, landscape and meteorological data. Within this framework, the spatial distribution of fuel breaks is reduced to the problem of finding network nodes (small land patches) which favour fire propagation. Here, this is accomplished by exploiting network centrality statistics. We illustrate the proposed approach through (a) an artificial forest of randomly distributed density of vegetation, and (b) a real-world case concerning the island of Rhodes in Greece whose major part of its forest was burned in 2008. Simulation results show that the proposed methodology outperforms the benchmark/conventional policy of fuel reduction as this can be realized by selective harvesting and/or prescribed burning based on the density and flammability of vegetation. Interestingly, our approach reveals that patches with sparse density of vegetation may act as hubs for the spread of the fire.

A Complex Network Theory Approach for the Spatial Distribution of Fire Breaks in Heterogeneous Forest Landscapes for the Control of Wildland Fires.

Directory of Open Access Journals (Sweden)

Lucia Russo

Full Text Available Based on complex network theory, we propose a computational methodology which addresses the spatial distribution of fuel breaks for the inhibition of the spread of wildland fires on heterogeneous landscapes. This is a two-level approach where the dynamics of fire spread are modeled as a random Markov field process on a directed network whose edge weights are determined by a Cellular Automata model that integrates detailed GIS, landscape and meteorological data. Within this framework, the spatial distribution of fuel breaks is reduced to the problem of finding network nodes (small land patches which favour fire propagation. Here, this is accomplished by exploiting network centrality statistics. We illustrate the proposed approach through (a an artificial forest of randomly distributed density of vegetation, and (b a real-world case concerning the island of Rhodes in Greece whose major part of its forest was burned in 2008. Simulation results show that the proposed methodology outperforms the benchmark/conventional policy of fuel reduction as this can be realized by selective harvesting and/or prescribed burning based on the density and flammability of vegetation. Interestingly, our approach reveals that patches with sparse density of vegetation may act as hubs for the spread of the fire.

Network resiliency through memory health monitoring and proactive management

Science.gov (United States)

Andrade Costa, Carlos H.; Cher, Chen-Yong; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

2017-11-21

A method for managing a network queue memory includes receiving sensor information about the network queue memory, predicting a memory failure in the network queue memory based on the sensor information, and outputting a notification through a plurality of nodes forming a network and using the network queue memory, the notification configuring communications between the nodes.

Software/hardware distributed processing network supporting the Ada environment

Science.gov (United States)

Wood, Richard J.; Pryk, Zen

1993-09-01

A high-performance, fault-tolerant, distributed network has been developed, tested, and demonstrated. The network is based on the MIPS Computer Systems, Inc. R3000 Risc for processing, VHSIC ASICs for high speed, reliable, inter-node communications and compatible commercial memory and I/O boards. The network is an evolution of the Advanced Onboard Signal Processor (AOSP) architecture. It supports Ada application software with an Ada- implemented operating system. A six-node implementation (capable of expansion up to 256 nodes) of the RISC multiprocessor architecture provides 120 MIPS of scalar throughput, 96 Mbytes of RAM and 24 Mbytes of non-volatile memory. The network provides for all ground processing applications, has merit for space-qualified RISC-based network, and interfaces to advanced Computer Aided Software Engineering (CASE) tools for application software development.

HeNCE: A Heterogeneous Network Computing Environment

Directory of Open Access Journals (Sweden)

Adam Beguelin

1994-01-01

Full Text Available Network computing seeks to utilize the aggregate resources of many networked computers to solve a single problem. In so doing it is often possible to obtain supercomputer performance from an inexpensive local area network. The drawback is that network computing is complicated and error prone when done by hand, especially if the computers have different operating systems and data formats and are thus heterogeneous. The heterogeneous network computing environment (HeNCE is an integrated graphical environment for creating and running parallel programs over a heterogeneous collection of computers. It is built on a lower level package called parallel virtual machine (PVM. The HeNCE philosophy of parallel programming is to have the programmer graphically specify the parallelism of a computation and to automate, as much as possible, the tasks of writing, compiling, executing, debugging, and tracing the network computation. Key to HeNCE is a graphical language based on directed graphs that describe the parallelism and data dependencies of an application. Nodes in the graphs represent conventional Fortran or C subroutines and the arcs represent data and control flow. This article describes the present state of HeNCE, its capabilities, limitations, and areas of future research.

Energy Cost Minimization in Heterogeneous Cellular Networks with Hybrid Energy Supplies

Directory of Open Access Journals (Sweden)

Bang Wang

2016-01-01

Full Text Available The ever increasing data demand has led to the significant increase of energy consumption in cellular mobile networks. Recent advancements in heterogeneous cellular networks and green energy supplied base stations provide promising solutions for cellular communications industry. In this article, we first review the motivations and challenges as well as approaches to address the energy cost minimization problem for such green heterogeneous networks. Owing to the diversities of mobile traffic and renewable energy, the energy cost minimization problem involves both temporal and spatial optimization of resource allocation. We next present a new solution to illustrate how to combine the optimization of the temporal green energy allocation and spatial mobile traffic distribution. The whole optimization problem is decomposed into four subproblems, and correspondingly our proposed solution is divided into four parts: energy consumption estimation, green energy allocation, user association, and green energy reallocation. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.

An Analysis of Weakly Consistent Replication Systems in an Active Distributed Network

OpenAIRE

Amit Chougule; Pravin Ghewari

2011-01-01

With the sudden increase in heterogeneity and distribution of data in wide-area networks, more flexible, efficient and autonomous approaches for management and data distribution are needed. In recent years, the proliferation of inter-networks and distributed applications has increased the demand for geographically-distributed replicated databases. The architecture of Bayou provides features that address the needs of database storage of world-wide applications. Key is the use of weak consisten...

Gender differences in working memory networks: a BrainMap meta-analysis.

Science.gov (United States)

Hill, Ashley C; Laird, Angela R; Robinson, Jennifer L

2014-10-01

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigations using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. Copyright © 2014 Elsevier B.V. All rights reserved.

A Forward Authentication Key Management Scheme for Heterogeneous Sensor Networks

Directory of Open Access Journals (Sweden)

Huang Jen-Yan

2011-01-01

Full Text Available Key encryption technology is a basic technique for protecting the secrecy of transmitted data among sensor nodes in wireless sensor networks. However, sensor nodes are inherently limited by insufficient hardware resources such as memory capacity and battery lifetime. As a result, few current key management schemes are appropriate for wireless sensor networks. This paper proposes a new key management method that uses dynamic key management schemes for heterogeneous sensor networks. The proposed scheme loads a hash function into the base station, cluster heads, and sensor nodes. The cluster heads and sensor nodes then generate their own keychains to provide forward authentication in case of key changes, security breaches, key changes due to security breaches. The cluster heads and sensor nodes establish pairwise keys to ensure transmission secrecy. The proposed scheme decreases the number of keys required for sensor nodes and cluster heads and is robust to the following attacks: guessing attacks, replay attacks, man-in-the-middle attacks, node capture attacks, and denial-of-service attacks.

Evolutionary games played by multi-agent system with different memory capacity

Science.gov (United States)

Zhang, Jianlei; Zhang, Chunyan

2015-06-01

The evolution of cooperation is still an enigma. Resolution of cooperative dilemma is a hot topic as a perplexing interdisciplinary project, and has captured wide attention of researchers from many disciplines as a multidisciplinary field. Our main concern is the design of a networked evolutionary game model in which players show difference in memory capability. The idea of different memory capacities has its origin on the pervasive individual heterogeneity of real agents in nature. It is concluded that this proposed multiple memory capacity stimulates cooperation in lattice-structured populations. The networking effect is also investigated via a scale free network which is associated with the heterogeneous populations structure. Interestingly, results suggest that the effectiveness of a heterogeneous network at fostering cooperation is reduced in the presence of individual memory here. A thorough inquiry in the coevolutionary dynamics of individual memory and spatial structure in evolutionary games is planned for the immediate future.

Scaling properties in time-varying networks with memory

Science.gov (United States)

Kim, Hyewon; Ha, Meesoon; Jeong, Hawoong

2015-12-01

The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.

How heterogeneous susceptibility and recovery rates affect the spread of epidemics on networks

Directory of Open Access Journals (Sweden)

Wei Gou

2017-08-01

Full Text Available In this paper, an extended heterogeneous SIR model is proposed, which generalizes the heterogeneous mean-field theory. Different from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree, our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery rates. Then, we analytically study the basic reproductive number and the final epidemic size. Combining with numerical simulations, it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are independent. If the mean of these two distributions is identical, increasing the variance of susceptibility may block the spread of epidemics, while the corresponding increase in the variance of disease course has little effect on the final epidemic size. It is also shown that positive correlations between individual susceptibility, course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence, whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence. Keywords: Networks, Heterogeneity, Susceptibility, Recovery rates, Correlation, The basic reproductive number, The final epidemic size

Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation.

Directory of Open Access Journals (Sweden)

Sergio Verduzco-Flores

2009-08-01

Full Text Available Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1 persistent fixed-frequency elevated rates above baseline, 2 elevated rates that decay throughout the tasks memory period, 3 rates that accelerate throughout the delay, and 4 patterns of inhibited firing (below baseline analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

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.

Effects of network topology on wealth distributions

International Nuclear Information System (INIS)

Garlaschelli, Diego; Loffredo, Maria I

2008-01-01

We focus on the problem of how the wealth is distributed among the units of a networked economic system. We first review the empirical results documenting that in many economies the wealth distribution is described by a combination of the log-normal and power-law behaviours. We then focus on the Bouchaud-Mezard model of wealth exchange, describing an economy of interacting agents connected through an exchange network. We report analytical and numerical results showing that the system self-organizes towards a stationary state whose associated wealth distribution depends crucially on the underlying interaction network. In particular, we show that if the network displays a homogeneous density of links, the wealth distribution displays either the log-normal or the power-law form. This means that the first-order topological properties alone (such as the scale-free property) are not enough to explain the emergence of the empirically observed mixed form of the wealth distribution. In order to reproduce this nontrivial pattern, the network has to be heterogeneously divided into regions with a variable density of links. We show new results detailing how this effect is related to the higher-order correlation properties of the underlying network. In particular, we analyse assortativity by degree and the pairwise wealth correlations, and discuss the effects that these properties have on each other

Heterogeneous networking in the home environment

OpenAIRE

Bolla, Raffaele; Davoli, Franco; Repetto, Matteo; Fragopoulos, Tasos; Serpanos, D.; Chessa, Stefano; Ferro, Erina

2006-01-01

The management and control at multiple protocol layers of a heterogeneous networking structure, to support multimedia applications in the home environment, is considered. The paper examines possible scenarios, and corresponding architectural solutions, also in the light of existing wireless and sensor networks technologies.

Generalized Load Sharing for Homogeneous Networks of Distributed Environment

Directory of Open Access Journals (Sweden)

A. Satheesh

2008-01-01

Full Text Available We propose a method for job migration policies by considering effective usage of global memory in addition to CPU load sharing in distributed systems. When a node is identified for lacking sufficient memory space to serve jobs, one or more jobs of the node will be migrated to remote nodes with low memory allocations. If the memory space is sufficiently large, the jobs will be scheduled by a CPU-based load sharing policy. Following the principle of sharing both CPU and memory resources, we present several load sharing alternatives. Our objective is to reduce the number of page faults caused by unbalanced memory allocations for jobs among distributed nodes, so that overall performance of a distributed system can be significantly improved. We have conducted trace-driven simulations to compare CPU-based load sharing policies with our policies. We show that our load sharing policies not only improve performance of memory bound jobs, but also maintain the same load sharing quality as the CPU-based policies for CPU-bound jobs. Regarding remote execution and preemptive migration strategies, our experiments indicate that a strategy selection in load sharing is dependent on the amount of memory demand of jobs, remote execution is more effective for memory-bound jobs, and preemptive migration is more effective for CPU-bound jobs. Our CPU-memory-based policy using either high performance or high throughput approach and using the remote execution strategy performs the best for both CPU-bound and memory-bound job in homogeneous networks of distributed environment.

Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization

NARCIS (Netherlands)

Takashima, A.; Bakker, I.; Hell, J.G. van; Janzen, G.; McQueen, J.M.

2014-01-01

The complementary learning systems account of declarative memory suggests two distinct memory networks, a fast-mapping, episodic system involving the hippocampus, and a slower semantic memory system distributed across the neocortex in which new information is gradually integrated with existing

Adaptive Monitoring and Control Architectures for Power Distribution Grids over Heterogeneous ICT Networks

DEFF Research Database (Denmark)

Olsen, Rasmus Løvenstein; Hägerling, Christian; Kurtz, Fabian M.

2014-01-01

The expected growth in distributed generation will significantly affect the operation and control of today’s distribution grids. Being confronted with short time power variations of distributed generations, the assurance of a reliable service (grid stability, avoidance of energy losses) and the q......The expected growth in distributed generation will significantly affect the operation and control of today’s distribution grids. Being confronted with short time power variations of distributed generations, the assurance of a reliable service (grid stability, avoidance of energy losses...... to the reliability due to the stochastic behaviour found in such networks. Therefore, key concepts are presented in this paper targeting the support of proper smart grid control in these network environments. An overview on the required Information and Communication Technology (ICT) architecture and its...

Voice over IP in Wireless Heterogeneous Networks

DEFF Research Database (Denmark)

Fathi, Hanane; Chakraborty, Shyam; Prasad, Ramjee

with the deployment of wireless heterogeneous systems, both speech and data traffic are carrried over wireless links by the same IP-based packet-switched infrastructure. However, this combination faces some challenges due to the inherent properties of the wireless network. The requirements for good quality VoIP...... communications are difficult to achieve in a time-varying environment due to channel errors and traffic congestion and across different systems. The provision of VoIP in wireless heterogeneous networks requires a set of time-efficient control mechanisms to support a VoIP session with acceptable quality....... The focus of Voice over IP in Wierless Heterogeneous Networks is on mechanisms that affect the VoIP user satisfaction  while not explicitly involved in the media session. This relates to the extra delays introduced by the security and the signaling protocols used to set up an authorized VoIP session...

Cognitive Heterogeneous Reconfigurable Optical Networks (CHRON): Enabling Technologies and Techniques

DEFF Research Database (Denmark)

Tafur Monroy, Idelfonso; Zibar, Darko; Guerrero Gonzalez, Neil

2011-01-01

We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical...... network to observe, act, learn and optimizes its performance, taking into account its high degree of heterogeneity with respect to quality of service, transmission and switching techniques....

Stochastic Wilson–Cowan models of neuronal network dynamics with memory and delay

International Nuclear Information System (INIS)

Goychuk, Igor; Goychuk, Andriy

2015-01-01

We consider a simple Markovian class of the stochastic Wilson–Cowan type models of neuronal network dynamics, which incorporates stochastic delay caused by the existence of a refractory period of neurons. From the point of view of the dynamics of the individual elements, we are dealing with a network of non-Markovian stochastic two-state oscillators with memory, which are coupled globally in a mean-field fashion. This interrelation of a higher-dimensional Markovian and lower-dimensional non-Markovian dynamics is discussed in its relevance to the general problem of the network dynamics of complex elements possessing memory. The simplest model of this class is provided by a three-state Markovian neuron with one refractory state, which causes firing delay with an exponentially decaying memory within the two-state reduced model. This basic model is used to study critical avalanche dynamics (the noise sustained criticality) in a balanced feedforward network consisting of the excitatory and inhibitory neurons. Such avalanches emerge due to the network size dependent noise (mesoscopic noise). Numerical simulations reveal an intermediate power law in the distribution of avalanche sizes with the critical exponent around −1.16. We show that this power law is robust upon a variation of the refractory time over several orders of magnitude. However, the avalanche time distribution is biexponential. It does not reflect any genuine power law dependence. (paper)

Neural Networks for Time Perception and Working Memory

Science.gov (United States)

Üstün, Sertaç; Kale, Emre H.; Çiçek, Metehan

2017-01-01

Time is an important concept which determines most human behaviors, however questions remain about how time is perceived and which areas of the brain are responsible for time perception. The aim of this study was to evaluate the relationship between time perception and working memory in healthy adults. Functional magnetic resonance imaging (fMRI) was used during the application of a visual paradigm. In all of the conditions, the participants were presented with a moving black rectangle on a gray screen. The rectangle was obstructed by a black bar for a time period and then reappeared again. During different conditions, participants (n = 15, eight male) responded according to the instructions they were given, including details about time and the working memory or dual task requirements. The results showed activations in right dorsolateral prefrontal and right intraparietal cortical networks, together with the anterior cingulate cortex (ACC), anterior insula and basal ganglia (BG) during time perception. On the other hand, working memory engaged the left prefrontal cortex, ACC, left superior parietal cortex, BG and cerebellum activity. Both time perception and working memory were related to a strong peristriate cortical activity. On the other hand, the interaction of time and memory showed activity in the intraparietal sulcus (IPS) and posterior cingulate cortex (PCC). These results support a distributed neural network based model for time perception and that the intraparietal and posterior cingulate areas might play a role in the interface of memory and timing. PMID:28286475

Associative memory in phasing neuron networks

Energy Technology Data Exchange (ETDEWEB)

Nair, Niketh S [ORNL; Bochove, Erik J. [United States Air Force Research Laboratory, Kirtland Air Force Base; Braiman, Yehuda [ORNL

2014-01-01

We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

New Heterogeneous Clustering Protocol for Prolonging Wireless Sensor Networks Lifetime

Directory of Open Access Journals (Sweden)

Md. Golam Rashed

2014-06-01

Full Text Available Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. The network characteristics of existing classical clustering protocols for wireless sensor network are homogeneous. Clustering protocols fail to maintain the stability of the system, especially when nodes are heterogeneous. We have seen that the behavior of Heterogeneous-Hierarchical Energy Aware Routing Protocol (H-HEARP becomes very unstable once the first node dies, especially in the presence of node heterogeneity. In this paper we assume a new clustering protocol whose network characteristics is heterogeneous for prolonging of network lifetime. The computer simulation results demonstrate that the proposed clustering algorithm outperforms than other clustering algorithms in terms of the time interval before the death of the first node (we refer to as stability period. The simulation results also show the high performance of the proposed clustering algorithm for higher values of extra energy brought by more powerful nodes.

Performance Evaluation of a Cluster-Based Service Discovery Protocol for Heterogeneous Wireless Sensor Networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2006-01-01

Abstract—This paper evaluates the performance in terms of resource consumption of a service discovery protocol proposed for heterogeneous Wireless Sensor Networks (WSNs). The protocol is based on a clustering structure, which facilitates the construction of a distributed directory. Nodes with higher

Mnemonic transmission, social contagion, and emergence of collective memory: Influence of emotional valence, group structure, and information distribution.

Science.gov (United States)

Choi, Hae-Yoon; Kensinger, Elizabeth A; Rajaram, Suparna

2017-09-01

Social transmission of memory and its consequence on collective memory have generated enduring interdisciplinary interest because of their widespread significance in interpersonal, sociocultural, and political arenas. We tested the influence of 3 key factors-emotional salience of information, group structure, and information distribution-on mnemonic transmission, social contagion, and collective memory. Participants individually studied emotionally salient (negative or positive) and nonemotional (neutral) picture-word pairs that were completely shared, partially shared, or unshared within participant triads, and then completed 3 consecutive recalls in 1 of 3 conditions: individual-individual-individual (control), collaborative-collaborative (identical group; insular structure)-individual, and collaborative-collaborative (reconfigured group; diverse structure)-individual. Collaboration enhanced negative memories especially in insular group structure and especially for shared information, and promoted collective forgetting of positive memories. Diverse group structure reduced this negativity effect. Unequally distributed information led to social contagion that creates false memories; diverse structure propagated a greater variety of false memories whereas insular structure promoted confidence in false recognition and false collective memory. A simultaneous assessment of network structure, information distribution, and emotional valence breaks new ground to specify how network structure shapes the spread of negative memories and false memories, and the emergence of collective memory. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Cyber Insurance for Heterogeneous Wireless Networks

OpenAIRE

Lu, Xiao; Niyato, Dusit; Jiang, Hai; Wang, Ping; Poor, H. Vincent

2017-01-01

Heterogeneous wireless networks (HWNs) composed of densely deployed base stations of different types with various radio access technologies have become a prevailing trend to accommodate ever-increasing traffic demand in enormous volume. Nowadays, users rely heavily on HWNs for ubiquitous network access that contains valuable and critical information such as financial transactions, e-health, and public safety. Cyber risks, representing one of the most significant threats to network security an...

Visualisation and characterisation of heterogeneous bimodal PDMS networks

DEFF Research Database (Denmark)

Bahrt, Frederikke; Daugaard, Anders Egede; Fleury, Clemence

2014-01-01

The existence of short-chain domains in heterogeneous bimodal PDMS networks has been confirmed visually, for the first time, through confocal fluorescence microscopy. The networks were prepared using a controlled reaction scheme where short PDMS chains were reacted below the gelation point...... bimodal networks with short-chain domains within a long-chain network. The average sizes of the short-chain domains were found to vary from 2.1 to 5.7 mm depending on the short-chain content. The visualised network structure could be correlated thereafter to the elastic properties, which were determined...... by rheology. All heterogeneous bimodal networks displayed significantly lower moduli than mono-modal PDMS elastomers prepared from the long polymer chains. Low-loss moduli as well as low-sol fractions indicate that low-elastic moduli can be obtained without compromising the network's structure...

Data Provenance for Agent-Based Models in a Distributed Memory

Directory of Open Access Journals (Sweden)

Delmar B. Davis

2018-04-01

Full Text Available Agent-Based Models (ABMs assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking and storage requirements for different capture configurations.

Multicell Cooperation for LTE-Advanced Heterogeneous Network Scenarios

DEFF Research Database (Denmark)

Soret, Beatriz; Wang, Hua; Rosa, Claudio

2013-01-01

In this article we present two promising practical use cases for simple multicell cooperation for LTE-Advanced heterogeneous network (HetNet) scenarios with macro and small cells. For co-channel deployment cases, we recommend the use of enhanced inter-cell interference coordination (e......ICIC) to mitigate cross-tier interference and ensure sufficient offload of users from macro to small cells. It is shown how the eICIC benefit is maximized by using a distributed inter-base station control framework for dynamic adjustment of essential parameters. Secondly, for scenarios where macro and small cells...... are deployed at different carriers an efficient use of the fragmented spectrum can be achieved by using collaborative inter-site carrier aggregation. In addition to distributed coordination/collaboration between base station nodes, the importance of explicit terminal assistance is highlighted. Comprehensive...

Distributed Database Semantic Integration of Wireless Sensor Network to Access the Environmental Monitoring System

Directory of Open Access Journals (Sweden)

Ubaidillah Umar

2018-06-01

Full Text Available A wireless sensor network (WSN works continuously to gather information from sensors that generate large volumes of data to be handled and processed by applications. Current efforts in sensor networks focus more on networking and development services for a variety of applications and less on processing and integrating data from heterogeneous sensors. There is an increased need for information to become shareable across different sensors, database platforms, and applications that are not easily implemented in traditional database systems. To solve the issue of these large amounts of data from different servers and database platforms (including sensor data, a semantic sensor web service platform is needed to enable a machine to extract meaningful information from the sensor’s raw data. This additionally helps to minimize and simplify data processing and to deduce new information from existing data. This paper implements a semantic web data platform (SWDP to manage the distribution of data sensors based on the semantic database system. SWDP uses sensors for temperature, humidity, carbon monoxide, carbon dioxide, luminosity, and noise. The system uses the Sesame semantic web database for data processing and a WSN to distribute, minimize, and simplify information processing. The sensor nodes are distributed in different places to collect sensor data. The SWDP generates context information in the form of a resource description framework. The experiment results demonstrate that the SWDP is more efficient than the traditional database system in terms of memory usage and processing time.

