Wisdom of crowds for robust gene network inference

PubMed Central

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

2012-01-01

Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

Performance of highly connected photonic switching lossless metro-access optical networks

NASA Astrophysics Data System (ADS)

Martins, Indayara Bertoldi; Martins, Yara; Barbosa, Felipe Rudge

2018-03-01

The present work analyzes the performance of photonic switching networks, optical packet switching (OPS) and optical burst switching (OBS), in mesh topology of different sizes and configurations. The "lossless" photonic switching node is based on a semiconductor optical amplifier, demonstrated and validated with experimental results on optical power gain, noise figure, and spectral range. The network performance was evaluated through computer simulations based on parameters such as average number of hops, optical packet loss fraction, and optical transport delay (Am). The combination of these elements leads to a consistent account of performance, in terms of network traffic and packet delivery for OPS and OBS metropolitan networks. Results show that a combination of highly connected mesh topologies having an ingress e-buffer present high efficiency and throughput, with very low packet loss and low latency, ensuring fast data delivery to the final receiver.

DEADS: Depth and Energy Aware Dominating Set Based Algorithm for Cooperative Routing along with Sink Mobility in Underwater WSNs.

PubMed

Umar, Amara; Javaid, Nadeem; Ahmad, Ashfaq; Khan, Zahoor Ali; Qasim, Umar; Alrajeh, Nabil; Hayat, Amir

2015-06-18

Performance enhancement of Underwater Wireless Sensor Networks (UWSNs) in terms of throughput maximization, energy conservation and Bit Error Rate (BER) minimization is a potential research area. However, limited available bandwidth, high propagation delay, highly dynamic network topology, and high error probability leads to performance degradation in these networks. In this regard, many cooperative communication protocols have been developed that either investigate the physical layer or the Medium Access Control (MAC) layer, however, the network layer is still unexplored. More specifically, cooperative routing has not yet been jointly considered with sink mobility. Therefore, this paper aims to enhance the network reliability and efficiency via dominating set based cooperative routing and sink mobility. The proposed work is validated via simulations which show relatively improved performance of our proposed work in terms the selected performance metrics.

Demonstration of Cost-Effective, High-Performance Computing at Performance and Reliability Levels Equivalent to a 1994 Vector Supercomputer

NASA Technical Reports Server (NTRS)

Babrauckas, Theresa

2000-01-01

The Affordable High Performance Computing (AHPC) project demonstrated that high-performance computing based on a distributed network of computer workstations is a cost-effective alternative to vector supercomputers for running CPU and memory intensive design and analysis tools. The AHPC project created an integrated system called a Network Supercomputer. By connecting computer work-stations through a network and utilizing the workstations when they are idle, the resulting distributed-workstation environment has the same performance and reliability levels as the Cray C90 vector Supercomputer at less than 25 percent of the C90 cost. In fact, the cost comparison between a Cray C90 Supercomputer and Sun workstations showed that the number of distributed networked workstations equivalent to a C90 costs approximately 8 percent of the C90.

Semi-interpenetrating polymer network for tougher and more microcracking resistant high temperature polymers

NASA Technical Reports Server (NTRS)

Pater, Ruth H. (Inventor)

1992-01-01

This invention is a semi-interpenetrating polymer network which includes a high performance thermosetting polyimide having a nadic end group acting as a crosslinking site and a high performance linear thermoplastic polyimide. An improved high temperature matrix resin is provided which is capable of performing at 316 C in air for several hundreds of hours. This resin has significantly improved toughness and microcracking resistance, excellent processability and mechanical performance, and cost effectiveness.

Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher

2005-08-01

Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.

Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks

PubMed Central

Yamanaka, Ryota; Kitano, Hiroaki

2013-01-01

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. PMID:24278007

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Transport Protocol (Transmission Control Protocol/User Datagram Protocol [TCP/UDP]) Analysis

DTIC Science & Technology

2015-09-01

the network Mac8 Medium Access Control ( Mac ) (Ethernet) address observed as destination for outgoing packets subsessionid8 Zero-based index of...15. SUBJECT TERMS tactical networks, data reduction, high-performance computing, data analysis, big data 16. SECURITY CLASSIFICATION OF: 17...Integer index of row cts_deid Device (instrument) Identifier where observation took place cts_collpt Collection point or logical observation point on

Advanced Performance Modeling with Combined Passive and Active Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dovrolis, Constantine; Sim, Alex

2015-04-15

To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performancemore » information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.« less

FireFly: reconfigurable optical wireless networking data centers

NASA Astrophysics Data System (ADS)

Kavehrad, Mohsen; Deng, Peng; Gupta, H.; Longtin, J.; Das, S. R.; Sekar, V.

2017-01-01

We explore a novel, free-space optics based approach for building data center interconnects. Data centers (DCs) are a critical piece of today's networked applications in both private and public sectors. The key factors that have driven this trend are economies of scale, reduced management costs, better utilization of hardware via statistical multiplexing, and the ability to elastically scale applications in response to changing workload patterns. A robust DC network fabric is fundamental to the success of DCs and to ensure that the network does not become a bottleneck for high-performance applications. In this context, DC network design must satisfy several goals: high performance (e.g., high throughput and low latency), low equipment and management cost, robustness to dynamic traffic patterns, incremental expandability to add new servers or racks, and other practical concerns such as cabling complexity, and power and cooling costs. Current DC network architectures do not seem to provide a satisfactory solution, with respect to the above requirements. In particular, traditional static (wired) networks are either overprovisioned or oversubscribed. Recent works have tried to overcome the above limitations by augmenting a static (wired) "core" with some flexible links (RF-wireless or optical). These augmented architectures show promise, but offer only incremental improvement in performance. Specifically, RFwireless based augmented solutions also offer only limited performance improvement, due to inherent interference and range constraints of RF links. This paper explores an alternative design point—a fully flexible and all-wireless DC interrack network based on free-space optical (FSO) links. We call this FireFly as in; Free-space optical Inter-Rack nEtwork with high FLexibilitY. We will present our designs and tests using various configurations that can help the performance and reliability of the FSO links.

Network issues for large mass storage requirements

NASA Technical Reports Server (NTRS)

Perdue, James

1992-01-01

File Servers and Supercomputing environments need high performance networks to balance the I/O requirements seen in today's demanding computing scenarios. UltraNet is one solution which permits both high aggregate transfer rates and high task-to-task transfer rates as demonstrated in actual tests. UltraNet provides this capability as both a Server-to-Server and Server-to-Client access network giving the supercomputing center the following advantages highest performance Transport Level connections (to 40 MBytes/sec effective rates); matches the throughput of the emerging high performance disk technologies, such as RAID, parallel head transfer devices and software striping; supports standard network and file system applications using SOCKET's based application program interface such as FTP, rcp, rdump, etc.; supports access to the Network File System (NFS) and LARGE aggregate bandwidth for large NFS usage; provides access to a distributed, hierarchical data server capability using DISCOS UniTree product; supports file server solutions available from multiple vendors, including Cray, Convex, Alliant, FPS, IBM, and others.

Scalable software-defined optical networking with high-performance routing and wavelength assignment algorithms.

PubMed

Lee, Chankyun; Cao, Xiaoyuan; Yoshikane, Noboru; Tsuritani, Takehiro; Rhee, June-Koo Kevin

2015-10-19

The feasibility of software-defined optical networking (SDON) for a practical application critically depends on scalability of centralized control performance. The paper, highly scalable