Application of reflective memory network in Tokamak fast controller

International Nuclear Information System (INIS)

Weng Chuqiao; Zhang Ming; Liu Rui; Zheng Wei; Zhuang Ge

2014-01-01

A specific application of reflective memory network in Tokamak fast controller was introduced in this paper. The PMC-5565 reflective memory card and ACC-5565 network hub were used to build a reflective memory real-time network to test its real- time function. The real-time, rapidity and determinacy of the time delay for fast controller controlling power device under the reflective memory network were tested in the LabVIEW RT real-time operation system. Depending on the reflective memory technology, the data in several fast controllers were synchronized, and multiple control tasks using a single control task were finished. The experiment results show that the reflective memory network can meet the real-time requirements for fast controller to perform the feedback control over devices. (authors)

Finite Memory Walk and Its Application to Small-World Network

Science.gov (United States)

Oshima, Hiraku; Odagaki, Takashi

2012-07-01

In order to investigate the effects of cycles on the dynamical process on both regular lattices and complex networks, we introduce a finite memory walk (FMW) as an extension of the simple random walk (SRW), in which a walker is prohibited from moving to sites visited during m steps just before the current position. This walk interpolates the simple random walk (SRW), which has no memory (m = 0), and the self-avoiding walk (SAW), which has an infinite memory (m = ∞). We investigate the FMW on regular lattices and clarify the fundamental characteristics of the walk. We find that (1) the mean-square displacement (MSD) of the FMW shows a crossover from the SAW at a short time step to the SRW at a long time step, and the crossover time is approximately equivalent to the number of steps remembered, and that the MSD can be rescaled in terms of the time step and the size of memory; (2) the mean first-return time (MFRT) of the FMW changes significantly at the number of remembered steps that corresponds to the size of the smallest cycle in the regular lattice, where ``smallest'' indicates that the size of the cycle is the smallest in the network; (3) the relaxation time of the first-return time distribution (FRTD) decreases as the number of cycles increases. We also investigate the FMW on the Watts--Strogatz networks that can generate small-world networks, and show that the clustering coefficient of the Watts--Strogatz network is strongly related to the MFRT of the FMW that can remember two steps.

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

OpenAIRE

Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M.; Prayer, Daniela; Langs, Georg; Jakab, András; Schöpf, Veronika

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable mor...

Learning to read aloud: A neural network approach using sparse distributed memory

Science.gov (United States)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Evolving spiking networks with variable resistive memories.

Science.gov (United States)

Howard, Gerard; Bull, Larry; de Lacy Costello, Ben; Gale, Ella; Adamatzky, Andrew

2014-01-01

Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

Properties of a memory network in psychology

International Nuclear Information System (INIS)

Wedemann, Roseli S.; Donangelo, Raul; Carvalho, Luis A. V. de

2007-01-01

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory

Properties of a memory network in psychology

Science.gov (United States)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2007-12-01

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

Distributed-memory matrix computations

DEFF Research Database (Denmark)

Balle, Susanne Mølleskov

1995-01-01

The main goal of this project is to investigate, develop, and implement algorithms for numerical linear algebra on parallel computers in order to acquire expertise in methods for parallel computations. An important motivation for analyzaing and investigating the potential for parallelism in these......The main goal of this project is to investigate, develop, and implement algorithms for numerical linear algebra on parallel computers in order to acquire expertise in methods for parallel computations. An important motivation for analyzaing and investigating the potential for parallelism...... in these algorithms is that many scientific applications rely heavily on the performance of the involved dense linear algebra building blocks. Even though we consider the distributed-memory as well as the shared-memory programming paradigm, the major part of the thesis is dedicated to distributed-memory architectures....... We emphasize distributed-memory massively parallel computers - such as the Connection Machines model CM-200 and model CM-5/CM-5E - available to us at UNI-C and at Thinking Machines Corporation. The CM-200 was at the time this project started one of the few existing massively parallel computers...

Mining Heterogeneous Information Networks by Exploring the Power of Links

Science.gov (United States)

Han, Jiawei

Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneous information networks with several interesting tasks, including link-based object distinction, veracity analysis, multidimensional online analytical processing of heterogeneous information networks, and rank-based clustering. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) Distinct for object distinction analysis, (2) TruthFinder for veracity analysis, (3) Infonet-OLAP for online analytical processing of information networks, and (4) RankClus for integrated ranking-based clustering. We also discuss some of our on-going studies in this direction.

Complete and phase synchronization in a heterogeneous small-world neuronal network

International Nuclear Information System (INIS)

Fang, Han; Qi-Shao, Lu; Quan-Bao, Ji; Marian, Wiercigroch

2009-01-01

Synchronous firing of neurons is thought to be important for information communication in neuronal networks. This paper investigates the complete and phase synchronization in a heterogeneous small-world chaotic Hindmarsh–Rose neuronal network. The effects of various network parameters on synchronization behaviour are discussed with some biological explanations. Complete synchronization of small-world neuronal networks is studied theoretically by the master stability function method. It is shown that the coupling strength necessary for complete or phase synchronization decreases with the neuron number, the node degree and the connection density are increased. The effect of heterogeneity of neuronal networks is also considered and it is found that the network heterogeneity has an adverse effect on synchrony. (general)

Evidence for anomalous network connectivity during working memory encoding in schizophrenia: an ICA based analysis.

Directory of Open Access Journals (Sweden)

Shashwath A Meda

2009-11-01

widely-distributed neural networks engaged for working memory cognition.

High speed vision processor with reconfigurable processing element array based on full-custom distributed memory

Science.gov (United States)

Chen, Zhe; Yang, Jie; Shi, Cong; Qin, Qi; Liu, Liyuan; Wu, Nanjian

2016-04-01

In this paper, a hybrid vision processor based on a compact full-custom distributed memory for near-sensor high-speed image processing is proposed. The proposed processor consists of a reconfigurable processing element (PE) array, a row processor (RP) array, and a dual-core microprocessor. The PE array includes two-dimensional processing elements with a compact full-custom distributed memory. It supports real-time reconfiguration between the PE array and the self-organized map (SOM) neural network. The vision processor is fabricated using a 0.18 µm CMOS technology. The circuit area of the distributed memory is reduced markedly into 1/3 of that of the conventional memory so that the circuit area of the vision processor is reduced by 44.2%. Experimental results demonstrate that the proposed design achieves correct functions.

CM Net heterogeneous local network of microcomputers

International Nuclear Information System (INIS)

Gutche, R.Kh.

1988-01-01

Realization of the CM Net heterogeneous local network for the PDP-11 type computers with the RT-11 operational system control is described. The main advantage of the network is the realization of virtual disks in general form including a system unit. the network can be used everywhere, where the problem on computer connection by simple interfaces is stated. 6 refs

Services Recommendation System based on Heterogeneous Network Analysis in Cloud Computing

OpenAIRE

Junping Dong; Qingyu Xiong; Junhao Wen; Peng Li

2014-01-01

Resources are provided mainly in the form of services in cloud computing. In the distribute environment of cloud computing, how to find the needed services efficiently and accurately is the most urgent problem in cloud computing. In cloud computing, services are the intermediary of cloud platform, services are connected by lots of service providers and requesters and construct the complex heterogeneous network. The traditional recommendation systems only consider the functional and non-functi...

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

EGSNRC distributed systems on commercial network

International Nuclear Information System (INIS)

McCormack, J.M.

2001-01-01

Full text: EGSnrc is a Monte Carlo based simulation program for determining radiation dose distribution within a body. Computational times are large as each individual photon path must be calculated and every energy absorption event stored. This means that EGSnrc lends itself to distributed processing, as each photon is independent of the next, and code is included within the package to enable this. EGSnrc is currently only supported on Unix based computer systems, whilst the department has ∼45 Pentium II and III class workstations all operating under Windows NT within a Novell network. This investigation demonstrates the capability of a windows based system to perform distributed computation of EGSnrc. All Unix scripts were modified to work as one single Windows NT batch file. The source code was then compiled using the gcc C compiler (a Windows NT version of the Unix compiler) without modification of the underlying source code. A small Visual Basic program was used as a trigger to start the simulation as a Windows NT service, with Novell Z.E.N. Works to distribute the trigger code to each system. When a trigger was received, the computer began a simulation as a low priority task in such a way that the user did not see anything on the screen, and so the simulation did not slow down the general running of the computer. The results were then transferred to the network, and collated on a central computer. As an unattended system, a calculation can start within 15 minutes of any desired time, calculate the desired results, and return the results for collation. This demonstrated effectively a distributed Windows NT TM EGSnrc system. Simulations must be chosen carefully to ensure that each photon can be considered independent, as photon histories do not get distributed. Each system that was used for EGSnrc was required to be capable of running the full EGSnrc simulation on its own EGSnrc stored the entire result array locally, so a large, high-resolution body required

Distributed Interplanetary Delay/Disruption Tolerant Network (DTN) Monitor and Control System

Science.gov (United States)

Wang, Shin-Ywan

2012-01-01

The main purpose of Distributed interplanetary Delay Tolerant Network Monitor and Control System as a DTN system network management implementation in JPL is defined to provide methods and tools that can monitor the DTN operation status, detect and resolve DTN operation failures in some automated style while either space network or some heterogeneous network is infused with DTN capability. In this paper, "DTN Monitor and Control system in Deep Space Network (DSN)" exemplifies a case how DTN Monitor and Control system can be adapted into a space network as it is DTN enabled.

Seamless and secure communications over heterogeneous wireless networks

CERN Document Server

Cao, Jiannong

2014-01-01

This brief provides an overview of the requirements, challenges, design issues and major techniques for seamless and secure communications over heterogeneous wireless networks. It summarizes and provides detailed insights into the latest research on handoff management, mobility management, fast authentication and security management to support seamless and secure roaming for mobile clients. The reader will also learn about the challenges in developing relevant technologies and providing ubiquitous Internet access over heterogeneous wireless networks. The authors have extensive experience in im

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...

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

Energy Technology Data Exchange (ETDEWEB)

Jin, Shuangshuang; Chen, Yousu; Wu, Di; Diao, Ruisheng; Huang, Zhenyu

2015-12-09

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Message Passing Interface (MPI) on distributed-memory clusters, respectively. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.

Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features.

Science.gov (United States)

Gao, Zhong-Ke; Jin, Ning-De; Wang, Wen-Xu; Lai, Ying-Cheng

2010-07-01

The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

Heterogeneous Agent Model with Memory and Asset Price Behaviour

Czech Academy of Sciences Publication Activity Database

Vošvrda, Miloslav; Vácha, Lukáš

2003-01-01

Roč. 12, č. 2 (2003), s. 155-168 ISSN 1210-0455 R&D Projects: GA ČR GA402/00/0439; GA ČR GA402/01/0034 Institutional research plan: CEZ:AV0Z1075907 Keywords : efficient markets hypothesis * technical trading rules * heterogeneous agent model with memory and learning Subject RIV: AH - Economics

Semantical Markov Logic Network for Distributed Reasoning in Cyber-Physical Systems

Directory of Open Access Journals (Sweden)

Abdul-Wahid Mohammed

2017-01-01

Full Text Available The challenges associated with developing accurate models for cyber-physical systems are attributable to the intrinsic concurrent and heterogeneous computations of these systems. Even though reasoning based on interconnected domain specific ontologies shows promise in enhancing modularity and joint functionality modelling, it has become necessary to build interoperable cyber-physical systems due to the growing pervasiveness of these systems. In this paper, we propose a semantically oriented distributed reasoning architecture for cyber-physical systems. This model accomplishes reasoning through a combination of heterogeneous models of computation. Using the flexibility of semantic agents as a formal representation for heterogeneous computational platforms, we define autonomous and intelligent agent-based reasoning procedure for distributed cyber-physical systems. Sensor networks underpin the semantic capabilities of this architecture, and semantic reasoning based on Markov logic networks is adopted to address uncertainty in modelling. To illustrate feasibility of this approach, we present a Markov logic based semantic event model for cyber-physical systems and discuss a case study of event handling and processing in a smart home.

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer

Science.gov (United States)

Li, Site; Zhu, Xiaomei; Liu, Bingya; Wang, Gaowei; Ao, Ping

2015-01-01

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity. PMID:25962957

A high-performance network for a distributed-control system

International Nuclear Information System (INIS)

Cuttone, G.; Aghion, F.; Giove, D.

1989-01-01

Local area networks play a central rule in modern distributed-control systems for accelerators. For a superconducting cyclotron under construction at the University of Milan, an optical Ethernet network has been implemented for the interconnection of multicomputer-based stations. Controller boards, with VLSI protocol chips, have been used. The higher levels of the ISO OSI model have been implemented to suit real-time control requirements. The experimental setup for measuring the data throughput between stations will be described. The effect of memory-to-memory data transfer with respect to the packet size has been studied for packets ranging from 200 bytes to 10 Kbytes. Results, showing the data throughput to range from 0.2 to 1.1 Mbit/s, will be discussed. (orig.)

On the area spectral efficiency improvement of heterogeneous network by exploiting the integration of macro-femto cellular networks

KAUST Repository

Shakir, Muhammad

2012-06-01

Heterogeneous networks are an attractive means of expanding mobile network capacity. A heterogeneous network is typically composed of multiple radio access technologies (RATs) where the base stations are transmitting with variable power. In this paper, we consider a Heterogeneous network where we complement the macrocell network with low-power low-cost user deployed nodes, such as femtocell base stations to increase the mean achievable capacity of the system. In this context, we integrate macro-femto cellular networks and derive the area spectral efficiency of the proposed two tier Heterogeneous network. We consider the deployment of femtocell base stations around the edge of the macrocell such that this configuration is referred to as femto-on-edge (FOE) configuration. Moreover, FOE configuration mandates reduction in intercell interference due to the mobile users which are located around the edge of the macrocell since these femtocell base stations are low-power nodes which has significantly lower transmission power than macrocell base stations. We present a mathematical analysis to calculate the instantaneous carrier to interference ratio (CIR) of the desired mobile user in macro and femto cellular networks and determine the total area spectral efficiency of the Heterogeneous network. Details of the simulation processes are included to support the analysis and show the efficacy of the proposed deployment. It has been shown that the proposed setup of the Heterogeneous network offers higher area spectral efficiency which aims to fulfill the expected demand of the future mobile users. © 2012 IEEE.

Topology influences performance in the associative memory neural networks

International Nuclear Information System (INIS)

Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang

2006-01-01

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

Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Muhammad Aslam

2017-01-01

Full Text Available Effective utilization of energy resources in Wireless Sensor Networks (WSNs has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC protocol that executes a Hybrid Clustering Algorithm (HCA to perform optimal centralized selection of Cluster-Heads (CHs within radius of centrally located Base Station (BS and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.

Hydraulic Parameter Generation Technique Using a Discrete Fracture Network with Bedrock Heterogeneity in Korea

Directory of Open Access Journals (Sweden)

Jae-Yeol Cheong

2017-12-01

Full Text Available In instances of damage to engineered barriers containing nuclear waste material, surrounding bedrock is a natural barrier that retards radionuclide movement by way of adsorption and delay due to groundwater flow through highly tortuous fractured rock pathways. At the Gyeongju nuclear waste disposal site, groundwater mainly flows through granitic and sedimentary rock fractures. Therefore, to understand the nuclide migration path, it is necessary to understand discrete fracture networks based on heterogeneous fracture orientations, densities, and size characteristics. In this study, detailed heterogeneous fracture distribution, including the density and orientation of the fractures, was considered for a region that has undergone long periods of change from various geological activities at and around the Gyeongju site. A site-scale discrete fracture network (DFN model was constructed taking into account: (i regional fracture heterogeneity constrained by a multiple linear regression analysis of fracture intensity on faults and electrical resistivity; and (ii the connectivity of conductive fractures having fracture hydraulic parameters, using transient flow simulation. Geometric and hydraulic heterogeneity of the DFN was upscaled into equivalent porous media for flow and transport simulation for a large-scale model.

Radiation detection and situation management by distributed sensor networks

International Nuclear Information System (INIS)

Jan, Frigo; Mielke, Angela; Cai, D. Michael

2009-01-01

Detection of radioactive materials in an urban environment usually requires large, portal-monitor-style radiation detectors. However, this may not be a practical solution in many transport scenarios. Alternatively, a distributed sensor network (DSN) could complement portal-style detection of radiological materials through the implementation of arrays of low cost, small heterogeneous sensors with the ability to detect the presence of radioactive materials in a moving vehicle over a specific region. In this paper, we report on the use of a heterogeneous, wireless, distributed sensor network for traffic monitoring in a field demonstration. Through wireless communications, the energy spectra from different radiation detectors are combined to improve the detection confidence. In addition, the DSN exploits other sensor technologies and algorithms to provide additional information about the vehicle, such as its speed, location, class (e.g. car, truck), and license plate number. The sensors are in-situ and data is processed in real-time at each node. Relevant information from each node is sent to a base station computer which is used to assess the movement of radioactive materials

Distributed-Memory Fast Maximal Independent Set

Energy Technology Data Exchange (ETDEWEB)

Kanewala Appuhamilage, Thejaka Amila J.; Zalewski, Marcin J.; Lumsdaine, Andrew

2017-09-13

The Maximal Independent Set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of MIS problems suggests the use of distributed-memory hardware as a cost-effective approach to providing necessary compute and memory resources. Luby proposed four randomized algorithms to solve the MIS problem. All those algorithms are designed focusing on shared-memory machines and are analyzed using the PRAM model. These algorithms do not have direct efficient distributed-memory implementations. In this paper, we extend two of Luby’s seminal MIS algorithms, “Luby(A)” and “Luby(B),” to distributed-memory execution, and we evaluate their performance. We compare our results with the “Filtered MIS” implementation in the Combinatorial BLAS library for two types of synthetic graph inputs.

Two-Layer Feedback Neural Networks with Associative Memories

International Nuclear Information System (INIS)

Gui-Kun, Wu; Hong, Zhao

2008-01-01

We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly. (general)

Cooperative networking in a heterogeneous wireless medium

CERN Document Server

Ismail, Muhammad

2013-01-01

This brief focuses on radio resource allocation in a heterogeneous wireless medium. It presents radio resource allocation algorithms with decentralized implementation, which support both single-network and multi-homing services. The brief provides a set of cooperative networking algorithms, which rely on the concepts of short-term call traffic load prediction, network cooperation, convex optimization, and decomposition theory. In the proposed solutions, mobile terminals play an active role in the resource allocation operation, instead of their traditional role as passive service recipients in the networking environment.

A short-term neural network memory

Energy Technology Data Exchange (ETDEWEB)

Morris, R.J.T.; Wong, W.S.

1988-12-01

Neural network memories with storage prescriptions based on Hebb's rule are known to collapse as more words are stored. By requiring that the most recently stored word be remembered precisely, a new simple short-term neutral network memory is obtained and its steady state capacity analyzed and simulated. Comparisons are drawn with Hopfield's method, the delta method of Widrow and Hoff, and the revised marginalist model of Mezard, Nadal, and Toulouse.

CT Identification and Fractal Characterization of 3-D Propagation and Distribution of Hydrofracturing Cracks in Low-Permeability Heterogeneous Rocks

Science.gov (United States)

Liu, Peng; Ju, Yang; Gao, Feng; Ranjith, Pathegama G.; Zhang, Qianbing

2018-03-01

Understanding and characterization of the three-dimensional (3-D) propagation and distribution of hydrofracturing cracks in heterogeneous rock are key for enhancing the stimulation of low-permeability petroleum reservoirs. In this study, we investigated the propagation and distribution characteristics of hydrofracturing cracks, by conducting true triaxial hydrofracturing tests and computed tomography on artificial heterogeneous rock specimens. Silica sand, Portland cement, and aedelforsite were mixed to create artificial heterogeneous rock specimens using the data of mineral compositions, coarse gravel distribution, and mechanical properties that were measured from the natural heterogeneous glutenite cores. To probe the effects of material heterogeneity on hydrofracturing cracks, the artificial homogenous specimens were created using the identical matrix compositions of the heterogeneous rock specimens and then fractured for comparison. The effects of horizontal geostress ratio on the 3-D growth and distribution of cracks during hydrofracturing were examined. A fractal-based method was proposed to characterize the complexity of fractures and the efficiency of hydrofracturing stimulation of heterogeneous media. The material heterogeneity and horizontal geostress ratio were found to significantly influence the 3-D morphology, growth, and distribution of hydrofracturing cracks. A horizontal geostress ratio of 1.7 appears to be the upper limit for the occurrence of multiple cracks, and higher ratios cause a single crack perpendicular to the minimum horizontal geostress component. The fracturing efficiency is associated with not only the fractured volume but also the complexity of the crack network.

Synchronous message-based communication for distributed heterogeneous systems

International Nuclear Information System (INIS)

Wilkinson, N.; Dohan, D.

1992-01-01

The use of a synchronous, message-based real-time operating system (Unison) as the basis of transparent interprocess and inter-processor communication over VME-bus is described. The implementation of a synchronous, message-based protocol for network communication between heterogeneous systems is discussed. In particular, the design and implementation of a message-based session layer over a virtual circuit transport layer protocol using UDP/IP is described. Inter-process communication is achieved via a message-based semantic which is portable by virtue of its ease of implementation in other operating system environments. Protocol performance for network communication among heterogeneous architecture is presented, including VMS, Unix, Mach and Unison. (author)

Optimal resource allocation solutions for heterogeneous cognitive radio networks

Directory of Open Access Journals (Sweden)

Babatunde Awoyemi

2017-05-01

Full Text Available Cognitive radio networks (CRN are currently gaining immense recognition as the most-likely next-generation wireless communication paradigm, because of their enticing promise of mitigating the spectrum scarcity and/or underutilisation challenge. Indisputably, for this promise to ever materialise, CRN must of necessity devise appropriate mechanisms to judiciously allocate their rather scarce or limited resources (spectrum and others among their numerous users. ‘Resource allocation (RA in CRN', which essentially describes mechanisms that can effectively and optimally carry out such allocation, so as to achieve the utmost for the network, has therefore recently become an important research focus. However, in most research works on RA in CRN, a highly significant factor that describes a more realistic and practical consideration of CRN has been ignored (or only partially explored, i.e., the aspect of the heterogeneity of CRN. To address this important aspect, in this paper, RA models that incorporate the most essential concepts of heterogeneity, as applicable to CRN, are developed and the imports of such inclusion in the overall networking are investigated. Furthermore, to fully explore the relevance and implications of the various heterogeneous classifications to the RA formulations, weights are attached to the different classes and their effects on the network performance are studied. In solving the developed complex RA problems for heterogeneous CRN, a solution approach that examines and exploits the structure of the problem in achieving a less-complex reformulation, is extensively employed. This approach, as the results presented show, makes it possible to obtain optimal solutions to the rather difficult RA problems of heterogeneous CRN.

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model

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Melanie Weber

2017-11-01

Full Text Available Neurodegenerative diseases and traumatic brain injuries (TBI are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS, which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i to extend Hopfield's model for associative memory to account for the effects of FAS, (ii to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model

Science.gov (United States)

Weber, Melanie; Maia, Pedro D.; Kutz, J. Nathan

2017-01-01

Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS), which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i) to extend Hopfield's model for associative memory to account for the effects of FAS, (ii) to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii) to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive deficits. PMID

3D Game Content Distributed Adaptation in Heterogeneous Environments

Directory of Open Access Journals (Sweden)

Berretty Robert-Paul

2007-01-01

Full Text Available Most current multiplayer 3D games can only be played on a single dedicated platform (a particular computer, console, or cell phone, requiring specifically designed content and communication over a predefined network. Below we show how, by using signal processing techniques such as multiresolution representation and scalable coding for all the components of a 3D graphics object (geometry, texture, and animation, we enable online dynamic content adaptation, and thus delivery of the same content over heterogeneous networks to terminals with very different profiles, and its rendering on them. We present quantitative results demonstrating how the best displayed quality versus computational complexity versus bandwidth tradeoffs have been achieved, given the distributed resources available over the end-to-end content delivery chain. Additionally, we use state-of-the-art, standardised content representation and compression formats (MPEG-4 AFX, JPEG 2000, XML, enabling deployment over existing infrastructure, while keeping hooks to well-established practices in the game industry.

Parallel SN algorithms in shared- and distributed-memory environments

International Nuclear Information System (INIS)

Haghighat, Alireza; Hunter, Melissa A.; Mattis, Ronald E.

1995-01-01

Different 2-D spatial domain partitioning Sn transport theory algorithms have been developed on the basis of the Block-Jacobi iterative scheme. These algorithms have been incorporated into TWOTRAN-II, and tested on a shared-memory CRAY Y-MP C90 and a distributed-memory IBM SP1. For a series of fixed source r-z geometry homogeneous problems, parallel efficiencies in a range of 50-90% are achieved on the C90 with 6 processors, and lower values (20-60%) are obtained on the SP1. It is demonstrated that better performance is attainable if one addresses issues such as convergence rate, load-balancing, and granularity for both architectures, as well as message passing (network bandwidth and latency) for SP1. (author). 17 refs, 4 figs

Dynamical behaviour of neuronal networks iterated with memory

International Nuclear Information System (INIS)

Melatagia, P.M.; Ndoundam, R.; Tchuente, M.

2005-11-01

We study memory iteration where the updating consider a longer history of each site and the set of interaction matrices is palindromic. We analyze two different ways of updating the networks: parallel iteration with memory and sequential iteration with memory that we introduce in this paper. For parallel iteration, we define Lyapunov functional which permits us to characterize the periods behaviour and explicitly bounds the transient lengths of neural networks iterated with memory. For sequential iteration, we use an algebraic invariant to characterize the periods behaviour of the studied model of neural computation. (author)

Social working memory: neurocognitive networks and directions for future research.

Science.gov (United States)

Meyer, Meghan L; Lieberman, Matthew D

2012-01-01

Navigating the social world requires the ability to maintain and manipulate information about people's beliefs, traits, and mental states. We characterize this capacity as social working memory (SWM). To date, very little research has explored this phenomenon, in part because of the assumption that general working memory systems would support working memory for social information. Various lines of research, however, suggest that social cognitive processing relies on a neurocognitive network (i.e., the "mentalizing network") that is functionally distinct from, and considered antagonistic with, the canonical working memory network. Here, we review evidence suggesting that demanding social cognition requires SWM and that both the mentalizing and canonical working memory neurocognitive networks support SWM. The neural data run counter to the common finding of parametric decreases in mentalizing regions as a function of working memory demand and suggest that the mentalizing network can support demanding cognition, when it is demanding social cognition. Implications for individual differences in social cognition and pathologies of social cognition are discussed.

Seamless Data Services for Real Time Communication in a Heterogeneous Networks using Network Tracking and Management

OpenAIRE

T, Adiline Macriga.; Kumar, Dr. P. Anandha

2010-01-01

Heterogeneous Networks is the integration of all existing networks under a single environment with an understanding between the functional operations and also includes the ability to make use of multiple broadband transport technologies and to support generalized mobility. It is a challenging feature for Heterogeneous networks to integrate several IP-based access technologies in a seamless way. The focus of this paper is on the requirements of a mobility management scheme for multimedia real-...

Coordinated Collaboration between Heterogeneous Distributed Energy Resources

Directory of Open Access Journals (Sweden)

Shahin Abdollahy

2014-01-01

Full Text Available A power distribution feeder, where a heterogeneous set of distributed energy resources is deployed, is examined by simulation. The energy resources include PV, battery storage, natural gas GenSet, fuel cells, and active thermal storage for commercial buildings. The resource scenario considered is one that may exist in a not too distant future. Two cases of interaction between different resources are examined. One interaction involves a GenSet used to partially offset the duty cycle of a smoothing battery connected to a large PV system. The other example involves the coordination of twenty thermal storage devices, each associated with a commercial building. Storage devices are intended to provide maximum benefit to the building, but it is shown that this can have a deleterious effect on the overall system, unless the action of the individual storage devices is coordinated. A network based approach is also introduced to calculate some type of effectiveness metric to all available resources which take part in coordinated operation. The main finding is that it is possible to achieve synergy between DERs on a system; however this required a unified strategy to coordinate the action of all devices in a decentralized way.

A trade-off between local and distributed information processing associated with remote episodic versus semantic memory.

Science.gov (United States)

Heisz, Jennifer J; Vakorin, Vasily; Ross, Bernhard; Levine, Brian; McIntosh, Anthony R

2014-01-01

Episodic memory and semantic memory produce very different subjective experiences yet rely on overlapping networks of brain regions for processing. Traditional approaches for characterizing functional brain networks emphasize static states of function and thus are blind to the dynamic information processing within and across brain regions. This study used information theoretic measures of entropy to quantify changes in the complexity of the brain's response as measured by magnetoencephalography while participants listened to audio recordings describing past personal episodic and general semantic events. Personal episodic recordings evoked richer subjective mnemonic experiences and more complex brain responses than general semantic recordings. Critically, we observed a trade-off between the relative contribution of local versus distributed entropy, such that personal episodic recordings produced relatively more local entropy whereas general semantic recordings produced relatively more distributed entropy. Changes in the relative contributions of local and distributed entropy to the total complexity of the system provides a potential mechanism that allows the same network of brain regions to represent cognitive information as either specific episodes or more general semantic knowledge.

Dopamine D1 signaling organizes network dynamics underlying working memory.

Science.gov (United States)

Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

2016-06-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

Science.gov (United States)

Wan, Mengting; Ouyang, Yunbo; Kaplan, Lance; Han, Jiawei

2015-01-01

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model ( Grempt ), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

Measuring the effects of heterogeneity on distributed systems

Science.gov (United States)

El-Toweissy, Mohamed; Zeineldine, Osman; Mukkamala, Ravi

1991-01-01

Distributed computer systems in daily use are becoming more and more heterogeneous. Currently, much of the design and analysis studies of such systems assume homogeneity. This assumption of homogeneity has been mainly driven by the resulting simplicity in modeling and analysis. A simulation study is presented which investigated the effects of heterogeneity on scheduling algorithms for hard real time distributed systems. In contrast to previous results which indicate that random scheduling may be as good as a more complex scheduler, this algorithm is shown to be consistently better than a random scheduler. This conclusion is more prevalent at high workloads as well as at high levels of heterogeneity.

Cooperation in memory-based prisoner's dilemma game on interdependent networks

Science.gov (United States)

Luo, Chao; Zhang, Xiaolin; Liu, Hong; Shao, Rui

2016-05-01

Memory or so-called experience normally plays the important role to guide the human behaviors in real world, that is essential for rational decisions made by individuals. Hence, when the evolutionary behaviors of players with bounded rationality are investigated, it is reasonable to make an assumption that players in system are with limited memory. Besides, in order to unravel the intricate variability of complex systems in real world and make a highly integrative understanding of their dynamics, in recent years, interdependent networks as a comprehensive network structure have obtained more attention in this community. In this article, the evolution of cooperation in memory-based prisoner's dilemma game (PDG) on interdependent networks composed by two coupled square lattices is studied. Herein, all or part of players are endowed with finite memory ability, and we focus on the mutual influence of memory effect and interdependent network reciprocity on cooperation of spatial PDG. We show that the density of cooperation can be significantly promoted within an optimal region of memory length and interdependent strength. Furthermore, distinguished by whether having memory ability/external links or not, each kind of players on networks would have distinct evolutionary behaviors. Our work could be helpful to understand the emergence and maintenance of cooperation under the evolution of memory-based players on interdependent networks.

SDN Based User-Centric Framework for Heterogeneous Wireless Networks

Directory of Open Access Journals (Sweden)

Zhaoming Lu

2016-01-01

Full Text Available Due to the rapid growth of mobile data traffic, more and more basestations and access points (APs have been densely deployed to provide users with ubiquitous network access, which make current wireless network a complex heterogeneous network (HetNet. However, traditional wireless networks are designed with network-centric approaches where different networks have different quality of service (QoS strategies and cannot easily cooperate with each other to serve network users. Massive network infrastructures could not assure users perceived network and service quality, which is an indisputable fact. To address this issue, we design a new framework for heterogeneous wireless networks with the principle of user-centricity, refactoring the network from users’ perspective to suffice their requirements and preferences. Different from network-centric approaches, the proposed framework takes advantage of Software Defined Networking (SDN and virtualization technology, which will bring better perceived services quality for wireless network users. In the proposed user-centric framework, control plane and data plane are decoupled to manage the HetNets in a flexible and coadjutant way, and resource virtualization technology is introduced to abstract physical resources of HetNets into unified virtualized resources. Hence, ubiquitous and undifferentiated network connectivity and QoE (quality of experience driven fine-grained resource management could be achieved for wireless network users.

Social working memory: Neurocognitive networks and directions for future research

Directory of Open Access Journals (Sweden)

Meghan L Meyer

2012-12-01

Full Text Available Navigating the social world requires the ability to maintain and manipulate information about people’s beliefs, traits, and mental states. We characterize this capacity as social working memory. To date, very little research has explored this phenomenon, in part because of the assumption that general working memory systems would support working memory for social information. Various lines of research, however, suggest that social cognitive processing relies on a neurocognitive network (i.e., the ‘mentalizing network’ that is functionally distinct from, and considered antagonistic with, the canonical working memory network. Here, we review evidence suggesting that demanding social cognition requires social working memory and that both the mentalizing and canonical working memory neurocognitive networks support social working memory. The neural data run counter to the common finding of parametric decreases in mentalizing regions as a function of working memory demand and suggest that the mentalizing network can support demanding cognition, when it is demanding social cognition. Implications for individual differences in social cognition and pathologies of social cognition are discussed.

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

Capacity of oscillatory associative-memory networks with error-free retrieval

International Nuclear Information System (INIS)

Nishikawa, Takashi; Lai Yingcheng; Hoppensteadt, Frank C.

2004-01-01

Networks of coupled periodic oscillators (similar to the Kuramoto model) have been proposed as models of associative memory. However, error-free retrieval states of such oscillatory networks are typically unstable, resulting in a near zero capacity. This puts the networks at disadvantage as compared with the classical Hopfield network. Here we propose a simple remedy for this undesirable property and show rigorously that the error-free capacity of our oscillatory, associative-memory networks can be made as high as that of the Hopfield network. They can thus not only provide insights into the origin of biological memory, but can also be potentially useful for applications in information science and engineering

Iconic memory and parietofrontal network: fMRI study using temporal integration.

Science.gov (United States)

Saneyoshi, Ayako; Niimi, Ryosuke; Suetsugu, Tomoko; Kaminaga, Tatsuro; Yokosawa, Kazuhiko

2011-08-03

We investigated the neural basis of iconic memory using functional magnetic resonance imaging. The parietofrontal network of selective attention is reportedly relevant to readout from iconic memory. We adopted a temporal integration task that requires iconic memory but not selective attention. The results showed that the task activated the parietofrontal network, confirming that the network is involved in readout from iconic memory. We further tested a condition in which temporal integration was performed by visual short-term memory but not by iconic memory. However, no brain region revealed higher activation for temporal integration by iconic memory than for temporal integration by visual short-term memory. This result suggested that there is no localized brain region specialized for iconic memory per se.

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

International Nuclear Information System (INIS)

Arik, Sabri

2006-01-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

Science.gov (United States)

Arik, Sabri

2006-02-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature.

Network printing in a heterogenous environment

International Nuclear Information System (INIS)

Beyer, C.; Schroth, G.

2001-01-01

Mail and printing are often said to be the most visible services for the user in the network. Though many people talked about the paperless bureau a few years ago it seems that the more digital data is accessible, the more it gets printed. Print management in a heterogenous network environments is typically crossing all operating systems. Each of those brings its own requirements and different printing system implementations with individual user interfaces. The scope is to give the user the advantage and features of the native interface of their operating system while making administration tasks as easy as possible by following the general ideas of a centralised network service on the server side

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design

OpenAIRE

Rhu, Minsoo; Gimelshein, Natalia; Clemons, Jason; Zulfiqar, Arslan; Keckler, Stephen W.

2016-01-01

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU...

Memory networks supporting retrieval effort and retrieval success under conditions of full and divided attention.

Science.gov (United States)

Skinner, Erin I; Fernandes, Myra A; Grady, Cheryl L

2009-01-01

We used a multivariate analysis technique, partial least squares (PLS), to identify distributed patterns of brain activity associated with retrieval effort and retrieval success. Participants performed a recognition memory task under full attention (FA) or two different divided attention (DA) conditions during retrieval. Behaviorally, recognition was disrupted when a word, but not digit-based distracting task, was performed concurrently with retrieval. PLS was used to identify patterns of brain activation that together covaried with the three memory conditions and which were functionally connected with activity in the right hippocampus to produce successful memory performance. Results indicate that activity in the right dorsolateral frontal cortex increases during conditions of DA at retrieval, and that successful memory performance in the DA-digit condition is associated with activation of the same network of brain regions functionally connected to the right hippocampus, as under FA, which increases with increasing memory performance. Finally, DA conditions that disrupt successful memory performance (DA-word) interfere with recruitment of both retrieval-effort and retrieval-success networks.

Economic networks: Heterogeneity-induced vulnerability and loss of synchronization

Science.gov (United States)

Colon, Célian; Ghil, Michael

2017-12-01

Interconnected systems are prone to propagation of disturbances, which can undermine their resilience to external perturbations. Propagation dynamics can clearly be affected by potential time delays in the underlying processes. We investigate how such delays influence the resilience of production networks facing disruption of supply. Interdependencies between economic agents are modeled using systems of Boolean delay equations (BDEs); doing so allows us to introduce heterogeneity in production delays and in inventories. Complex network topologies are considered that reproduce realistic economic features, including a network of networks. Perturbations that would otherwise vanish can, because of delay heterogeneity, amplify and lead to permanent disruptions. This phenomenon is enabled by the interactions between short cyclic structures. Difference in delays between two interacting, and otherwise resilient, structures can in turn lead to loss of synchronization in damage propagation and thus prevent recovery. Finally, this study also shows that BDEs on complex networks can lead to metastable relaxation oscillations, which are damped out in one part of a network while moving on to another part.

A Memory Efficient Network Encryption Scheme

Science.gov (United States)

El-Fotouh, Mohamed Abo; Diepold, Klaus

In this paper, we studied the two widely used encryption schemes in network applications. Shortcomings have been found in both schemes, as these schemes consume either more memory to gain high throughput or low memory with low throughput. The need has aroused for a scheme that has low memory requirements and in the same time possesses high speed, as the number of the internet users increases each day. We used the SSM model [1], to construct an encryption scheme based on the AES. The proposed scheme possesses high throughput together with low memory requirements.

Attention-based Memory Selection Recurrent Network for Language Modeling

OpenAIRE

Liu, Da-Rong; Chuang, Shun-Po; Lee, Hung-yi

2016-01-01

Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information about the words it have seen before in the sentence, and thus the useful long-term information may be ignored when predicting the next words. In this paper, we propose Attention-based Memory Selection Recurrent Network (AMSRN), in which the model can review the information stored in the memory at each previous time ...

Cognitive, Heterogeneous and Reconfigurable Optical Networks: The CHRON Project

DEFF Research Database (Denmark)

Caballero Jambrina, Antonio; Borkowski, Robert; de Miguel, Ignacio

2014-01-01

. The incorporation of cognitive techniques can help to optimize a network by employing mechanisms that can observe, act, learn and improve network performance, taking into account end-to-end goals. The EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network proposes a strategy to efficiently control...... the network by implementing cognition. In this paper we present a survey of different techniques developed throughout the course of the project to apply cognition in future optical networks....

Modular structure of functional networks in olfactory memory.

Science.gov (United States)

Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre

2014-07-15

Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas

Resource management for heterogeneous networks in LTE systems

CERN Document Server

Hu, Rose Qingyang

2014-01-01

This SpringerBrief provides an in-depth look at the key issues that affect the performance of heterogeneous networks and presents schemes that can effectively tackle these issues. In particular, this book discusses unbalanced traffic load among the macro and micro Base Stations (BSs) caused by the transmit power disparity, and a load-balancing based mobile association scheme to balance the traffic load among the macro and micro BSs. This book also introduces a fractional frequency reuse (FFR) scheme with proper power control to help reduce interference at the UEs which are most vulnerable to such intra-cell interference. The last section investigates radio resource allocation issues for heterogeneous networks with cooperative relays, and proposes a resource allocation framework that could achieve proportional fairness among the UEs. Numerical results are provided to demonstrate the effectiveness of the proposed solutions in tackling the problem and improving network performance. Resource Management for Hetero...

A Time-predictable Memory Network-on-Chip

DEFF Research Database (Denmark)

Schoeberl, Martin; Chong, David VH; Puffitsch, Wolfgang

2014-01-01

To derive safe bounds on worst-case execution times (WCETs), all components of a computer system need to be time-predictable: the processor pipeline, the caches, the memory controller, and memory arbitration on a multicore processor. This paper presents a solution for time-predictable memory...... arbitration and access for chip-multiprocessors. The memory network-on-chip is organized as a tree with time-division multiplexing (TDM) of accesses to the shared memory. The TDM based arbitration completely decouples processor cores and allows WCET analysis of the memory accesses on individual cores without...

Heterogeneous Gossip

Science.gov (United States)

Frey, Davide; Guerraoui, Rachid; Kermarrec, Anne-Marie; Koldehofe, Boris; Mogensen, Martin; Monod, Maxime; Quéma, Vivien

Gossip-based information dissemination protocols are considered easy to deploy, scalable and resilient to network dynamics. Load-balancing is inherent in these protocols as the dissemination work is evenly spread among all nodes. Yet, large-scale distributed systems are usually heterogeneous with respect to network capabilities such as bandwidth. In practice, a blind load-balancing strategy might significantly hamper the performance of the gossip dissemination.

Graph properties of synchronized cortical networks during visual working memory maintenance.

Science.gov (United States)

Palva, Satu; Monto, Simo; Palva, J Matias

2010-02-15

Oscillatory synchronization facilitates communication in neuronal networks and is intimately associated with human cognition. Neuronal activity in the human brain can be non-invasively imaged with magneto- (MEG) and electroencephalography (EEG), but the large-scale structure of synchronized cortical networks supporting cognitive processing has remained uncharacterized. We combined simultaneous MEG and EEG (MEEG) recordings with minimum-norm-estimate-based inverse modeling to investigate the structure of oscillatory phase synchronized networks that were active during visual working memory (VWM) maintenance. Inter-areal phase-synchrony was quantified as a function of time and frequency by single-trial phase-difference estimates of cortical patches covering the entire cortical surfaces. The resulting networks were characterized with a number of network metrics that were then compared between delta/theta- (3-6 Hz), alpha- (7-13 Hz), beta- (16-25 Hz), and gamma- (30-80 Hz) frequency bands. We found several salient differences between frequency bands. Alpha- and beta-band networks were more clustered and small-world like but had smaller global efficiency than the networks in the delta/theta and gamma bands. Alpha- and beta-band networks also had truncated-power-law degree distributions and high k-core numbers. The data converge on showing that during the VWM-retention period, human cortical alpha- and beta-band networks have a memory-load dependent, scale-free small-world structure with densely connected core-like structures. These data further show that synchronized dynamic networks underlying a specific cognitive state can exhibit distinct frequency-dependent network structures that could support distinct functional roles. Copyright 2009 Elsevier Inc. All rights reserved.

Heterogeneous Networks for the IoT and Machine Type Communications

OpenAIRE

Abrignani, Melchiorre Danilo

2016-01-01

The Internet of Things promises to be a key-factor in the forthcoming industrial and social revolution. The Internet of Things concept rely on pervasive communications where ’things’ are ’always connected’. The focus of the thesis is on Heterogeneous Networks for Internet of Things and Machine Type Communications. Heterogeneous Networks are an enabling factor of paramount important in order to achieve the ’always connected’ paradigm. On the other hand, Machine Type Communications are deeply d...

Data and Network Science for Noisy Heterogeneous Systems

Science.gov (United States)

Rider, Andrew Kent

2013-01-01

Data in many growing fields has an underlying network structure that can be taken advantage of. In this dissertation we apply data and network science to problems in the domains of systems biology and healthcare. Data challenges in these fields include noisy, heterogeneous data, and a lack of ground truth. The primary thesis of this work is that…

Efficient implementation of multidimensional fast fourier transform on a distributed-memory parallel multi-node computer

Science.gov (United States)

Bhanot, Gyan V [Princeton, NJ; Chen, Dong [Croton-On-Hudson, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2012-01-10

The present in invention is directed to a method, system and program storage device for efficiently implementing a multidimensional Fast Fourier Transform (FFT) of a multidimensional array comprising a plurality of elements initially distributed in a multi-node computer system comprising a plurality of nodes in communication over a network, comprising: distributing the plurality of elements of the array in a first dimension across the plurality of nodes of the computer system over the network to facilitate a first one-dimensional FFT; performing the first one-dimensional FFT on the elements of the array distributed at each node in the first dimension; re-distributing the one-dimensional FFT-transformed elements at each node in a second dimension via "all-to-all" distribution in random order across other nodes of the computer system over the network; and performing a second one-dimensional FFT on elements of the array re-distributed at each node in the second dimension, wherein the random order facilitates efficient utilization of the network thereby efficiently implementing the multidimensional FFT. The "all-to-all" re-distribution of array elements is further efficiently implemented in applications other than the multidimensional FFT on the distributed-memory parallel supercomputer.

Mobility management for SIP sessions in a heterogeneous network environment

NARCIS (Netherlands)

Romijn, Willem A.; Plas, Dirk-Jaap; Bijwaard, D.; Meeuwissen, Erik; van Ooijen, Gijs

2004-01-01

The next generation of communication networks is expected to create a heterogeneous network environment encompassing an ever-increasing number of different access networks and end-user terminals that will enable the introduction of and provide access to numerous feature-rich end-user services. It is

Epidemic Spreading with Heterogeneous Awareness on Human Networks

Directory of Open Access Journals (Sweden)

Yanling Lu

2017-01-01

Full Text Available The spontaneous awareness behavioral responses of individuals have a significant impact on epidemic spreading. In this paper, a modified Susceptible-Alert-Infected-Susceptible (SAIS epidemic model with heterogeneous awareness is presented to study epidemic spreading in human networks and the impact of heterogeneous awareness on epidemic dynamics. In this model, when susceptible individuals receive awareness information about the presence of epidemic from their infected neighbor nodes, they will become alert individuals with heterogeneous awareness rate. Theoretical analysis and numerical simulations show that heterogeneous awareness can enhance the epidemic threshold with certain conditions and reduce the scale of virus outbreaks compared with no awareness. What is more, for the same awareness parameter, it also shows that heterogeneous awareness can slow effectively the spreading size and does not delay the arrival time of epidemic spreading peak compared with homogeneous awareness.

Modeling the heterogeneity of human dynamics based on the measurements of influential users in Sina Microblog

Science.gov (United States)

Wang, Chenxu; Guan, Xiaohong; Qin, Tao; Yang, Tao

2015-06-01

Online social network has become an indispensable communication tool in the information age. The development of microblog also provides us a great opportunity to study human dynamics that play a crucial role in the design of efficient communication systems. In this paper we study the characteristics of the tweeting behavior based on the data collected from Sina Microblog. The user activity level is measured to characterize how often a user posts a tweet. We find that the user activity level follows a bimodal distribution. That is, the microblog users tend to be either active or inactive. The inter-tweeting time distribution is then measured at both the aggregate and individual levels. We find that the inter-tweeting time follows a piecewise power law distribution of two tails. Furthermore, the exponents of the two tails have different correlations with the user activity level. These findings demonstrate that the dynamics of the tweeting behavior are heterogeneous in different time scales. We then develop a dynamic model co-driven by the memory and the interest mechanism to characterize the heterogeneity. The numerical simulations validate the model and verify that the short time interval tweeting behavior is driven by the memory mechanism while the long time interval behavior by the interest mechanism.

Heterogeneous LTE-Advanced Network Expansion for 1000x Capacity

DEFF Research Database (Denmark)

Hu, Liang; Sanchez, Maria Laura Luque; Maternia, Michal

2013-01-01

this paper studies LTE (Long-Term Evolution)-Advanced heterogeneous network expansion in a dense urban environment for a 1000 times capacity increase and a 10 times increase in minimum user data rate requirements. The radio network capacity enhancement via outdoor and indoor small cell densificat...

Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks.

Science.gov (United States)

Wadud, Zahid; Javaid, Nadeem; Khan, Muhammad Awais; Alrajeh, Nabil; Alabed, Mohamad Souheil; Guizani, Nadra

2017-07-21

In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.

Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization.

Science.gov (United States)

Takashima, Atsuko; Bakker, Iske; van Hell, Janet G; Janzen, Gabriele; McQueen, James M

2014-01-01

The complementary learning systems account of declarative memory suggests two distinct memory networks, a fast-mapping, episodic system involving the hippocampus, and a slower semantic memory system distributed across the neocortex in which new information is gradually integrated with existing representations. In this study, we investigated the extent to which these two networks are involved in the integration of novel words into the lexicon after extensive learning, and how the involvement of these networks changes after 24h. In particular, we explored whether having richer information at encoding influences the lexicalization trajectory. We trained participants with two sets of novel words, one where exposure was only to the words' phonological forms (the form-only condition), and one where pictures of unfamiliar objects were associated with the words' phonological forms (the picture-associated condition). A behavioral measure of lexical competition (indexing lexicalization) indicated stronger competition effects for the form-only words. Imaging (fMRI) results revealed greater involvement of phonological lexical processing areas immediately after training in the form-only condition, suggesting that tight connections were formed between novel words and existing lexical entries already at encoding. Retrieval of picture-associated novel words involved the episodic/hippocampal memory system more extensively. Although lexicalization was weaker in the picture-associated condition, overall memory strength was greater when tested after a 24hour delay, probably due to the availability of both episodic and lexical memory networks to aid retrieval. It appears that, during lexicalization of a novel word, the relative involvement of different memory networks differs according to the richness of the information about that word available at encoding. © 2013.

Distributed clone detection in static wireless sensor networks: random walk with network division.

Science.gov (United States)

Khan, Wazir Zada; Aalsalem, Mohammed Y; Saad, N M

2015-01-01

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

Distributed clone detection in static wireless sensor networks: random walk with network division.

Directory of Open Access Journals (Sweden)

Wazir Zada Khan

Full Text Available Wireless Sensor Networks (WSNs are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

Distributed Gradient Descent for Solving Optimal Power Flow in Radial Networks

OpenAIRE

Gan, Lingwen; Low, Steven H.

2016-01-01

Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control, One embodiment includes a node controller comprising a memory containing: a plurality of node operating parameters; and a plurality of node operating parameters describing operating parameters for a set of at least one node selected from the group consisting of at least one downstream node and at least one upstream node; wherein the processor is configured by the ...

Total recall in distributive associative memories

Science.gov (United States)

Danforth, Douglas G.

1991-01-01

Iterative error correction of asymptotically large associative memories is equivalent to a one-step learning rule. This rule is the inverse of the activation function of the memory. Spectral representations of nonlinear activation functions are used to obtain the inverse in closed form for Sparse Distributed Memory, Selected-Coordinate Design, and Radial Basis Functions.

A generalized voter model with time-decaying memory on a multilayer network

Science.gov (United States)

Zhong, Li-Xin; Xu, Wen-Juan; Chen, Rong-Da; Zhong, Chen-Yang; Qiu, Tian; Shi, Yong-Dong; Wang, Li-Liang

2016-09-01

By incorporating a multilayer network and time-decaying memory into the original voter model, we investigate the coupled effects of spatial and temporal accumulation of peer pressure on the consensus. Heterogeneity in peer pressure and the time-decaying mechanism are both shown to be detrimental to the consensus. We find the transition points below which a consensus can always be reached and above which two opposed opinions are more likely to coexist. Our mean-field analysis indicates that the phase transitions in the present model are governed by the cumulative influence of peer pressure and the updating threshold. We find a functional relation between the consensus threshold and the decay rate of the influence of peer is found. As to the pressure. The time required to reach a consensus is governed by the coupling of the memory length and the decay rate. An intermediate decay rate may greatly reduce the time required to reach a consensus.

The European FP7 CHRON Project: Towards Cognitive Heterogeneous Reconfigurable Optical Networks

DEFF Research Database (Denmark)

Miguel, I. de; Duran, R.J.; Abril, E.J.

2010-01-01

We provide an overview of the EU-funded CHRON project, which addresses the challenge of controlling and managing the next generation of heterogeneous optical networks supporting the Future Internet. Such networks will be of a highly heterogeneous nature in terms of both a wide variety of services...... is on the essential elements of the proposed cognitive network, in particular a cognitive decision system, an intelligent monitoring system, and a cognitive control system....

Short-term memory in networks of dissociated cortical neurons.

Science.gov (United States)

Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J

2013-01-30

Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.

Heterogeneous Distribution of Chromium on Mercury

Science.gov (United States)

Nittler, L. R.; Boujibar, A.; Crapster-Pregont, E.; Frank, E. A.; McCoy, T. J.; McCubbin, F. M.; Starr, R. D.; Vander Kaaden, K. E.; Vorburger, A.; Weider, S. Z.

2018-05-01

Mercury's surface has an average Cr/Si ratio of 0.003 (Cr 800 ppm), with at least a factor of 2 systematic uncertainty. Cr is heterogeneously distributed and correlated with Mg, Ca, S, and Fe and anti-correlated with Al.

Cognitive resource management for heterogeneous cellular networks

CERN Document Server

Liu, Yongkang

2014-01-01

This Springer Brief focuses on cognitive resource management in heterogeneous cellular networks (Het Net) with small cell deployment for the LTE-Advanced system. It introduces the Het Net features, presents practical approaches using cognitive radio technology in accommodating small cell data relay and optimizing resource allocation and examines the effectiveness of resource management among small cells given limited coordination bandwidth and wireless channel uncertainty. The authors introduce different network characteristics of small cell, investigate the mesh of small cell access points in

A note on exponential convergence of neural networks with unbounded distributed delays

Energy Technology Data Exchange (ETDEWEB)

Chu Tianguang [Intelligent Control Laboratory, Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing 100871 (China)]. E-mail: chutg@pku.edu.cn; Yang Haifeng [Intelligent Control Laboratory, Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing 100871 (China)

2007-12-15

This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113-8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network.

A note on exponential convergence of neural networks with unbounded distributed delays

International Nuclear Information System (INIS)

Chu Tianguang; Yang Haifeng

2007-01-01

This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113-8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network

A distributed scheduling algorithm for heterogeneous real-time systems

Science.gov (United States)

Zeineldine, Osman; El-Toweissy, Mohamed; Mukkamala, Ravi

1991-01-01

Much of the previous work on load balancing and scheduling in distributed environments was concerned with homogeneous systems and homogeneous loads. Several of the results indicated that random policies are as effective as other more complex load allocation policies. The effects of heterogeneity on scheduling algorithms for hard real time systems is examined. A distributed scheduler specifically to handle heterogeneities in both nodes and node traffic is proposed. The performance of the algorithm is measured in terms of the percentage of jobs discarded. While a random task allocation is very sensitive to heterogeneities, the algorithm is shown to be robust to such non-uniformities in system components and load.

Real-time video streaming in mobile cloud over heterogeneous wireless networks

Science.gov (United States)

Abdallah-Saleh, Saleh; Wang, Qi; Grecos, Christos

2012-06-01

Recently, the concept of Mobile Cloud Computing (MCC) has been proposed to offload the resource requirements in computational capabilities, storage and security from mobile devices into the cloud. Internet video applications such as real-time streaming are expected to be ubiquitously deployed and supported over the cloud for mobile users, who typically encounter a range of wireless networks of diverse radio access technologies during their roaming. However, real-time video streaming for mobile cloud users across heterogeneous wireless networks presents multiple challenges. The network-layer quality of service (QoS) provision to support high-quality mobile video delivery in this demanding scenario remains an open research question, and this in turn affects the application-level visual quality and impedes mobile users' perceived quality of experience (QoE). In this paper, we devise a framework to support real-time video streaming in this new mobile video networking paradigm and evaluate the performance of the proposed framework empirically through a lab-based yet realistic testing platform. One particular issue we focus on is the effect of users' mobility on the QoS of video streaming over the cloud. We design and implement a hybrid platform comprising of a test-bed and an emulator, on which our concept of mobile cloud computing, video streaming and heterogeneous wireless networks are implemented and integrated to allow the testing of our framework. As representative heterogeneous wireless networks, the popular WLAN (Wi-Fi) and MAN (WiMAX) networks are incorporated in order to evaluate effects of handovers between these different radio access technologies. The H.264/AVC (Advanced Video Coding) standard is employed for real-time video streaming from a server to mobile users (client nodes) in the networks. Mobility support is introduced to enable continuous streaming experience for a mobile user across the heterogeneous wireless network. Real-time video stream packets

Integrating CLIPS applications into heterogeneous distributed systems

Science.gov (United States)

Adler, Richard M.

1991-01-01

SOCIAL is an advanced, object-oriented development tool for integrating intelligent and conventional applications across heterogeneous hardware and software platforms. SOCIAL defines a family of 'wrapper' objects called agents, which incorporate predefined capabilities for distributed communication and control. Developers embed applications within agents and establish interactions between distributed agents via non-intrusive message-based interfaces. This paper describes a predefined SOCIAL agent that is specialized for integrating C Language Integrated Production System (CLIPS)-based applications. The agent's high-level Application Programming Interface supports bidirectional flow of data, knowledge, and commands to other agents, enabling CLIPS applications to initiate interactions autonomously, and respond to requests and results from heterogeneous remote systems. The design and operation of CLIPS agents are illustrated with two distributed applications that integrate CLIPS-based expert systems with other intelligent systems for isolating and mapping problems in the Space Shuttle Launch Processing System at the NASA Kennedy Space Center.

4G and Beyond - Exploiting Heterogeneity in Mobile Networks

DEFF Research Database (Denmark)

Zakrzewska, Anna

of mobile networks and focuses on the main challenges resulting from their heterogeneity. To effectively manage this diversity, a novel hierarchical approach considering all types of resources, spectral, optical and computational is proposed. The generic framework covers all kinds of network heterogeneity...... and can be beneficial at any stage of network deployment or operation. Furthermore, a dedicated study of RAT selection and resource allocation is performed and a new optimisation model is introduced and evaluated. The results show a significant reduction of number of handovers at the cost of a slight...... throughput degradation, which leads to a more stable connectivity in multi-RAT environments. A new HetNet architecture with control and user plane separation is thoroughly evaluated. The study focuses on the soft-pilot requirements and assignment problem, which is solved using a proposed optimisation model...

Compensatory Analysis and Optimization for MADM for Heterogeneous Wireless Network Selection

Directory of Open Access Journals (Sweden)

Jian Zhou

2016-01-01

Full Text Available In the next-generation heterogeneous wireless networks, a mobile terminal with a multi-interface may have network access from different service providers using various technologies. In spite of this heterogeneity, seamless intersystem mobility is a mandatory requirement. One of the major challenges for seamless mobility is the creation of a network selection scheme, which is for users that select an optimal network with best comprehensive performance between different types of networks. However, the optimal network may be not the most reasonable one due to compensation of MADM (Multiple Attribute Decision Making, and the network is called pseudo-optimal network. This paper conducts a performance evaluation of a number of widely used MADM-based methods for network selection that aim to keep the mobile users always best connected anywhere and anytime, where subjective weight and objective weight are all considered. The performance analysis shows that the selection scheme based on MEW (weighted multiplicative method and combination weight can better avoid accessing pseudo-optimal network for balancing network load and reducing ping-pong effect in comparison with three other MADM solutions.

On Connected Target k-Coverage in Heterogeneous Wireless Sensor Networks.

Science.gov (United States)

Yu, Jiguo; Chen, Ying; Ma, Liran; Huang, Baogui; Cheng, Xiuzhen

2016-01-15

Coverage and connectivity are two important performance evaluation indices for wireless sensor networks (WSNs). In this paper, we focus on the connected target k-coverage (CTC k) problem in heterogeneous wireless sensor networks (HWSNs). A centralized connected target k-coverage algorithm (CCTC k) and a distributed connected target k-coverage algorithm (DCTC k) are proposed so as to generate connected cover sets for energy-efficient connectivity and coverage maintenance. To be specific, our proposed algorithms aim at achieving minimum connected target k-coverage, where each target in the monitored region is covered by at least k active sensor nodes. In addition, these two algorithms strive to minimize the total number of active sensor nodes and guarantee that each sensor node is connected to a sink, such that the sensed data can be forwarded to the sink. Our theoretical analysis and simulation results show that our proposed algorithms outperform a state-of-art connected k-coverage protocol for HWSNs.

Systemic risk and heterogeneous leverage in banking networks

Science.gov (United States)

Kuzubaş, Tolga Umut; Saltoğlu, Burak; Sever, Can

2016-11-01

This study probes systemic risk implications of leverage heterogeneity in banking networks. We show that the presence of heterogeneous leverages drastically changes the systemic effects of defaults and the nature of the contagion in interbank markets. Using financial leverage data from the US banking system, through simulations, we analyze the systemic significance of different types of borrowers, the evolution of the network, the consequences of interbank market size and the impact of market segmentation. Our study is related to the recent Basel III regulations on systemic risk and the treatment of the Global Systemically Important Banks (GSIBs). We also assess the extent to which the recent capital surcharges on GSIBs may curb financial fragility. We show the effectiveness of surcharge policy for the most-levered banks vis-a-vis uniform capital injection.

Multi-Valued Associative Memory Neural Network

Institute of Scientific and Technical Information of China (English)

修春波; 刘向东; 张宇河

2003-01-01

A novel learning method for multi-valued associative memory network is introduced, which is based on Hebb rule, but utilizes more information. According to the current probe vector, the connection weights matrix could be chosen dynamically. Double-valued and multi-valued associative memory are all realized in our simulation experiment. The experimental results show that the method could enhance the associative success rate.

Realizations of highly heterogeneous collagen networks via stochastic reconstruction for micromechanical analysis of tumor cell invasion

Science.gov (United States)

Nan, Hanqing; Liang, Long; Chen, Guo; Liu, Liyu; Liu, Ruchuan; Jiao, Yang

2018-03-01

Three-dimensional (3D) collective cell migration in a collagen-based extracellular matrix (ECM) is among one of the most significant topics in developmental biology, cancer progression, tissue regeneration, and immune response. Recent studies have suggested that collagen-fiber mediated force transmission in cellularized ECM plays an important role in stress homeostasis and regulation of collective cellular behaviors. Motivated by the recent in vitro observation that oriented collagen can significantly enhance the penetration of migrating breast cancer cells into dense Matrigel which mimics the intravasation process in vivo [Han et al. Proc. Natl. Acad. Sci. USA 113, 11208 (2016), 10.1073/pnas.1610347113], we devise a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization. Specifically, a collagen network is represented via the graph (node-bond) model and the microstructural statistics considered include the cross-link (node) density, valence distribution, fiber (bond) length distribution, as well as fiber orientation distribution. An optimization problem is formulated in which the objective function is defined as the squared difference between a set of target microstructural statistics and the corresponding statistics for the simulated network. Simulated annealing is employed to solve the optimization problem by evolving an initial network via random perturbations to generate realizations of homogeneous networks with randomly oriented fibers, homogeneous networks with aligned fibers, heterogeneous networks with a continuous variation of fiber orientation along a prescribed direction, as well as a binary system containing a collagen region with aligned fibers and a dense Matrigel region with randomly oriented fibers. The generation and propagation of active forces in the simulated networks due to polarized contraction of an embedded ellipsoidal cell and a small group

5G heterogeneous networks self-organizing and optimization

CERN Document Server

Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing

2016-01-01

This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.

A simple network agreement-based approach for combining evidences in a heterogeneous sensor network

Directory of Open Access Journals (Sweden)

Raúl Eusebio-Grande

2015-12-01

Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.

The working memory networks of the human brain.

Science.gov (United States)

Linden, David E J

2007-06-01

Working memory and short-term memory are closely related in their cognitive architecture, capacity limitations, and functional neuroanatomy, which only partly overlap with those of long-term memory. The author reviews the functional neuroimaging literature on the commonalities and differences between working memory and short-term memory and the interplay of areas with modality-specific and supramodal representations in the brain networks supporting these fundamental cognitive processes. Sensory stores in the visual, auditory, and somatosensory cortex play a role in short-term memory, but supramodal parietal and frontal areas are often recruited as well. Classical working memory operations such as manipulation, protection against interference, or updating almost certainly require at least some degree of prefrontal support, but many pure maintenance tasks involve these areas as well. Although it seems that activity shifts from more posterior regions during encoding to more anterior regions during delay, some studies reported sustained delay activity in sensory areas as well. This spatiotemporal complexity of the short-term memory/working memory networks is mirrored in the activation patterns that may explain capacity constraints, which, although most prominent in the parietal cortex, seem to be pervasive across sensory and premotor areas. Finally, the author highlights open questions for cognitive neuroscience research of working memory, such as that of the mechanisms for integrating different types of content (binding) or those providing the link to long-term memory.

Efficient implementation of a multidimensional fast fourier transform on a distributed-memory parallel multi-node computer

Science.gov (United States)

Bhanot, Gyan V [Princeton, NJ; Chen, Dong [Croton-On-Hudson, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2008-01-01

The present in invention is directed to a method, system and program storage device for efficiently implementing a multidimensional Fast Fourier Transform (FFT) of a multidimensional array comprising a plurality of elements initially distributed in a multi-node computer system comprising a plurality of nodes in communication over a network, comprising: distributing the plurality of elements of the array in a first dimension across the plurality of nodes of the computer system over the network to facilitate a first one-dimensional FFT; performing the first one-dimensional FFT on the elements of the array distributed at each node in the first dimension; re-distributing the one-dimensional FFT-transformed elements at each node in a second dimension via "all-to-all" distribution in random order across other nodes of the computer system over the network; and performing a second one-dimensional FFT on elements of the array re-distributed at each node in the second dimension, wherein the random order facilitates efficient utilization of the network thereby efficiently implementing the multidimensional FFT. The "all-to-all" re-distribution of array elements is further efficiently implemented in applications other than the multidimensional FFT on the distributed-memory parallel supercomputer.

Interconnecting heterogeneous database management systems

Science.gov (United States)

Gligor, V. D.; Luckenbaugh, G. L.

1984-01-01

It is pointed out that there is still a great need for the development of improved communication between remote, heterogeneous database management systems (DBMS). Problems regarding the effective communication between distributed DBMSs are primarily related to significant differences between local data managers, local data models and representations, and local transaction managers. A system of interconnected DBMSs which exhibit such differences is called a network of distributed, heterogeneous DBMSs. In order to achieve effective interconnection of remote, heterogeneous DBMSs, the users must have uniform, integrated access to the different DBMs. The present investigation is mainly concerned with an analysis of the existing approaches to interconnecting heterogeneous DBMSs, taking into account four experimental DBMS projects.

Research on distributed heterogeneous data PCA algorithm based on cloud platform

Science.gov (United States)

Zhang, Jin; Huang, Gang

2018-05-01

Principal component analysis (PCA) of heterogeneous data sets can solve the problem that centralized data scalability is limited. In order to reduce the generation of intermediate data and error components of distributed heterogeneous data sets, a principal component analysis algorithm based on heterogeneous data sets under cloud platform is proposed. The algorithm performs eigenvalue processing by using Householder tridiagonalization and QR factorization to calculate the error component of the heterogeneous database associated with the public key to obtain the intermediate data set and the lost information. Experiments on distributed DBM heterogeneous datasets show that the model method has the feasibility and reliability in terms of execution time and accuracy.

Plastic modulation of episodic memory networks in the aging brain with cognitive decline.

Science.gov (United States)

Bai, Feng; Yuan, Yonggui; Yu, Hui; Zhang, Zhijun

2016-07-15

Social-cognitive processing has been posited to underlie general functions such as episodic memory. Episodic memory impairment is a recognized hallmark of amnestic mild cognitive impairment (aMCI) who is at a high risk for dementia. Three canonical networks, self-referential processing, executive control processing and salience processing, have distinct roles in episodic memory retrieval processing. It remains unclear whether and how these sub-networks of the episodic memory retrieval system would be affected in aMCI. This task-state fMRI study constructed systems-level episodic memory retrieval sub-networks in 28 aMCI and 23 controls using two computational approaches: a multiple region-of-interest based approach and a voxel-level functional connectivity-based approach, respectively. These approaches produced the remarkably similar findings that the self-referential processing network made critical contributions to episodic memory retrieval in aMCI. More conspicuous alterations in self-referential processing of the episodic memory retrieval network were identified in aMCI. In order to complete a given episodic memory retrieval task, increases in cooperation between the self-referential processing network and other sub-networks were mobilized in aMCI. Self-referential processing mediate the cooperation of the episodic memory retrieval sub-networks as it may help to achieve neural plasticity and may contribute to the prevention and treatment of dementia. Copyright © 2016 Elsevier B.V. All rights reserved.

Physical-layer Network Coding in Two-Way Heterogeneous Cellular Networks with Power Imbalance

OpenAIRE

Thampi, Ajay K; Liew, Soung Chang; Armour, Simon M D; Fan, Zhong; You, Lizhao; Kaleshi, Dritan

2016-01-01

The growing demand for high-speed data, quality of service (QoS) assurance and energy efficiency has triggered the evolution of 4G LTE-A networks to 5G and beyond. Interference is still a major performance bottleneck. This paper studies the application of physical-layer network coding (PNC), a technique that exploits interference, in heterogeneous cellular networks. In particular, we propose a rate-maximising relay selection algorithm for a single cell with multiple relays assuming the decode...

A Gamma Memory Neural Network for System Identification

Science.gov (United States)

Motter, Mark A.; Principe, Jose C.

1992-01-01

A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

Internal fracture heterogeneity in discrete fracture network modelling: Effect of correlation length and textures with connected and disconnected permeability field

Science.gov (United States)

Frampton, A.; Hyman, J.; Zou, L.

2017-12-01

Analysing flow and transport in sparsely fractured media is important for understanding how crystalline bedrock environments function as barriers to transport of contaminants, with important applications towards subsurface repositories for storage of spent nuclear fuel. Crystalline bedrocks are particularly favourable due to their geological stability, low advective flow and strong hydrogeochemical retention properties, which can delay transport of radionuclides, allowing decay to limit release to the biosphere. There are however many challenges involved in quantifying and modelling subsurface flow and transport in fractured media, largely due to geological complexity and heterogeneity, where the interplay between advective and dispersive flow strongly impacts both inert and reactive transport. A key to modelling transport in a Lagrangian framework involves quantifying pathway travel times and the hydrodynamic control of retention, and both these quantities strongly depend on heterogeneity of the fracture network at different scales. In this contribution, we present recent analysis of flow and transport considering fracture networks with single-fracture heterogeneity described by different multivariate normal distributions. A coherent triad of fields with identical correlation length and variance are created but which greatly differ in structure, corresponding to textures with well-connected low, medium and high permeability structures. Through numerical modelling of multiple scales in a stochastic setting we quantify the relative impact of texture type and correlation length against network topological measures, and identify key thresholds for cases where flow dispersion is controlled by single-fracture heterogeneity versus network-scale heterogeneity. This is achieved by using a recently developed novel numerical discrete fracture network model. Furthermore, we highlight enhanced flow channelling for cases where correlation structure continues across

Statistics of the uplink co-tier interference in closed access heterogeneous networks

KAUST Repository

Tabassum, Hina

2013-09-01

In this paper, we derive a statistical model of the co-tier interference in closed access two tier heterogeneous wireless cellular networks with femtocell deployments. The derived model captures the impact of bounded path loss model, wall penetration loss, user distributions, random locations, and density of the femtocells. Firstly, we derive the analytical expressions for the probability density function (PDF) and moment generating function (MGF) of the co-tier interference considering a single femtocell interferer by exploiting the random disc line picking theory from geometric probability. We then derive the MGF of the cumulative interference from all femtocell interferers considering full spectral reuse in each femtocell. Orthogonal spectrum partitioning is assumed between the macrocell and femtocell networks to avoid any cross-tier interference. Finally, the accuracy of the derived expressions is validated through Monte-Carlo simulations and the expressions are shown to be useful in quantifying important network performance metrics such as ergodic capacity. © 2013 IEEE.

Memory Compression Techniques for Network Address Management in MPI

Energy Technology Data Exchange (ETDEWEB)

Guo, Yanfei; Archer, Charles J.; Blocksome, Michael; Parker, Scott; Bland, Wesley; Raffenetti, Ken; Balaji, Pavan

2017-05-29

MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance critical than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.

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

Directory of Open Access Journals (Sweden)

Hongli Liu

2009-01-01

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

Reliable Uplink Communication through Double Association in Wireless Heterogeneous Networks

DEFF Research Database (Denmark)

Kim, Dong Min; Popovski, Petar

2016-01-01

We investigate methods for network association that improve the reliability of uplink transmissions in dense wireless heterogeneous networks. The stochastic geometry analysis shows that the double association, in which an uplink transmission is transmitted to a macro Base Station (BS) and small BS...

Evaluation of a Connectionless NoC for a Real-Time Distributed Shared Memory Many-Core System

NARCIS (Netherlands)

Rutgers, J.H.; Bekooij, Marco Jan Gerrit; Smit, Gerardus Johannes Maria

2012-01-01

Real-time embedded systems like smartphones tend to comprise an ever increasing number of processing cores. For scalability and the need for guaranteed performance, the use of a connection-oriented network-on-chip (NoC) is advocated. Furthermore, a distributed shared memory architecture is preferred

Social Transmission of False Memory in Small Groups and Large Networks.

Science.gov (United States)

Maswood, Raeya; Rajaram, Suparna

2018-05-21

Sharing information and memories is a key feature of social interactions, making social contexts important for developing and transmitting accurate memories and also false memories. False memory transmission can have wide-ranging effects, including shaping personal memories of individuals as well as collective memories of a network of people. This paper reviews a collection of key findings and explanations in cognitive research on the transmission of false memories in small groups. It also reviews the emerging experimental work on larger networks and collective false memories. Given the reconstructive nature of memory, the abundance of misinformation in everyday life, and the variety of social structures in which people interact, an understanding of transmission of false memories has both scientific and societal implications. © 2018 Cognitive Science Society, Inc.

Chimera-like states in structured heterogeneous networks

Science.gov (United States)

Li, Bo; Saad, David

2017-04-01

Chimera-like states are manifested through the coexistence of synchronous and asynchronous dynamics and have been observed in various systems. To analyze the role of network topology in giving rise to chimera-like states, we study a heterogeneous network model comprising two groups of nodes, of high and low degrees of connectivity. The architecture facilitates the analysis of the system, which separates into a densely connected coherent group of nodes, perturbed by their sparsely connected drifting neighbors. It describes a synchronous behavior of the densely connected group and scaling properties of the induced perturbations.

Preventing Out-of-Sequence for Multicast Input-Queued Space-Memory-Memory Clos-Network

DEFF Research Database (Denmark)

Yu, Hao; Ruepp, Sarah Renée; Berger, Michael Stübert

2011-01-01

This paper proposes an out-of-sequence (OOS) preventative cell dispatching algorithm, the multicast flow-based round robin (MFRR), for multicast input-queued space-memory-memory (IQ-SMM) Clos-network architecture. Independently treating each incoming cell, such as the desynchronized static round...

Management of speech and video telephony quality in heterogeneous wireless networks

CERN Document Server

Lewcio, Błażej

2014-01-01

This book shows how networking research and quality engineering can be combined to successfully manage the transmission quality when speech and video telephony is delivered in heterogeneous wireless networks. Nomadic use of services requires intelligent management of ongoing transmission, and to make the best of available resources many fundamental trade-offs must be considered. Network coverage versus throughput and reliability of a connection is one key aspect, efficiency versus robustness of signal compression is another. However, to successfully manage services, user-perceived Quality of Experience (QoE) in heterogeneous networks must be known, and the perception of quality changes must be understood.  These issues are addressed in this book, in particular focusing on the perception of quality changes due to switching between diverse networks, speech and video codecs, and encoding bit rates during active calls.

Capacity of Heterogeneous Mobile Wireless Networks with D-Delay Transmission Strategy.

Science.gov (United States)

Wu, Feng; Zhu, Jiang; Xi, Zhipeng; Gao, Kai

2016-03-25

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks.

Measuring dynamic process of working memory training with functional brain networks

Directory of Open Access Journals (Sweden)

Hong Wang

2015-12-01

Full Text Available In this paper, we proposed the functional brain networks and graphic theory method to measure the effect of working memory training on the neural activities. 12 subjects were recruited in this study, and they did the same working memory task before they had been trained and after training. We architected functional brain networks based on EEG coherence and calculated properties of brain networks to measure the neural co-activities and the working memory level of subjects. As the result, the internal connections in frontal region decreased after working memory training, but the connection between frontal region and top region increased. And the more small-world feature was observed after training. The features observed above were in alpha (8-13 Hz and beta (13-30 Hz bands. The functional brain networks based on EEG coherence proposed in this paper can be used as the indicator of working memory level.

The effects of heterogeneities on memory-dependent diffusion

Science.gov (United States)

Adib, Farhad; Neogi, P.

1993-07-01

Case II diffusion is often seen in glassy polymers, where the mass uptake in sorption is proportional to time t instead of sqrt{t}. A memory dependent diffusion is needed to explain such effects, where the relaxation function used to describe the memory effect has a characteristic time. The ratio of this time to the overall diffusion times is the diffusional Deborah number. Simple models show that case II results when the Deborah number is around one, that is, when the two time scales are comparable. Under investigation are the possible effects of the fact that the glassy polymers are heterogeneous over molecular scales. The averaging form given by DiMarzio and Sanchez has been used to obtain the averaged response. The calculated dynamics of sorption show that whereas case II is still observed, the long term tails change dramatically from the oscillatory to torpid, to chaotic, which are all observed in the experiments. The Deborah number defined here in a self-consistent manner collapses in those cases, but causes no other ill-effects.

Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models.

Science.gov (United States)

Krivitsky, Pavel N; Handcock, Mark S; Raftery, Adrian E; Hoff, Peter D

2009-07-01

Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets. We also apply it to two simulated network datasets with the same, highly skewed, degree distribution, but very different network behavior: one unstructured and the other with transitivity and clustering. Models based on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but our model does.

Distribution of orientation selectivity in recurrent networks of spiking neurons with different random topologies.

Science.gov (United States)

Sadeh, Sadra; Rotter, Stefan

2014-01-01

Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity.

An Outline of Data Aggregation Security in Heterogeneous Wireless Sensor Networks.

Science.gov (United States)

Boubiche, Sabrina; Boubiche, Djallel Eddine; Bilami, Azzedine; Toral-Cruz, Homero

2016-04-12

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes' resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.

An Outline of Data Aggregation Security in Heterogeneous Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sabrina Boubiche

2016-04-01

Full Text Available Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.

An Outline of Data Aggregation Security in Heterogeneous Wireless Sensor Networks

Science.gov (United States)

Boubiche, Sabrina; Boubiche, Djallel Eddine; Bilami, Azzedine; Toral-Cruz, Homero

2016-01-01

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach. PMID:27077866

Monte Carlo photon transport on shared memory and distributed memory parallel processors

International Nuclear Information System (INIS)

Martin, W.R.; Wan, T.C.; Abdel-Rahman, T.S.; Mudge, T.N.; Miura, K.

1987-01-01

Parallelized Monte Carlo algorithms for analyzing photon transport in an inertially confined fusion (ICF) plasma are considered. Algorithms were developed for shared memory (vector and scalar) and distributed memory (scalar) parallel processors. The shared memory algorithm was implemented on the IBM 3090/400, and timing results are presented for dedicated runs with two, three, and four processors. Two alternative distributed memory algorithms (replication and dispatching) were implemented on a hypercube parallel processor (1 through 64 nodes). The replication algorithm yields essentially full efficiency for all cube sizes; with the 64-node configuration, the absolute performance is nearly the same as with the CRAY X-MP. The dispatching algorithm also yields efficiencies above 80% in a large simulation for the 64-processor configuration

Analysis and Visualization of Internet QA Bulletin Boards Represented as Heterogeneous Networks

Science.gov (United States)

Murata, Tsuyoshi; Ikeya, Tomoyuki

Visualizing and analyzing social interactions of CGM (Consumer Generated Media) are important for understanding overall activities on the internet. Social interactions are often represented as simple networks that are composed of homogeneous nodes and edges between them. However, related entities in real world are often not homogeneous. Such relations are naturally represented as heterogeneous networks composed of more than one kind of nodes and edges connecting them. In the case of CGM, for example, users and their contents constitute nodes of heterogeneous networks. There are related users (user communities) and related contents (contents communities) in the heterogeneous networks. Discovering both communities and finding correspondence among them will clarify the characteristics of the communites. This paper describes an attempt for visualizing and analyzing social interactions of Yahoo! Chiebukuro (Japanese Yahoo! Answers). New criteria for measuring correspondence between user communities and board communites are defined, and characteristics of both communities are analyzed using the criteria.

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.

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models. [Sparse, Distributed Memory

Science.gov (United States)

Keeler, James D.

1988-01-01

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays.

Science.gov (United States)

Arik, Sabri

2005-05-01

This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all continuous nonmonotonic neuron activation functions. It is shown that in some special cases of the results, the stability criteria can be easily checked. Some examples are also given to compare the results with the previous results derived in the literature.

Low-Feedback Opportunistic Scheduling Schemes for Wireless Networks with Heterogenous Users

KAUST Repository

Rashid, Faraan

2012-07-01

Efficient implementation of resource sharing strategies in a multi-user wireless environment can improve the performance of a network significantly. In this thesis we study various scheduling strategies for wireless networks and handle the problem of opportunistically scheduling transmissions using channel aware schemes. First we propose a scheme that can handle users with asymmetric channel conditions and is opportunistic in the sense that it exploits the multi-user diversity of the network. The scheme requires the users to have a priori knowledge of their channel distributions. The associated overhead is limited meaning it offers reduced feedback load, that does not scale with the increasing number of users. The main technique used to shrink the feedback load is the contention based distributed implementation of a splitting algorithm that does not require explicit feedback to the scheduler from every user. The users find the best among themselves, in a distributed manner, while requiring just a ternary broadcast feedback from the scheduler at the end of each mini-slot. In addition, it can also handle fairness constraints in time and throughput to various degrees. Next we propose another opportunistic scheduler that offers most of the benefits of the previously proposed scheme but is more practical because it can also handle heterogenous users whose channel distributions are unknown. This new scheme actually reduces the complexity and is also more robust for changing traffic patterns. Finally we extend both these schemes to the scenario where there are fixed thresholds, this enables us to handle opportunistic scheduling in practical systems that can only transmit over finite number of discrete rates with the additional benefit that full feedback session, even from the selected user, is never required.

Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Directory of Open Access Journals (Sweden)

Yong Wang

2018-01-01

Full Text Available In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN. The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA and the Particle Swarm Optimization (PSO algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units’ reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.

Working memory training promotes general cognitive abilities in genetically heterogeneous mice.

Science.gov (United States)

Light, Kenneth R; Kolata, Stefan; Wass, Christopher; Denman-Brice, Alexander; Zagalsky, Ryan; Matzel, Louis D

2010-04-27

In both humans and mice, the efficacy of working memory capacity and its related process, selective attention, are each strongly predictive of individuals' aggregate performance in cognitive test batteries [1-9]. Because working memory is taxed during most cognitive tasks, the efficacy of working memory may have a causal influence on individuals' performance on tests of "intelligence" [10, 11]. Despite the attention this has received, supporting evidence has been largely correlational in nature (but see [12]). Here, genetically heterogeneous mice were assessed on a battery of five learning tasks. Animals' aggregate performance across the tasks was used to estimate their general cognitive abilities, a trait that is in some respects analogous to intelligence [13, 14]. Working memory training promoted an increase in animals' selective attention and their aggregate performance on these tasks. This enhancement of general cognitive performance by working memory training was attenuated if its selective attention demands were reduced. These results provide evidence that the efficacy of working memory capacity and selective attention may be causally related to an animal's general cognitive performance and provide a framework for behavioral strategies to promote those abilities. Furthermore, the pattern of behavior reported here reflects a conservation of the processes that regulate general cognitive performance in humans and infrahuman animals. Copyright © 2010 Elsevier Ltd. All rights reserved.

Social networks: Evolving graphs with memory dependent edges

Science.gov (United States)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Reduced negativity effect in older adults' memory for emotional pictures: the heterogeneity-homogeneity list paradigm.

Science.gov (United States)

Grühn, Daniel; Scheibe, Susanne; Baltes, Paul B

2007-09-01

Using the heterogeneity-homogeneity list paradigm, the authors investigated 48 young adults' (20-30 years) and 48 older adults' (65-75 years) recognition memory for emotional pictures. The authors obtained no evidence for a positivity bias in older adults' memory: Age differences were primarily driven by older adults' diminished ability to remember negative pictures. The authors further found a strong effect of list types: Pictures, particularly neutral ones, were better recognized in homogeneous (blocked) lists than in heterogeneous (mixed) ones. Results confirm those of a previous study by D. Grühn, J. Smith, and P. B. Baltes (2005) that used a different type of to-be-remembered material, that is, pictures instead of words. (PsycINFO Database Record (c) 2007 APA, all rights reserved).

Memory functions reveal structural properties of gene regulatory networks

Science.gov (United States)

Perez-Carrasco, Ruben

2018-01-01

Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

Science.gov (United States)

2017-01-01

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity—a measure of network segregation—is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization.

Science.gov (United States)

Westphal, Andrew J; Wang, Siliang; Rissman, Jesse

2017-03-29

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control

Seamless interworking architecture for WBAN in heterogeneous wireless networks with QoS guarantees.

Science.gov (United States)

Khan, Pervez; Ullah, Niamat; Ullah, Sana; Kwak, Kyung Sup

2011-10-01

The IEEE 802.15.6 standard is a communication standard optimized for low-power and short-range in-body/on-body nodes to serve a variety of medical, consumer electronics and entertainment applications. Providing high mobility with guaranteed Quality of Service (QoS) to a WBAN user in heterogeneous wireless networks is a challenging task. A WBAN uses a Personal Digital Assistant (PDA) to gather data from body sensors and forwards it to a remote server through wide range wireless networks. In this paper, we present a coexistence study of WBAN with Wireless Local Area Networks (WLAN) and Wireless Wide Area Networks (WWANs). The main issue is interworking of WBAN in heterogenous wireless networks including seamless handover, QoS, emergency services, cooperation and security. We propose a Seamless Interworking Architecture (SIA) for WBAN in heterogenous wireless networks based on a cost function. The cost function is based on power consumption and data throughput costs. Our simulation results show that the proposed scheme outperforms typical approaches in terms of throughput, delay and packet loss rate.

Generalized memory associativity in a network model for the neuroses

Science.gov (United States)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2009-03-01

We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

Science.gov (United States)

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

Hybrid computing using a neural network with dynamic external memory.

Science.gov (United States)

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Meta-path based heterogeneous combat network link prediction

Science.gov (United States)

Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

2017-09-01

The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis

NARCIS (Netherlands)

Dam, van J.C.J.; Schaap, P.J.; Martins dos Santos, V.A.P.; Suarez Diez, M.

2014-01-01

Background: Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each

Provably Secure Heterogeneous Access Control Scheme for Wireless Body Area Network.

Science.gov (United States)

Omala, Anyembe Andrew; Mbandu, Angolo Shem; Mutiria, Kamenyi Domenic; Jin, Chunhua; Li, Fagen

2018-04-28

Wireless body area network (WBAN) provides a medium through which physiological information could be harvested and transmitted to application provider (AP) in real time. Integrating WBAN in a heterogeneous Internet of Things (IoT) ecosystem would enable an AP to monitor patients from anywhere and at anytime. However, the IoT roadmap of interconnected 'Things' is still faced with many challenges. One of the challenges in healthcare is security and privacy of streamed medical data from heterogeneously networked devices. In this paper, we first propose a heterogeneous signcryption scheme where a sender is in a certificateless cryptographic (CLC) environment while a receiver is in identity-based cryptographic (IBC) environment. We then use this scheme to design a heterogeneous access control protocol. Formal security proof for indistinguishability against adaptive chosen ciphertext attack and unforgeability against adaptive chosen message attack in random oracle model is presented. In comparison with some of the existing access control schemes, our scheme has lower computation and communication cost.

Changes in brain network efficiency and working memory performance in aging.

Science.gov (United States)

Stanley, Matthew L; Simpson, Sean L; Dagenbach, Dale; Lyday, Robert G; Burdette, Jonathan H; Laurienti, Paul J

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

Science.gov (United States)

Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

Distributed learning enhances relational memory consolidation.

Science.gov (United States)

Litman, Leib; Davachi, Lila

2008-09-01

It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of forgetting relative to ML. Furthermore, we demonstrate that this savings in forgetting is specific to relational, but not item, memory. In the context of extant theories and knowledge of memory consolidation, these results suggest that an important mechanism underlying the mnemonic benefit of DL is enhanced memory consolidation. We speculate that synaptic strengthening mechanisms supporting long-term memory consolidation may be differentially mediated by the spacing of memory reactivation. These findings have broad implications for the scientific study of episodic memory consolidation and, more generally, for educational curriculum development and policy.

Functional heterogeneity within Broca's area during verbal working memory.

Science.gov (United States)

Chein, J M; Fissell, K; Jacobs, S; Fiez, J A

2002-12-01

In previous fMRI research, we found that two subregions of the left inferior frontal cortex showed distinct patterns of activity during a verbal working memory task. Specifically, a more dorsal region tracked with performance, while a more ventral region was sensitive to lexical status. To test the veracity of this finding, we developed a new method for meta-analysis of neuroimaging results. In this method, Gaussian probability distributions are formed around stereotaxic coordinates obtained from published neuroimaging studies. These probability distributions are then combined to identify regions of convergence across studies. When this method was applied to prior studies of working memory, the results largely paralleled those from earlier reviews of the literature on working memory, but also confirmed our empirical findings showing distinct foci within Broca's area. Further application of this meta-analytic technique substantiated the dissociation in these regions for performance and sublexical processing. These results help to validate a novel approach for meta-analysis of neuroimaging findings that avoids many of the subjective assumptions involved in alternative approaches.

Walking Across Wikipedia: A Scale-Free Network Model of Semantic Memory Retrieval

Directory of Open Access Journals (Sweden)

Graham William Thompson

2014-02-01

Full Text Available Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like animals are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.

Aplikasi Bidirectional Assosiatif Memori (BAM) Network pada Pengenalan Model

OpenAIRE

Iskandar, Iskhaq

2001-01-01

Penelitian ini bertujuan untuk menyusun suatu simulasi komputer yang dapat dipergunakan untuk menguji kemampuan memori komputer dalam mengenali suatu model tertentu berdasarkan algoritma Bidirectional Assosiatif Memori Neural Network. Model yang digunakan dalam penelitian dalam penelitian ini adalah huruf-huruf abjad yang dinyatakan dalam kode polar –1 dan +1 dalam bentuk matrik [5x3]. Hasil yang didapat dalam penelitian ini menunjukkan bahwa rancangan network yang disusun mampu mengenali mod...

Voltage regulation in distribution networks with distributed generation

Science.gov (United States)

Blažič, B.; Uljanić, B.; Papič, I.

2012-11-01

The paper deals with the topic of voltage regulation in distribution networks with relatively high distributed energy resources (DER) penetration. The problem of voltage rise is described and different options for voltage regulation are given. The influence of DER on voltage profile and the effectiveness of the investigated solutions are evaluated by means of simulation in DIgSILENT. The simulated network is an actual distribution network in Slovenia with a relatively high penetration of distributed generation. Recommendations for voltage control in networks with DER penetration are given at the end.

MHBCDA: Mobility and Heterogeneity aware Bandwidth Efficient Cluster based Data Aggregation for Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2013-01-01

Wireless Sensor Network (WSN) offers a variety of novel applications for mobile targets. It generates the large amount of redundant sensing data. The data aggregation techniques are extensively used to reduce the energy consumption and increase the network lifetime, although it has the side effect...... efficient. It exploits correlation of data packets generated by varying the packet generation rate. It prevents transmission of redundant data packets by improving energy consumption by 4.11% and prolongs the network life by 34.45% as compared with state-of-the-art solutions.......-based Data Aggregation (MHBCDA) algorithm for the randomly distributed nodes. It considers the mobile sink based packet aggregation for the heterogeneous WSN. It uses predefined region for the aggregation at cluster head to minimize computation and communication cost. The MHBCDA is energy and bandwidth...

MPTCP Tunnel: An Architecture for Aggregating Bandwidth of Heterogeneous Access Networks

Directory of Open Access Journals (Sweden)

Xiaolan Liu

2018-01-01

Full Text Available Fixed and cellular networks are two typical access networks provided by operators. Fixed access network is widely employed; nevertheless, its bandwidth is sometimes not sufficient enough to meet user bandwidth requirements. Meanwhile, cellular access network owns unique advantages of wider coverage, faster increasing link speed, more flexible deployment, and so forth. Therefore, it is attractive for operators to mitigate the bandwidth shortage by bundling these two. Actually, there have been existing schemes proposed to aggregate the bandwidth of two access networks, whereas they all have their own problems, like packet reordering or extra latency overhead. To address this problem, we design new architecture, MPTCP Tunnel, to aggregate the bandwidth of multiple heterogeneous access networks from the perspective of operators. MPTCP Tunnel uses MPTCP, which solves the reordering problem essentially, to bundle multiple access networks. Besides, MPTCP Tunnel sets up only one MPTCP connection at play which adapts itself to multiple traffic types and TCP flows. Furthermore, MPTCP Tunnel forwards intact IP packets through access networks, maintaining the end-to-end TCP semantics. Experimental results manifest that MPTCP Tunnel can efficiently aggregate the bandwidth of multiple access networks and is more adaptable to the increasing heterogeneity of access networks than existing mechanisms.

Mobility management in LTE heterogeneous networks

CERN Document Server

Karandikar, Abhay; Mehta, Mahima

2017-01-01

This book is the first of its kind, compiling information on the Long-Term Evolution (LTE) standards, which are enhanced to address new mobility-related challenges in Heterogeneous Networks (HetNets). It identifies the related challenges and discusses solutions and the simulation methodology for modeling HetNet mobility – cutting-edge information that was previously accessible only in the form of 3GPP specifications and documents, and research papers. The book reviews the current LTE mobility framework and discusses some of the changes for enhancing mobility management in HetNets. It describes the measurement procedures, handover (HO) mechanisms and HO success/failure scenarios. HetNets are intended to provide very high spectral efficiency while ensuring seamless coverage by deploying low-power nodes within the umbrella macrocell network. While mobility management in homogeneous networks is well understood, LTE standards are being enhanced to address the HetNet-specific mobility management challenges emergi...

Percolation in Heterogeneous Media

International Nuclear Information System (INIS)

Vocka, Radim

1999-01-01

This work is a theoretical reflection on the problematic of the modeling of heterogeneous media, that is on the way of their simple representation conserving their characteristic features. Two particular problems are addressed in this thesis. Firstly, we study the transport in porous media, that is in a heterogeneous media which structure is quenched. A pore space is represented in a simple way - a pore is symbolized as a tube of a given length and a given diameter. The fact that the correlations in the distribution of pore sizes are taken into account by a construction of a hierarchical network makes possible the modeling of porous media with a porosity distributed over several length scales. The transport in the hierarchical network shows qualitatively different phenomena from those observed in simpler models. A comparison of numerical results with experimental data shows that the hierarchical network gives a good qualitative representation of the structure of real porous media. Secondly, we study a problem of the transport in a heterogeneous media which structure is evolving during the time. The models where the evolution of the structure is not influenced by the transport are studied in detail. These models present a phase transition of the same nature as that observed on the percolation networks. We propose a new theoretical description of this transition, and we express critical exponents describing the evolution of the conductivity as a function of fundamental exponents of percolation theory. (author) [fr

Memory and learning in a class of neural network models

International Nuclear Information System (INIS)

Wallace, D.J.

1986-01-01

The author discusses memory and learning properties of the neural network model now identified with Hopfield's work. The model, how it attempts to abstract some key features of the nervous system, and the sense in which learning and memory are identified in the model are described. A brief report is presented on the important role of phase transitions in the model and their implications for memory capacity. The results of numerical simulations obtained using the ICL Distributed Array Processors at Edinburgh are presented. A summary is presented on how the fraction of images which are perfectly stored, depends on the number of nodes and the number of nominal images which one attempts to store using the prescription in Hopfield's paper. Results are presented on the second phase transition in the model, which corresponds to almost total loss of storage capacity as the number of nominal images is increased. Results are given on the performance of a new iterative algorithm for exact storage of up to N images in an N node model

Multilevel QoS-policy-based routing management architecture appropriate for heterogeneous network environments

Science.gov (United States)

Chatzaki, Magda; Sartzetakis, Stelios

1998-09-01

As telecom providers introduce new and more sophisticated services the necessity of a global, unified view of the network infrastructure becomes demanding. Today, heterogenous backbone networks are interconnected in order to provide global connectivity. Due to technological impairments the cost of network operation, the maintenance complexity and the overuse of resources are extremely high under the goal of supporting the diverting customer requirements. We propose a scheme for ATM QoS support in such heterogenous, multi-domain, multi-technology network environment. The objective is to optimize users' and networks' profits by giving them the opportunity to satisfy their requirements. Our approach introduces a manager able to take routing decisions supporting quality of service guarantees for the customers, while making efficient use of network resources.

A Partially Distributed Intrusion Detection System for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Eung Jun Cho

2013-11-01

Full Text Available The increasing use of wireless sensor networks, which normally comprise several very small sensor nodes, makes their security an increasingly important issue. They can be practically and efficiently secured using intrusion detection systems. Conventional security mechanisms are not usually applicable due to the sensor nodes having limitations of computational power, memory capacity, and battery power. Therefore, specific security systems should be designed to function under constraints of energy or memory. A partially distributed intrusion detection system with low memory and power demands is proposed here. It employs a Bloom filter, which allows reduced signature code size. Multiple Bloom filters can be combined to reduce the signature code for each Bloom filter array. The mechanism could then cope with potential denial of service attacks, unlike many previous detection systems with Bloom filters. The mechanism was evaluated and validated through analysis and simulation.

Research and Design of Dynamic Migration Access Control Technology Based on Heterogeneous Network

Directory of Open Access Journals (Sweden)

Wang Feng

2017-01-01

Full Text Available With the continuous development of wireless networks, the amount of privacy services in heterogeneous mobile networks is increasing, such as information storage, user access, and so on. Access control security issues for heterogeneous mobile radio network, this paper proposes a dynamic migration access control technology based on heterogeneous network. Through the system architecture of the mutual trust system, we can understand the real-time mobile node failure or abnormal state. To make the service can be terminated for the node. And adopt the 802.1X authentication way to improve the security of the system. Finally, it by combining the actual running test data, the trust update algorithm of the system is optimized to reduce the actual security threats in the environment. Experiments show that the system’s anti-attack, the success rate of access, bit error rate is in line with the expected results. This system can effectively reduce the system authentication information is illegally obtained after the network security protection mechanism failure and reduce the risk of user data leakage.

Cellular- and micro-dosimetry of heterogeneously distributed tritium.

Science.gov (United States)

Chao, Tsi-Chian; Wang, Chun-Ching; Li, Junli; Li, Chunyan; Tung, Chuan-Jong

2012-01-01

The assessment of radiotoxicity for heterogeneously distributed tritium should be based on the subcellular dose and relative biological effectiveness (RBE) for cell nucleus. In the present work, geometry-dependent absorbed dose and RBE were calculated using Monte Carlo codes for tritium in the cell, cell surface, cytoplasm, or cell nucleus. Penelope (PENetration and Energy LOss of Positrins and Electrons) code was used to calculate the geometry-dependent absorbed dose, lineal energy, and electron fluence spectrum. RBE for the intestinal crypt regeneration was calculated using a lineal energy-dependent biological weighting function. RBE for the induction of DNA double strand breaks was estimated using a nucleotide-level map for clustered DNA lesions of the Monte Carlo damage simulation (MCDS) code. For a typical cell of 10 μm radius and 5 μm nuclear radius, tritium in the cell nucleus resulted in much higher RBE-weighted absorbed dose than tritium distributed uniformly. Conversely, tritium distributed on the cell surface led to trivial RBE-weighted absorbed dose due to irradiation geometry and great attenuation of beta particles in the cytoplasm. For tritium uniformly distributed in the cell, the RBE-weighted absorbed dose was larger compared to tritium uniformly distributed in the tissue. Cellular- and micro-dosimetry models were developed for the assessment of heterogeneously distributed tritium.

Sequence memory based on coherent spin-interaction neural networks.

Science.gov (United States)

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

2014-12-01

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

Cognitive Control Network Contributions to Memory-Guided Visual Attention.

Science.gov (United States)

Rosen, Maya L; Stern, Chantal E; Michalka, Samantha W; Devaney, Kathryn J; Somers, David C

2016-05-01

Visual attentional capacity is severely limited, but humans excel in familiar visual contexts, in part because long-term memories guide efficient deployment of attention. To investigate the neural substrates that support memory-guided visual attention, we performed a set of functional MRI experiments that contrast long-term, memory-guided visuospatial attention with stimulus-guided visuospatial attention in a change detection task. Whereas the dorsal attention network was activated for both forms of attention, the cognitive control network(CCN) was preferentially activated during memory-guided attention. Three posterior nodes in the CCN, posterior precuneus, posterior callosal sulcus/mid-cingulate, and lateral intraparietal sulcus exhibited the greatest specificity for memory-guided attention. These 3 regions exhibit functional connectivity at rest, and we propose that they form a subnetwork within the broader CCN. Based on the task activation patterns, we conclude that the nodes of this subnetwork are preferentially recruited for long-term memory guidance of visuospatial attention. Published by Oxford University Press 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

The circadian rhythm induced by the heterogeneous network structure of the suprachiasmatic nucleus

Science.gov (United States)

Gu, Changgui; Yang, Huijie

2016-05-01

In mammals, the master clock is located in the suprachiasmatic nucleus (SCN), which is composed of about 20 000 nonidentical neuronal oscillators expressing different intrinsic periods. These neurons are coupled through neurotransmitters to form a network consisting of two subgroups, i.e., a ventrolateral (VL) subgroup and a dorsomedial (DM) subgroup. The VL contains about 25% SCN neurons that receive photic input from the retina, and the DM comprises the remaining 75% SCN neurons which are coupled to the VL. The synapses from the VL to the DM are evidently denser than that from the DM to the VL, in which the VL dominates the DM. Therefore, the SCN is a heterogeneous network where the neurons of the VL are linked with a large number of SCN neurons. In the present study, we mimicked the SCN network based on Goodwin model considering four types of networks including an all-to-all network, a Newman-Watts (NW) small world network, an Erdös-Rényi (ER) random network, and a Barabási-Albert (BA) scale free network. We found that the circadian rhythm was induced in the BA, ER, and NW networks, while the circadian rhythm was absent in the all-to-all network with weak cellular coupling, where the amplitude of the circadian rhythm is largest in the BA network which is most heterogeneous in the network structure. Our finding provides an alternative explanation for the induction or enhancement of circadian rhythm by the heterogeneity of the network structure.

Competitive diffusion in online social networks with heterogeneous users

Science.gov (United States)

Li, Pei; He, Su; Wang, Hui; Zhang, Xin

2014-06-01

Online social networks have attracted increasing attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. However, most research on diffusion dynamics in epidemiology cannot be applied directly to characterize online social networks, where users are heterogeneous and may act differently according to their standpoints. In this paper, we propose models to characterize the competitive diffusion in online social networks with heterogeneous users. We classify messages into two types (i.e., positive and negative) and users into three types (i.e., positive, negative and neutral). We estimate the positive (negative) influence for a user generating a given type message, which is the number of times that positive (negative) messages are processed (i.e., read) incurred by this action. We then consider the diffusion threshold, above which the corresponding influence will approach infinity, and the effect threshold, above which the unexpected influence of generating a message will exceed the expected one. We verify all these results by simulations, which show the analysis results are perfectly consistent with the simulation results. These results are of importance in understanding the diffusion dynamics in online social networks, and also critical for advertisers in viral marketing where there are fans, haters and neutrals.

Optimization of communication network topology for navigation sharing among distributed satellites

Science.gov (United States)

Dang, Zhaohui; Zhang, Yulin

2013-01-01

Navigation sharing among distributed satellites is quite important for coordinated motion and collision avoidance. This paper proposes optimization methods of the communication network topology to achieve navigation sharing. The whole communication network constructing by inter-satellite links are considered as a topology graph. The aim of this paper is to find the communication network topology with minimum communication connections' number (MCCN) in different conditions. It has found that the communication capacity and the number of channels are two key parameters affecting the results. The model of MCCN topology for navigation sharing is established and corresponding method is designed. Two main scenarios, viz., homogeneous case and heterogeneous case, are considered. For the homogeneous case where each member has the same communication capacity, it designs a construction method (Algorithm 1) to find the MCCN topology. For the heterogeneous case, it introduces a modified genetic algorithm (Algorithm 2) to find the MCCN topology. When considering the fact that the number of channels is limited, the Algorithm 2 is further modified by adding a penalized term in the fitness function. The effectiveness of these algorithms is all proved in theoretical. Three examples are further tested to illustrate the methods developed in this paper.

Distributed Sensor Networks

Science.gov (United States)

1979-09-30

University, Pittsburgh, Pennsylvania (1976). 14. R. L. Kirby, "ULISP for PDP-11s with Memory Management ," Report MCS-76-23763, University of Maryland...teletVpe or 9 raphIc S output. The recor iuL, po , uitist il so mon itot its owvn ( Onmand queue and a( knowlede commands Sent to It hN the UsCtr interfa I...kernel. By a net- work kernel we mean a multicomputer distributed operating system kernel that includes proces- sor schedulers, "core" memory managers , and

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...

The establishment of heterogeneous networks in performative spaces

DEFF Research Database (Denmark)

Suenson, Valinka; Harder, Henrik

2010-01-01

This paper presents an empirical research combining the Actor Network Theory with a development of RFID (Radio Frequency Identification) technology for tracking indoor movement behavior. The aim of the paper is to disucss the value of RFID technology as a method for observing heterogeneous networks...... and architecture through this theoretical perspective, it highlights the importance of the user involvement in the performative spaces where materiality becomes an actant on equal terms with the users. This paper discusses the use of RFID technology for tracking indoor movenment behavior as a method for examining...

Multistability in bidirectional associative memory neural networks

International Nuclear Information System (INIS)

Huang Gan; Cao Jinde

2008-01-01

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n-dimensional networks can have 3 n equilibria and 2 n equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results

Multistability in bidirectional associative memory neural networks

Science.gov (United States)

Huang, Gan; Cao, Jinde

2008-04-01

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

Rumor propagation with heterogeneous transmission in social networks

Science.gov (United States)

Vega-Oliveros, Didier A.; Costa, Luciano da F.; Rodrigues, Francisco A.

2017-02-01

Rumor models consider that information transmission occurs with the same probability between each pair of nodes. However, this assumption is not observed in social networks, which contain influential spreaders. To overcome this limitation, we assume that central individuals have a higher capacity to convince their neighbors than peripheral subjects. From extensive numerical simulations we find that spreading is improved in scale-free networks when the transmission probability is proportional to the PageRank, degree, and betweenness centrality. In addition, the results suggest that spreading can be controlled by adjusting the transmission probabilities of the most central nodes. Our results provide a conceptual framework for understanding the interplay between rumor propagation and heterogeneous transmission in social networks.

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

Science.gov (United States)

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Heterogeneity maintenance in glioblastoma: a social network.

Science.gov (United States)

Bonavia, Rudy; Inda, Maria-del-Mar; Cavenee, Webster K; Furnari, Frank B

2011-06-15

Glioblastoma multiforme (GBM), the most common intracranial tumor in adults, is characterized by extensive heterogeneity at the cellular and molecular levels. This insidious feature arises inevitably in almost all cancers and has great significance for the general outcome of the malignancy, because it confounds our understanding of the disease and also intrinsically contributes to the tumor's aggressiveness and poses an obstacle to the design of effective therapies. The classic view that heterogeneity arises as the result of a tumor's "genetic chaos" and the more contemporary cancer stem cell (CSC) hypothesis tend to identify a single cell population as the therapeutic target: the prevailing clone over time in the first case and the CSC in the latter. However, there is growing evidence that the different tumor cell populations may not be simple bystanders. Rather, they can establish a complex network of interactions between each other and with the tumor microenvironment that eventually strengthens tumor growth and increases chances to escape therapy. These differing but complementary ideas about the origin and maintenance of tumor heterogeneity and its importance in GBM are reviewed here.

On the Modeling and Analysis of Heterogeneous Radio Access Networks using a Poisson Cluster Process

DEFF Research Database (Denmark)

Suryaprakash, Vinay; Møller, Jesper; Fettweis, Gerhard P.

processes, some of which are alluded to (later) in this paper. We model a heterogeneous network consisting of two types of base stations by using a particular Poisson cluster process model. The main contributions are two-fold. First, a complete description of the interference in heterogeneous networks...

Techniques for Reducing Consistency-Related Communication in Distributed Shared Memory System

OpenAIRE

Zwaenepoel, W; Bennett, J.K.; Carter, J.B.

1995-01-01

Distributed shared memory 8DSM) is an abstraction of shared memory on a distributed memory machine. Hardware DSM systems support this abstraction at the architecture level; software DSM systems support the abstraction within the runtime system. One of the key problems in building an efficient software DSM system is to reduce the amount of communication needed to keep the distributed memories consistent. In this paper we present four techniques for doing so: 1) software release consistency; 2)...

Distributed coordination of heterogeneous agents using a semantic overlay network and a goal-directed graphplan planner.

Directory of Open Access Journals (Sweden)

António Luís Lopes

Full Text Available In this paper, we describe a distributed coordination system that allows agents to seamlessly cooperate in problem solving by partially contributing to a problem solution and delegating the subproblems for which they do not have the required skills or knowledge to appropriate agents. The coordination mechanism relies on a dynamically built semantic overlay network that allows the agents to efficiently locate, even in very large unstructured networks, the necessary skills for a specific problem. Each agent performs partial contributions to the problem solution using a new distributed goal-directed version of the Graphplan algorithm. This new goal-directed version of the original Graphplan algorithm provides an efficient solution to the problem of "distraction", which most forward-chaining algorithms suffer from. We also discuss a set of heuristics to be used in the backward-search process of the planning algorithm in order to distribute this process amongst idle agents in an attempt to find a solution in less time. The evaluation results show that our approach is effective in building a scalable and efficient agent society capable of solving complex distributable problems.

The effects of working memory training on functional brain network efficiency.

Science.gov (United States)

Langer, Nicolas; von Bastian, Claudia C; Wirz, Helen; Oberauer, Klaus; Jäncke, Lutz

2013-10-01

The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM. Copyright © 2013 Elsevier Ltd. All rights reserved.

Ring interconnection for distributed memory automation and computing system

Energy Technology Data Exchange (ETDEWEB)

Vinogradov, V I [Inst. for Nuclear Research of the Russian Academy of Sciences, Moscow (Russian Federation)

1996-12-31

Problems of development of measurement, acquisition and central systems based on a distributed memory and a ring interface are discussed. It has been found that the RAM LINK-type protocol can be used for ringlet links in non-symmetrical distributed memory architecture multiprocessor system interaction. 5 refs.

Cortical networks dynamically emerge with the interplay of slow and fast oscillations for memory of a natural scene.

Science.gov (United States)

Mizuhara, Hiroaki; Sato, Naoyuki; Yamaguchi, Yoko

2015-05-01

Neural oscillations are crucial for revealing dynamic cortical networks and for serving as a possible mechanism of inter-cortical communication, especially in association with mnemonic function. The interplay of the slow and fast oscillations might dynamically coordinate the mnemonic cortical circuits to rehearse stored items during working memory retention. We recorded simultaneous EEG-fMRI during a working memory task involving a natural scene to verify whether the cortical networks emerge with the neural oscillations for memory of the natural scene. The slow EEG power was enhanced in association with the better accuracy of working memory retention, and accompanied cortical activities in the mnemonic circuits for the natural scene. Fast oscillation showed a phase-amplitude coupling to the slow oscillation, and its power was tightly coupled with the cortical activities for representing the visual images of natural scenes. The mnemonic cortical circuit with the slow neural oscillations would rehearse the distributed natural scene representations with the fast oscillation for working memory retention. The coincidence of the natural scene representations could be obtained by the slow oscillation phase to create a coherent whole of the natural scene in the working memory. Copyright © 2015 Elsevier Inc. All rights reserved.

Heterogeneity of D-Serine Distribution in the Human Central Nervous System

Science.gov (United States)

Suzuki, Masataka; Imanishi, Nobuaki; Mita, Masashi; Hamase, Kenji; Aiso, Sadakazu

2017-01-01

D-serine is an endogenous ligand for N-methyl-D-aspartate glutamate receptors. Accumulating evidence including genetic associations of D-serine metabolism with neurological or psychiatric diseases suggest that D-serine is crucial in human neurophysiology. However, distribution and regulation of D-serine in humans are not well understood. Here, we found that D-serine is heterogeneously distributed in the human central nervous system (CNS). The cerebrum contains the highest level of D-serine among the areas in the CNS. There is heterogeneity in its distribution in the cerebrum and even within the cerebral neocortex. The neocortical heterogeneity is associated with Brodmann or functional areas but is unrelated to basic patterns of cortical layer structure or regional expressional variation of metabolic enzymes for D-serine. Such D-serine distribution may reflect functional diversity of glutamatergic neurons in the human CNS, which may serve as a basis for clinical and pharmacological studies on D-serine modulation. PMID:28604057

Toward heterogeneity in feedforward network with synaptic delays based on FitzHugh-Nagumo model

Science.gov (United States)

Qin, Ying-Mei; Men, Cong; Zhao, Jia; Han, Chun-Xiao; Che, Yan-Qiu

2018-01-01

We focus on the role of heterogeneity on the propagation of firing patterns in feedforward network (FFN). Effects of heterogeneities both in parameters of neuronal excitability and synaptic delays are investigated systematically. Neuronal heterogeneity is found to modulate firing rates and spiking regularity by changing the excitability of the network. Synaptic delays are strongly related with desynchronized and synchronized firing patterns of the FFN, which indicate that synaptic delays may play a significant role in bridging rate coding and temporal coding. Furthermore, quasi-coherence resonance (quasi-CR) phenomenon is observed in the parameter domain of connection probability and delay-heterogeneity. All these phenomena above enable a detailed characterization of neuronal heterogeneity in FFN, which may play an indispensable role in reproducing the important properties of in vivo experiments.

Hybrid shared/distributed parallelism for 3D characteristics transport solvers

International Nuclear Information System (INIS)

Dahmani, M.; Roy, R.

2005-01-01

In this paper, we will present a new hybrid parallel model for solving large-scale 3-dimensional neutron transport problems used in nuclear reactor simulations. Large heterogeneous reactor problems, like the ones that occurs when simulating Candu cores, have remained computationally intensive and impractical for routine applications on single-node or even vector computers. Based on the characteristics method, this new model is designed to solve the transport equation after distributing the calculation load on a network of shared memory multi-processors. The tracks are either generated on the fly at each characteristics sweep or stored in sequential files. The load balancing is taken into account by estimating the calculation load of tracks and by distributing batches of uniform load on each node of the network. Moreover, the communication overhead can be predicted after benchmarking the latency and bandwidth using appropriate network test suite. These models are useful for predicting the performance of the parallel applications and to analyze the scalability of the parallel systems. (authors)

Coherent oscillatory networks supporting short-term memory retention.

Science.gov (United States)

Payne, Lisa; Kounios, John

2009-01-09

Accumulating evidence suggests that top-down processes, reflected by frontal-midline theta-band (4-8 Hz) electroencephalogram (EEG) oscillations, strengthen the activation of a memory set during short-term memory (STM) retention. In addition, the amplitude of posterior alpha-band (8-13 Hz) oscillations during STM retention is thought to reflect a mechanism that protects fragile STM activations from interference by gating bottom-up sensory inputs. The present study addressed two important questions about these phenomena. First, why have previous studies not consistently found memory set-size effects on frontal-midline theta? Second, how does posterior alpha participate in STM retention? To answer these questions, large-scale network connectivity during STM retention was examined by computing EEG wavelet coherence during the retention period of a modified Sternberg task using visually-presented letters as stimuli. The results showed (a) increasing theta-band coherence between frontal-midline and left temporal-parietal sites with increasing memory load, and (b) increasing alpha-band coherence between midline parietal and left temporal/parietal sites with increasing memory load. These findings support the view that theta-band coherence, rather than amplitude, is the key factor in selective top-down strengthening of the memory set and demonstrate that posterior alpha-band oscillations associated with sensory gating are involved in STM retention by participating in the STM network.

Coexistence: Threat to the Performance of Heterogeneous Network

Science.gov (United States)

Sharma, Neetu; Kaur, Amanpreet

2010-11-01

Wireless technology is gaining broad acceptance as users opt for the freedom that only wireless network can provide. Well-accepted wireless communication technologies generally operate in frequency bands that are shared among several users, often using different RF schemes. This is true in particular for WiFi, Bluetooth, and more recently ZigBee. These all three operate in the unlicensed 2.4 GHz band, also known as ISM band, which has been key to the development of a competitive and innovative market for wireless embedded devices. But, as with any resource held in common, it is crucial that those technologies coexist peacefully to allow each user of the band to fulfill its communication goals. This has led to an increase in wireless devices intended for use in IEEE 802.11 wireless local area networks (WLANs) and wireless personal area networks (WPANs), both of which support operation in the crowded 2.4-GHz industrial, scientific and medical (ISM) band. Despite efforts made by standardization bodies to ensure smooth coexistence it may occur that communication technologies transmitting for instance at very different power levels interfere with each other. In particular, it has been pointed out that ZigBee could potentially experience interference from WiFi traffic given that while both protocols can transmit on the same channel, WiFi transmissions usually occur at much higher power level. In this work, we considered a heterogeneous network and analyzed the impact of coexistence between IEEE 802.15.4 and IEEE 802.11b. To evaluate the performance of this network, measurement and simulation study are conducted and developed in the QualNet Network simulator, version 5.0.Model is analyzed for different placement models or topologies such as Random. Grid & Uniform. Performance is analyzed on the basis of characteristics such as throughput, average jitter and average end to end delay. Here, the impact of varying different antenna gain & shadowing model for this

Sparse distributed memory

Science.gov (United States)

Denning, Peter J.

1989-01-01

Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

Distributed Shared Memory for the Cell Broadband Engine (DSMCBE)

DEFF Research Database (Denmark)

Larsen, Morten Nørgaard; Skovhede, Kenneth; Vinter, Brian

2009-01-01

in and out of non-coherent local storage blocks for each special processor element. In this paper we present a software library, namely the Distributed Shared Memory for the Cell Broadband Engine (DSMCBE). By using techniques known from distributed shared memory DSMCBE allows programmers to program the CELL...

Software network analyzer for computer network performance measurement planning over heterogeneous services in higher educational institutes

OpenAIRE

Ismail, Mohd Nazri

2009-01-01

In 21st century, convergences of technologies and services in heterogeneous environment have contributed multi-traffic. This scenario will affect computer network on learning system in higher educational Institutes. Implementation of various services can produce different types of content and quality. Higher educational institutes should have a good computer network infrastructure to support usage of various services. The ability of computer network should consist of i) higher bandwidth; ii) ...

Price Competition in Two-Sided Markets with Heterogeneous Consumers and Network Effects

NARCIS (Netherlands)

Filistrucchi, L.; Klein, T.J.

We model a two-sided market with heterogeneous customers and two heterogeneous network effects. In our model, customers on each market side care differently about both the number and the type of customers on the other side. Examples of two-sided markets are online platforms or daily newspapers. In

A simplified memory network model based on pattern formations

Science.gov (United States)

Xu, Kesheng; Zhang, Xiyun; Wang, Chaoqing; Liu, Zonghua

2014-12-01

Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

Science.gov (United States)

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Out-of-Sequence Preventative Cell Dispatching for Multicast Input-Queued Space-Memory-Memory Clos-Network

DEFF Research Database (Denmark)

Yu, Hao; Ruepp, Sarah Renée; Berger, Michael Stübert

2011-01-01

This paper proposes two out-of-sequence (OOS) preventative cell dispatching algorithms for the multicast input-queued space-memory-memory (IQ-SMM) Clos-network switch architecture, i.e. the multicast flow-based DSRR (MF-DSRR) and the multicast flow-based round-robin (MFRR). Treating each cell...

Languages, compilers and run-time environments for distributed memory machines

CERN Document Server

Saltz, J

1992-01-01

Papers presented within this volume cover a wide range of topics related to programming distributed memory machines. Distributed memory architectures, although having the potential to supply the very high levels of performance required to support future computing needs, present awkward programming problems. The major issue is to design methods which enable compilers to generate efficient distributed memory programs from relatively machine independent program specifications. This book is the compilation of papers describing a wide range of research efforts aimed at easing the task of programmin

Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

Science.gov (United States)

Li, Jun; Zhao, Patrick X

2016-01-01

Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments

Directory of Open Access Journals (Sweden)

Shen Shen

2018-02-01

Full Text Available Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Directory of Open Access Journals (Sweden)

Syed Tahir Hussain Rizvi

2016-12-01

Full Text Available Deep convolutional neural networks achieve state-of-the-art performance in image classification. The computational and memory requirements of such networks are however huge, and that is an issue on embedded devices due to their constraints. Most of this complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. This paper proposes a complete approach to image classification providing common layers used in neural networks. Namely, the proposed approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. The Compute Unified Device Architecture(CUDA-based implementation of the proposed approach is evaluated over three different image classification networks on a Tegra K1 CPU-GPU mobile processor. Experiments show that the presented heterogeneous scheme boasts a 50× speedup over the CPU-only reference and outperforms a GPU-based reference by 2×, while slashing the power consumption by nearly 30%.

Selective adaptation in networks of heterogeneous populations: model, simulation, and experiment.

Directory of Open Access Journals (Sweden)

Avner Wallach

2008-02-01

Full Text Available Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems, this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies, it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various subpopulations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions that were corroborated in both computer simulations and in cultures of cortical neurons developing in vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases.

Obtaining contaminant arrival distributions for steady flow in heterogeneous systems

International Nuclear Information System (INIS)

Anon.

1977-01-01

The versatility of the new contaminant arrival distributions for determining environmental consequences of subsurface pollution problems is demonstrated through application to a field example involving land drainage in heterogeneous porous materials. Though the four phases of the hydrologic evaluations are complicated because of the material heterogeneity encountered in the field problem, the arrival distributions still effectively summarize the minimal amount of data required to determine the environmental implications. These arrival distributions yield a single graph or tabular set of data giving the consequences of the subsurface pollution problems. Accordingly, public control authorities would be well advised to request that the results of subsurface pollution investigations be provided in the form of arrival distributions and the resulting simpler summary curve or tabulation. Such an objective is most easily accomplished through compliance with the requirements for assuring a complete subsurface evaluation

Neural network modeling of associative memory: Beyond the Hopfield model

Science.gov (United States)

Dasgupta, Chandan

1992-07-01

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

A MODEL OF HETEROGENEOUS DISTRIBUTED SYSTEM FOR FOREIGN EXCHANGE PORTFOLIO ANALYSIS

Directory of Open Access Journals (Sweden)

Dragutin Kermek

2006-06-01

Full Text Available The paper investigates the design of heterogeneous distributed system for foreign exchange portfolio analysis. The proposed model includes few separated and dislocated but connected parts through distributed mechanisms. Making system distributed brings new perspectives to performance busting where software based load balancer gets very important role. Desired system should spread over multiple, heterogeneous platforms in order to fulfil open platform goal. Building such a model incorporates different patterns from GOF design patterns, business patterns, J2EE patterns, integration patterns, enterprise patterns, distributed design patterns to Web services patterns. The authors try to find as much as possible appropriate patterns for planned tasks in order to capture best modelling and programming practices.

A Distributed Routing Scheme for Energy Management in Solar Powered Sensor Networks

KAUST Repository

Dehwah, Ahmad H.

2017-10-11

Energy management is critical for solar-powered sensor networks. In this article, we consider data routing policies to optimize the energy in solar powered networks. Motivated by multipurpose sensor networks, the objective is to find the best network policy that maximizes the minimal energy among nodes in a sensor network, over a finite time horizon, given uncertain energy input forecasts. First, we derive the optimal policy in certain special cases using forward dynamic programming. We then introduce a greedy policy that is distributed and exhibits significantly lower complexity. When computationally feasible, we compare the performance of the optimal policy with the greedy policy. We also demonstrate the performance and computational complexity of the greedy policy over randomly simulated networks, and show that it yields results that are almost identical to the optimal policy, for greatly reduced worst-case computational costs and memory requirements. Finally, we demonstrate the implementation of the greedy policy on an experimental sensor network.

Heterogeneity in development of aspects of working memory predicts longitudinal attention deficit hyperactivity disorder symptom change.

Science.gov (United States)

Karalunas, Sarah L; Gustafsson, Hanna C; Dieckmann, Nathan F; Tipsord, Jessica; Mitchell, Suzanne H; Nigg, Joel T

2017-08-01

The role of cognitive mechanisms in the clinical course of neurodevelopmental disorders is poorly understood. Attention Deficit Hyperactivity Disorder (ADHD) is emblematic in that numerous alterations in cognitive development are apparent, yet how they relate to changes in symptom expression with age is unclear. To resolve the role of cognitive mechanisms in ADHD, a developmental perspective that takes into account expected within-group heterogeneity is needed. The current study uses an accelerated longitudinal design and latent trajectory growth mixture models in a sample of children ages 7-13 years carefully characterized as with (n = 437) and without (n = 297) ADHD to (a) identify heterogeneous developmental trajectories for response inhibition, visual spatial working memory maintenance, and delayed reward discounting and (b) to assess the relationships between these cognitive trajectories and ADHD symptom change. Best-fitting models indicated multiple trajectory classes in both the ADHD and typically developing samples, as well as distinct relationships between each cognitive process and ADHD symptom change. Developmental change in response inhibition and delayed reward discounting were unrelated to ADHD symptom change, while individual differences in the rate of visual spatial working memory maintenance improvement predicted symptom remission in ADHD. Characterizing heterogeneity in cognitive development will be crucial for clarifying mechanisms of symptom persistence and recovery. Results here suggest working memory maintenance may be uniquely related to ADHD symptom improvement. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

An Incremental Time-delay Neural Network for Dynamical Recurrent Associative Memory

Institute of Scientific and Technical Information of China (English)

无

2002-01-01

An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.

Guifi.net: Security analysis of a heterogeneous community network

OpenAIRE

Ramos García, Patricia

2018-01-01

Guifi.net is a heterogeneous community network that brings Internet to rural areas or vulnerable groups. This opens the door to many advances, but encompasses some risks as well. The aim of this project is to assess the general security of Guifi.net from tests performed on a key network element: the router. In particular, MikroTik and Ubiquiti are the most used makes in Guifi.net and hence, the target of this project. Basic, yet important, security settings are tested. On the plus side, the ...

On the area spectral efficiency improvement of heterogeneous network by exploiting the integration of macro-femto cellular networks

KAUST Repository

Shakir, Muhammad; Alouini, Mohamed-Slim

2012-01-01

. In this paper, we consider a Heterogeneous network where we complement the macrocell network with low-power low-cost user deployed nodes, such as femtocell base stations to increase the mean achievable capacity of the system. In this context, we integrate macro

ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...

Energy Harvesting in Heterogeneous Networks with Hybrid Powered Communication Systems

KAUST Repository

Alsharoa, Ahmad; Celik, Abdulkadir; Kamal, Ahmed E.

2018-01-01

In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

Science.gov (United States)

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

Directory of Open Access Journals (Sweden)

Rodrigo F. O. Pena

2018-03-01

Full Text Available Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i different neural subpopulations (e.g., excitatory and inhibitory neurons have different cellular or connectivity parameters; (ii the number and strength of the input connections are random (Erdős-Rényi topology and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of

Distributed sensor networks

CERN Document Server

Rubin, Donald B; Carlin, John B; Iyengar, S Sitharama; Brooks, Richard R; University, Clemson

2014-01-01

An Overview, S.S. Iyengar, Ankit Tandon, and R.R. BrooksMicrosensor Applications, David ShepherdA Taxonomy of Distributed Sensor Networks, Shivakumar Sastry and S.S. IyengarContrast with Traditional Systems, R.R. BrooksDigital Signal Processing Background, Yu Hen HuImage-Processing Background Lynne Grewe and Ben ShahshahaniObject Detection and Classification, Akbar M. SayeedParameter Estimation David FriedlanderTarget Tracking with Self-Organizing Distributed Sensors R.R. Brooks, C. Griffin, D.S. Friedlander, and J.D. KochCollaborative Signal and Information Processing: AnInformation-Directed Approach Feng Zhao, Jie Liu, Juan Liu, Leonidas Guibas, and James ReichEnvironmental Effects, David C. SwansonDetecting and Counteracting Atmospheric Effects Lynne L. GreweSignal Processing and Propagation for Aeroacoustic Sensor Networks, Richard J. Kozick, Brian M. Sadler, and D. Keith WilsonDistributed Multi-Target Detection in Sensor Networks Xiaoling Wang, Hairong Qi, and Steve BeckFoundations of Data Fusion f...

Effective Data Acquisition Protocol for Multi-Hop Heterogeneous Wireless Sensor Networks Using Compressive Sensing

Directory of Open Access Journals (Sweden)

Ahmed M. Khedr

2015-10-01

Full Text Available In designing wireless sensor networks (WSNs, it is important to reduce energy dissipation and prolong network lifetime. Clustering of nodes is one of the most effective approaches for conserving energy in WSNs. Cluster formation protocols generally consider the heterogeneity of sensor nodes in terms of energy difference of nodes but ignore the different transmission ranges of them. In this paper, we propose an effective data acquisition clustered protocol using compressive sensing (EDACP-CS for heterogeneous WSNs that aims to conserve the energy of sensor nodes in the presence of energy and transmission range heterogeneity. In EDACP-CS, cluster heads are selected based on the distance from the base station and sensor residual energy. Simulation results show that our protocol offers a much better performance than the existing protocols in terms of energy consumption, stability, network lifetime, and throughput.

Beaver dams maintain fish biodiversity by increasing habitat heterogeneity throughout a low-gradient stream network

Science.gov (United States)

Smith, Joseph M.; Mather, Martha E.

2013-01-01

Understanding the relationship between heterogeneity and biodiversity is an active focus of ecological research. Although habitat heterogeneity is conceptually linked to biodiversity, the amount and configuration of heterogeneity that maintains biodiversity within ecosystems is not well understood, especially for an entire stream network.

Factors affecting reorganisation of memory encoding networks in temporal lobe epilepsy

Science.gov (United States)

Sidhu, M.K.; Stretton, J.; Winston, G.P.; Symms, M.; Thompson, P.J.; Koepp, M.J.; Duncan, J.S.

2015-01-01

Summary Aims In temporal lobe epilepsy (TLE) due to hippocampal sclerosis reorganisation in the memory encoding network has been consistently described. Distinct areas of reorganisation have been shown to be efficient when associated with successful subsequent memory formation or inefficient when not associated with successful subsequent memory. We investigated the effect of clinical parameters that modulate memory functions: age at onset of epilepsy, epilepsy duration and seizure frequency in a large cohort of patients. Methods We studied 53 patients with unilateral TLE and hippocampal sclerosis (29 left). All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words. A continuous regression analysis was used to investigate the effects of age at onset of epilepsy, epilepsy duration and seizure frequency on the activation patterns in the memory encoding network. Results Earlier age at onset of epilepsy was associated with left posterior hippocampus activations that were involved in successful subsequent memory formation in left hippocampal sclerosis patients. No association of age at onset of epilepsy was seen with face encoding in right hippocampal sclerosis patients. In both left hippocampal sclerosis patients during word encoding and right hippocampal sclerosis patients during face encoding, shorter duration of epilepsy and lower seizure frequency were associated with medial temporal lobe activations that were involved in successful memory formation. Longer epilepsy duration and higher seizure frequency were associated with contralateral extra-temporal activations that were not associated with successful memory formation. Conclusion Age at onset of epilepsy influenced verbal memory encoding in patients with TLE due to hippocampal sclerosis in the speech-dominant hemisphere. Shorter duration of epilepsy and lower seizure frequency were associated with less disruption of the efficient memory encoding network whilst

Conformity-based cooperation in online social networks: The effect of heterogeneous social influence

International Nuclear Information System (INIS)

Xu, Bo; Wang, Jianwei; Zhang, Xuejun

2015-01-01

This paper extends the conformity model by introducing heterogeneous social influence into the analysis. We associate the influence of a player in the network with its degree centrality assuming that players of higher degree exhibit more social influence on its neighbors. The results show that the equilibrium level of cooperators can be dramatically enhanced if the conformity-driven players are preferentially influenced by neighbors of higher degree. We attribute this finding to two elementary mechanisms in the evolutionary process: (1) degree-based social influence facilitates the formation of strategic clusters around hubs; and (2) payoff-heterogeneity between cooperative clusters and defective clusters contributes to the promotion of cooperation. This research reveals the important role of heterogeneous social influence on the emergence of cooperation in social networks.

Heterogeneous continuous-time random walks

Science.gov (United States)

Grebenkov, Denis S.; Tupikina, Liubov

2018-01-01

We introduce a heterogeneous continuous-time random walk (HCTRW) model as a versatile analytical formalism for studying and modeling diffusion processes in heterogeneous structures, such as porous or disordered media, multiscale or crowded environments, weighted graphs or networks. We derive the exact form of the propagator and investigate the effects of spatiotemporal heterogeneities onto the diffusive dynamics via the spectral properties of the generalized transition matrix. In particular, we show how the distribution of first-passage times changes due to local and global heterogeneities of the medium. The HCTRW formalism offers a unified mathematical language to address various diffusion-reaction problems, with numerous applications in material sciences, physics, chemistry, biology, and social sciences.

Distributed terascale volume visualization using distributed shared virtual memory

KAUST Repository

Beyer, Johanna; Hadwiger, Markus; Schneider, Jens; Jeong, Wonki; Pfister, Hanspeter

2011-01-01

Table 1 illustrates the impact of different distribution unit sizes, different screen resolutions, and numbers of GPU nodes. We use two and four GPUs (NVIDIA Quadro 5000 with 2.5 GB memory) and a mouse cortex EM dataset (see Figure 2) of resolution

Heterogeneous ice slurry flow and concentration distribution in horizontal pipes

International Nuclear Information System (INIS)

Wang, Jihong; Zhang, Tengfei; Wang, Shugang

2013-01-01

Highlights: • A Mixture CFD model is applied to describe heterogeneous ice slurry flow. • The ice slurry rheological behavior is considered piecewise. • The coupled flow and concentration profiles in heterogeneous slurry flow is acquired. • The current numerical model achieves good balance between precision and universality. -- Abstract: Ice slurry is an energy-intensive solid–liquid mixture fluid which may play an important role in various cooling purposes. Knowing detailed flow information is important from the system design point of view. However, the heterogeneous ice slurry flow makes it difficult to be quantified due to the complex two phase flow characteristic. The present study applies a Mixture computational fluid dynamics (CFD) model based on different rheological behavior to characterize the heterogeneous ice slurry flow. The Mixture CFD model was firstly validated by three different experiments. Then the validated Mixture CFD model was applied to solve the ice slurry isothermal flow by considering the rheological behavior piecewise. Finally, the numerical solutions have displayed the coupled flow information, such as slurry velocity, ice particle concentration and pressure drop distribution. The results show that, the ice slurry flow distribution will appear varying degree of asymmetry under different operating conditions. The rheological behavior will be affected by the asymmetric flow distributions. When mean flow velocity is high, Thomas equation can be appropriate for describing ice slurry viscosity. While with the decreasing of mean flow velocity, the ice slurry behaves Bingham rheology. As compared with experimental pressure drop results, the relative errors of numerical computation are almost within ±15%. The Mixture CFD model is validated to be an effective model for describing heterogeneous ice slurry flow and could supply plentiful flow information

The distribution and the functions of autobiographical memories: Why do older adults remember autobiographical memories from their youth?

Science.gov (United States)

Wolf, Tabea; Zimprich, Daniel

2016-09-01

In the present study, the distribution of autobiographical memories was examined from a functional perspective: we examined whether the extent to which long-term autobiographical memories were rated as having a self-, a directive, or a social function affects the location (mean age) and scale (standard deviation) of the memory distribution. Analyses were based on a total of 5598 autobiographical memories generated by 149 adults aged between 50 and 81 years in response to 51 cue-words. Participants provided their age at the time when the recalled events had happened and rated how frequently they recall these events for self-, directive, and social purposes. While more frequently using autobiographical memories for self-functions was associated with an earlier mean age, memories frequently shared with others showed a narrower distribution around a later mean age. The directive function, by contrast, did not affect the memory distribution. The results strengthen the assumption that experiences from an individual's late adolescence serve to maintain a sense of self-continuity throughout the lifespan. Experiences that are frequently shared with others, in contrast, stem from a narrow age range located in young adulthood.

On the Dual-Decomposition-Based Resource and Power Allocation with Sleeping Strategy for Heterogeneous Networks

KAUST Repository

Alsharoa, Ahmad M.

2015-05-01

In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality of service determined by each target data rate. We study the model where one macrocell base station is placed in the cell center, and multiple small cell base stations and femtocell access points are distributed around it. The dual decomposition technique is adopted to jointly optimize the power and carrier allocation in the downlink direction in addition to the selection of turned off small cell base stations. Our numerical results investigate the performance of the proposed scheme versus different system parameters and show an important saving in terms of total power consumption. © 2015 IEEE.

Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors

Directory of Open Access Journals (Sweden)

Hao Yan

2016-01-01

Full Text Available Traumatic brain injuries (TBIs are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub, left hippocampus (the personal experience binding hub, and left parahippocampal gyrus (the contextual association hub were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory and the right medial frontal gyrus (MeFG in the anterior prefrontal cortex (PFC. We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging.

Simulation of Routing Protocol with CoS/QoS Enhancements in Heterogeneous Communication Network

National Research Council Canada - National Science Library

Kubera, Emil; Sliwa, Joanna; Zubel, Krzysztof; Mroczko, Adrian

2006-01-01

The article describes the subject of QoS routing mechanism in tactical heterogeneous communication network consisting of network elements built in different technologies and connected following rules...

Top-down and bottom-up attention-to-memory: mapping functional connectivity in two distinct networks that underlie cued and uncued recognition memory.

Science.gov (United States)

Burianová, Hana; Ciaramelli, Elisa; Grady, Cheryl L; Moscovitch, Morris

2012-11-15

The objective of this study was to examine the functional connectivity of brain regions active during cued and uncued recognition memory to test the idea that distinct networks would underlie these memory processes, as predicted by the attention-to-memory (AtoM) hypothesis. The AtoM hypothesis suggests that dorsal parietal cortex (DPC) allocates effortful top-down attention to memory retrieval during cued retrieval, whereas ventral parietal cortex (VPC) mediates spontaneous bottom-up capture of attention by memory during uncued retrieval. To identify networks associated with these two processes, we conducted a functional connectivity analysis of a left DPC and a left VPC region, both identified by a previous analysis of task-related regional activations. We hypothesized that the two parietal regions would be functionally connected with distinct neural networks, reflecting their engagement in the differential mnemonic processes. We found two spatially dissociated networks that overlapped only in the precuneus. During cued trials, DPC was functionally connected with dorsal attention areas, including the superior parietal lobules, right precuneus, and premotor cortex, as well as relevant memory areas, such as the left hippocampus and the middle frontal gyri. During uncued trials, VPC was functionally connected with ventral attention areas, including the supramarginal gyrus, cuneus, and right fusiform gyrus, as well as the parahippocampal gyrus. In addition, activity in the DPC network was associated with faster response times for cued retrieval. This is the first study to show a dissociation of the functional connectivity of posterior parietal regions during episodic memory retrieval, characterized by a top-down AtoM network involving DPC and a bottom-up AtoM network involving VPC. Copyright © 2012 Elsevier Inc. All rights reserved.

DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.

Science.gov (United States)

Han, Ruisong; Yang, Wei; Wang, Yipeng; You, Kaiming

2017-05-01

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

International Nuclear Information System (INIS)

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron; Arnold, Dorian

2017-01-01

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation at scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.

in Heterogeneous Media

Directory of Open Access Journals (Sweden)

Saeed Balouchi

2013-01-01

Full Text Available Fractured reservoirs contain about 85 and 90 percent of oil and gas resources respectively in Iran. A comprehensive study and investigation of fractures as the main factor affecting fluid flow or perhaps barrier seems necessary for reservoir development studies. High degrees of heterogeneity and sparseness of data have incapacitated conventional deterministic methods in fracture network modeling. Recently, simulated annealing (SA has been applied to generate stochastic realizations of spatially correlated fracture networks by assuming that the elastic energy of fractures follows Boltzmann distribution. Although SA honors local variability, the objective function of geometrical fracture modeling is defined for homogeneous conditions. In this study, after the introduction of SA and the derivation of the energy function, a novel technique is presented to adjust the model with highly heterogeneous data for a fractured field from the southwest of Iran. To this end, the regular object-based model is combined with a grid-based technique to cover the heterogeneity of reservoir properties. The original SA algorithm is also modified by being constrained in different directions and weighting the energy function to make it appropriate for heterogeneous conditions. The simulation results of the presented approach are in good agreement with the observed field data.

A multi-attribute vertical handoff scheme for heterogeneous wireless networks

Directory of Open Access Journals (Sweden)

JI Xiaolong

2014-04-01

Full Text Available In order to meet the user demand for different services as well as to mitigate the Ping-pong effect caused by vertical handoff for wireless network,a multi-attribute vertical handoff scheme for heterogeneous wireless network is proposed.In the algorithm,a fuzzy logic method is used to make pre-decision.The optimal handoff target network is selected by a cost function of network which uses an Analytic Hierarchy Process to calculate the weights of SNR,delay,cost and user preference in different business scenarios.Simulation is performed in the environment which is overlapped by WiMAX and UMTS networks.Results show that the proposed approach can effectively reduce the number of handoff and power consumption in a condition to satisfy the user needs.

Recurrent Neural Network For Forecasting Time Series With Long Memory Pattern

Science.gov (United States)

Walid; Alamsyah

2017-04-01

Recurrent Neural Network as one of the hybrid models are often used to predict and estimate the issues related to electricity, can be used to describe the cause of the swelling of electrical load which experienced by PLN. In this research will be developed RNN forecasting procedures at the time series with long memory patterns. Considering the application is the national electrical load which of course has a different trend with the condition of the electrical load in any country. This research produces the algorithm of time series forecasting which has long memory pattern using E-RNN after this referred to the algorithm of integrated fractional recurrent neural networks (FIRNN).The prediction results of long memory time series using models Fractional Integrated Recurrent Neural Network (FIRNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Recurrent Neural Network (FIRNN) (24,6,1) provides the smallest MSE value, which is 0.00149684.

Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales.

Directory of Open Access Journals (Sweden)

Eduardo Freitas Moreira

Full Text Available Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for

Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales.

Science.gov (United States)

Moreira, Eduardo Freitas; Boscolo, Danilo; Viana, Blandina Felipe

2015-01-01

Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for these relationships and

Electrical resistivity sounding to study water content distribution in heterogeneous soils

Science.gov (United States)

Electrical resistivity (ER) sounding is increasingly being used as non-invasive technique to reveal and map soil heterogeneity. The objective of this work was to assess ER sounding applicability to study soil water distribution in spatially heterogeneous soils. The 30x30-m study plot was located at ...

ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS

Directory of Open Access Journals (Sweden)

Amanda Prorok

2016-02-01

Full Text Available We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type is defined by the traits (capabilities that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

One-way shared memory

DEFF Research Database (Denmark)

Schoeberl, Martin

2018-01-01

Standard multicore processors use the shared main memory via the on-chip caches for communication between cores. However, this form of communication has two limitations: (1) it is hardly time-predictable and therefore not a good solution for real-time systems and (2) this single shared memory...... is a bottleneck in the system. This paper presents a communication architecture for time-predictable multicore systems where core-local memories are distributed on the chip. A network-on-chip constantly copies data from a sender core-local memory to a receiver core-local memory. As this copying is performed...... in one direction we call this architecture a one-way shared memory. With the use of time-division multiplexing for the memory accesses and the network-on-chip routers we achieve a time-predictable solution where the communication latency and bandwidth can be bounded. An example architecture for a 3...

Fast mapping rapidly integrates information into existing memory networks.

Science.gov (United States)

Coutanche, Marc N; Thompson-Schill, Sharon L

2014-12-01

Successful learning involves integrating new material into existing memory networks. A learning procedure known as fast mapping (FM), thought to simulate the word-learning environment of children, has recently been linked to distinct neuroanatomical substrates in adults. This idea has suggested the (never-before tested) hypothesis that FM may promote rapid incorporation into cortical memory networks. We test this hypothesis here in 2 experiments. In our 1st experiment, we introduced 50 participants to 16 unfamiliar animals and names through FM or explicit encoding (EE) and tested participants on the training day, and again after sleep. Learning through EE produced strong declarative memories, without immediate lexical competition, as expected from slow-consolidation models. Learning through FM, however, led to almost immediate lexical competition, which continued to the next day. Additionally, the learned words began to prime related concepts on the day following FM (but not EE) training. In a 2nd experiment, we replicated the lexical integration results and determined that presenting an already-known item during learning was crucial for rapid integration through FM. The findings presented here indicate that learned items can be integrated into cortical memory networks at an accelerated rate through fast mapping. The retrieval of a related known concept, in order to infer the target of the FM question, is critical for this effect. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Wealth distribution on complex networks

Science.gov (United States)

Ichinomiya, Takashi

2012-12-01

We study the wealth distribution of the Bouchaud-Mézard model on complex networks. It is known from numerical simulations that this distribution depends on the topology of the network; however, no one has succeeded in explaining it. Using “adiabatic” and “independent” assumptions along with the central-limit theorem, we derive equations that determine the probability distribution function. The results are compared to those of simulations for various networks. We find good agreement between our theory and the simulations, except for the case of Watts-Strogatz networks with a low rewiring rate due to the breakdown of independent assumption.

Short-term memory capacity in networks via the restricted isometry property.

Science.gov (United States)

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Musical and verbal semantic memory: two distinct neural networks?

Science.gov (United States)

Groussard, M; Viader, F; Hubert, V; Landeau, B; Abbas, A; Desgranges, B; Eustache, F; Platel, H

2010-02-01

Semantic memory has been investigated in numerous neuroimaging and clinical studies, most of which have used verbal or visual, but only very seldom, musical material. Clinical studies have suggested that there is a relative neural independence between verbal and musical semantic memory. In the present study, "musical semantic memory" is defined as memory for "well-known" melodies without any knowledge of the spatial or temporal circumstances of learning, while "verbal semantic memory" corresponds to general knowledge about concepts, again without any knowledge of the spatial or temporal circumstances of learning. Our aim was to compare the neural substrates of musical and verbal semantic memory by administering the same type of task in each modality. We used high-resolution PET H(2)O(15) to observe 11 young subjects performing two main tasks: (1) a musical semantic memory task, where the subjects heard the first part of familiar melodies and had to decide whether the second part they heard matched the first, and (2) a verbal semantic memory task with the same design, but where the material consisted of well-known expressions or proverbs. The musical semantic memory condition activated the superior temporal area and inferior and middle frontal areas in the left hemisphere and the inferior frontal area in the right hemisphere. The verbal semantic memory condition activated the middle temporal region in the left hemisphere and the cerebellum in the right hemisphere. We found that the verbal and musical semantic processes activated a common network extending throughout the left temporal neocortex. In addition, there was a material-dependent topographical preference within this network, with predominantly anterior activation during musical tasks and predominantly posterior activation during semantic verbal tasks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Aggregated Representation of Distribution Networks for Large-Scale Transmission Network Simulations

DEFF Research Database (Denmark)

Göksu, Ömer; Altin, Müfit; Sørensen, Poul Ejnar

2014-01-01

As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include...... the distributed generation within those analysis. In this paper a practical methodology to obtain aggregated behaviour of the distributed generation is proposed. The methodology, which is based on the use of the IEC standard wind turbine models, is applied on a benchmark distribution network via simulations....

Sparse Distributed Memory: understanding the speed and robustness of expert memory

Directory of Open Access Journals (Sweden)

Marcelo Salhab Brogliato

2014-04-01

Full Text Available How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the `tip-of-tongue' memory event--which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve to this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory.

Software-Defined Radio Global System for Mobile Communications Transmitter Development for Heterogeneous Network Vulnerability Testing

Science.gov (United States)

2013-12-01

AbdelWahab, “ 2G / 3G Inter-RAT Handover Performance Analysis,” Second European Conference on Antennas and Propagation, pp. 1, 8, 11–16, Nov. 2007. [19] J...RADIO GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS TRANSMITTER DEVELOPMENT FOR HETEROGENEOUS NETWORK VULNERABILITY TESTING by Carson C. McAbee... MOBILE COMMUNICATIONS TRANSMITTER DEVELOPMENT FOR HETEROGENEOUS NETWORK VULNERABILITY TESTING 5. FUNDING NUMBERS 6. AUTHOR(S) Carson C. McAbee

Observed and unobserved heterogeneity in stochastic frontier models: An application to the electricity distribution industry

International Nuclear Information System (INIS)

Kopsakangas-Savolainen, Maria; Svento, Rauli

2011-01-01

In this study we combine different possibilities to model firm level heterogeneity in stochastic frontier analysis. We show that both observed and unobserved heterogeneities cause serious biases in inefficiency results. Modelling observed and unobserved heterogeneities treat individual firms in different ways and even though the expected mean inefficiency scores in both cases diminish the firm level efficiency rank orders turn out to be very different. The best fit with the data is obtained by modelling unobserved heterogeneity through randomizing frontier parameters and at the same time explicitly modelling the observed heterogeneity into the inefficiency distribution. These results are obtained by using data from Finnish electricity distribution utilities and the results are relevant in relation to electricity distribution pricing and regulation. -- Research Highlights: → We show that both observed and unobserved heterogeneities of firms cause biases in inefficiency results. → Different ways of accounting firm level heterogeneity end up with very different rank orders of firms. → The model which combines the characteristics of unobserved and observed heterogeneity fits the data best.

Resource allocation in heterogeneous cloud radio access networks: advances and challenges

KAUST Repository

Dahrouj, Hayssam

2015-06-01

Base station densification is increasingly used by network operators to provide better throughput and coverage performance to mobile subscribers in dense data traffic areas. Such densification is progressively diffusing the move from traditional macrocell base stations toward heterogeneous networks with diverse cell sizes (e.g., microcell, picocell, femotcell) and diverse radio access technologies (e.g., GSM, CDMA), and LTE). The coexistence of the different network entities brings an additional set of challenges, particularly in terms of the provisioning of high-speed communications and the management of wireless interference. Resource sharing between different entities, largely incompatible in conventional systems due to the lack of interconnections, becomes a necessity. By connecting all the base stations from different tiers to a central processor (referred to as the cloud) through wire/wireline backhaul links, the heterogeneous cloud radio access network, H-CRAN, provides an open, simple, controllable, and flexible paradigm for resource allocation. This article discusses challenges and recent developments in H-CRAN design. It proposes promising resource allocation schemes in H-CRAN: coordinated scheduling, hybrid backhauling, and multicloud association. Simulations results show how the proposed strategies provide appreciable performance improvement compared to methods from recent literature. © 2015 IEEE.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

Science.gov (United States)

Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

2016-01-01

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

Science.gov (United States)

Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

2016-04-13

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear

Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

DEFF Research Database (Denmark)

Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

2017-01-01

it easier to find unused memory location and therefor facilitates the evolution of continual learning networks. Our results suggest that augmenting evolving networks with an external memory component is not only a viable mechanism for adaptive behaviors in neuroevolution but also allows these networks...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...... associations quickly and over long periods of time. The results in this paper demonstrate that the ENTM is able to perform one-shot learning in reinforcement learning tasks without catastrophic forgetting of previously stored associations. Additionally, we introduce a new ENTM default jump mechanism that makes...

Opinion formation and distribution in a bounded-confidence model on various networks

Science.gov (United States)

Meng, X. Flora; Van Gorder, Robert A.; Porter, Mason A.

2018-02-01

In the social, behavioral, and economic sciences, it is important to predict which individual opinions eventually dominate in a large population, whether there will be a consensus, and how long it takes for a consensus to form. Such ideas have been studied heavily both in physics and in other disciplines, and the answers depend strongly both on how one models opinions and on the network structure on which opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider heterogeneity of interactions in a population along with social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data). We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the time to reach steady states on various model parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have important effects on the convergence time and the number of steady-state opinion groups. For some network architectures, we observe that the relationship between the convergence time and model parameters undergoes a transition at a critical value of the confidence bound. For some networks, the steady-state opinion distribution also changes from consensus to multiple opinion groups at this critical value.

Memory and pattern storage in neural networks with activity dependent synapses

Science.gov (United States)

Mejias, J. F.; Torres, J. J.

2009-01-01

We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

Information system architecture to support transparent access to distributed, heterogeneous data sources

International Nuclear Information System (INIS)

Brown, J.C.

1994-08-01

Quality situation assessment and decision making require access to multiple sources of data and information. Insufficient accessibility to data exists for many large corporations and Government agencies. By utilizing current advances in computer technology, today's situation analyst's have a wealth of information at their disposal. There are many potential solutions to the information accessibility problem using today's technology. The United States Department of Energy (US-DOE) faced this problem when dealing with one class of problem in the US. The result of their efforts has been the creation of the Tank Waste Information Network System -- TWINS. The TWINS solution combines many technologies to address problems in several areas such as User Interfaces, Transparent Access to Multiple Data Sources, and Integrated Data Access. Data related to the complex is currently distributed throughout several US-DOE installations. Over time, each installation has adopted their own set of standards as related to information management. Heterogeneous hardware and software platforms exist both across the complex and within a single installation. Standards for information management vary between US-DOE mission areas within installations. These factors contribute to the complexity of accessing information in a manner that enhances the performance and decision making process of the analysts. This paper presents one approach taken by the DOE to resolve the problem of distributed, heterogeneous, multi-media information management for the HLW Tank complex. The information system architecture developed for the DOE by the TWINS effort is one that is adaptable to other problem domains and uses

A distribution network review

International Nuclear Information System (INIS)

Fairbairn, R.J.; Maunder, D.; Kenyon, P.

1999-01-01

This report summarises the findings of a study reviewing the distribution network in England, Scotland and Wales to evaluate its ability to accommodate more embedded generation from both fossil fuel and renewable energy sources. The background to the study is traced, and descriptions of the existing electricity supply system, the licence conditions relating to embedded generation, and the effects of the Review of Electricity Trading Arrangements are given. The ability of the UK distribution networks to accept embedded generation is examined, and technical benefits/drawbacks arising from embedded generation, and the potential for uptake of embedded generation technologies are considered. The distribution network capacity and the potential uptake of embedded generation are compared, and possible solutions to overcome obstacles are suggested. (UK)

A distribution network review

Energy Technology Data Exchange (ETDEWEB)

Fairbairn, R.J.; Maunder, D.; Kenyon, P.

1999-07-01

This report summarises the findings of a study reviewing the distribution network in England, Scotland and Wales to evaluate its ability to accommodate more embedded generation from both fossil fuel and renewable energy sources. The background to the study is traced, and descriptions of the existing electricity supply system, the licence conditions relating to embedded generation, and the effects of the Review of Electricity Trading Arrangements are given. The ability of the UK distribution networks to accept embedded generation is examined, and technical benefits/drawbacks arising from embedded generation, and the potential for uptake of embedded generation technologies are considered. The distribution network capacity and the potential uptake of embedded generation are compared, and possible solutions to overcome obstacles are suggested. (UK)

A Bio-Inspired QoS-Oriented Handover Model in Heterogeneous Wireless Networks

Directory of Open Access Journals (Sweden)

Daxin Tian

2014-01-01

Full Text Available We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.

Advanced Distribution Network Modelling with Distributed Energy Resources

Science.gov (United States)

O'Connell, Alison

The addition of new distributed energy resources, such as electric vehicles, photovoltaics, and storage, to low voltage distribution networks means that these networks will undergo major changes in the future. Traditionally, distribution systems would have been a passive part of the wider power system, delivering electricity to the customer and not needing much control or management. However, the introduction of these new technologies may cause unforeseen issues for distribution networks, due to the fact that they were not considered when the networks were originally designed. This thesis examines different types of technologies that may begin to emerge on distribution systems, as well as the resulting challenges that they may impose. Three-phase models of distribution networks are developed and subsequently utilised as test cases. Various management strategies are devised for the purposes of controlling distributed resources from a distribution network perspective. The aim of the management strategies is to mitigate those issues that distributed resources may cause, while also keeping customers' preferences in mind. A rolling optimisation formulation is proposed as an operational tool which can manage distributed resources, while also accounting for the uncertainties that these resources may present. Network sensitivities for a particular feeder are extracted from a three-phase load flow methodology and incorporated into an optimisation. Electric vehicles are the focus of the work, although the method could be applied to other types of resources. The aim is to minimise the cost of electric vehicle charging over a 24-hour time horizon by controlling the charge rates and timings of the vehicles. The results demonstrate the advantage that controlled EV charging can have over an uncontrolled case, as well as the benefits provided by the rolling formulation and updated inputs in terms of cost and energy delivered to customers. Building upon the rolling optimisation, a

Large scale network-centric distributed systems

CERN Document Server

Sarbazi-Azad, Hamid

2014-01-01

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

Memory-assisted measurement-device-independent quantum key distribution

Science.gov (United States)

Panayi, Christiana; Razavi, Mohsen; Ma, Xiongfeng; Lütkenhaus, Norbert

2014-04-01

A protocol with the potential of beating the existing distance records for conventional quantum key distribution (QKD) systems is proposed. It borrows ideas from quantum repeaters by using memories in the middle of the link, and that of measurement-device-independent QKD, which only requires optical source equipment at the user's end. For certain memories with short access times, our scheme allows a higher repetition rate than that of quantum repeaters with single-mode memories, thereby requiring lower coherence times. By accounting for various sources of nonideality, such as memory decoherence, dark counts, misalignment errors, and background noise, as well as timing issues with memories, we develop a mathematical framework within which we can compare QKD systems with and without memories. In particular, we show that with the state-of-the-art technology for quantum memories, it is potentially possible to devise memory-assisted QKD systems that, at certain distances of practical interest, outperform current QKD implementations.

Memory-assisted measurement-device-independent quantum key distribution

International Nuclear Information System (INIS)

Panayi, Christiana; Razavi, Mohsen; Ma, Xiongfeng; Lütkenhaus, Norbert

2014-01-01

A protocol with the potential of beating the existing distance records for conventional quantum key distribution (QKD) systems is proposed. It borrows ideas from quantum repeaters by using memories in the middle of the link, and that of measurement-device-independent QKD, which only requires optical source equipment at the user's end. For certain memories with short access times, our scheme allows a higher repetition rate than that of quantum repeaters with single-mode memories, thereby requiring lower coherence times. By accounting for various sources of nonideality, such as memory decoherence, dark counts, misalignment errors, and background noise, as well as timing issues with memories, we develop a mathematical framework within which we can compare QKD systems with and without memories. In particular, we show that with the state-of-the-art technology for quantum memories, it is potentially possible to devise memory-assisted QKD systems that, at certain distances of practical interest, outperform current QKD implementations. (paper)

Distribution of model-based multipoint heterogeneity lod scores.

Science.gov (United States)

Xing, Chao; Morris, Nathan; Xing, Guan

2010-12-01

The distribution of two-point heterogeneity lod scores (HLOD) has been intensively investigated because the conventional χ(2) approximation to the likelihood ratio test is not directly applicable. However, there was no study investigating th e distribution of the multipoint HLOD despite its wide application. Here we want to point out that, compared with the two-point HLOD, the multipoint HLOD essentially tests for homogeneity given linkage and follows a relatively simple limiting distribution ½χ²₀+ ½χ²₁, which can be obtained by established statistical theory. We further examine the theoretical result by simulation studies. © 2010 Wiley-Liss, Inc.

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

Directory of Open Access Journals (Sweden)

Andras eJakab

2014-10-01

Full Text Available The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st – 38th gestational weeks (GW with a network-based statistical inference approach. The overall connectivity network, short range and interhemispheric connections showed sigmoid expansion curve peaking at the 26-29. GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW, temporal (peak: 26 GW, frontal (peak: 26.4 GW and parietal expansion (peak: 27.5 GW. We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macroconnectivity.

Fetal functional imaging portrays heterogeneous development of emerging human brain networks.

Science.gov (United States)

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26-29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.

Robust Synchronization in an E/I Network with Medium Synaptic Delay and High Level of Heterogeneity

International Nuclear Information System (INIS)

Han Fang; Wang Zhi-Jie; Gong Tao; Fan Hong

2015-01-01

It is known that both excitatory and inhibitory neuronal networks can achieve robust synchronization only under certain conditions, such as long synaptic delay or low level of heterogeneity. In this work, robust synchronization can be found in an excitatory/inhibitory (E/I) neuronal network with medium synaptic delay and high level of heterogeneity, which often occurs in real neuronal networks. Two effects of post-synaptic potentials (PSP) to network synchronization are presented, and the synaptic contribution of excitatory and inhibitory neurons to robust synchronization in this E/I network is investigated. It is found that both excitatory and inhibitory neurons may contribute to robust synchronization in E/I networks, especially the excitatory PSP has a more positive effect on synchronization in E/I networks than that in excitatory networks. This may explain the strong robustness of synchronization in E/I neuronal networks. (paper)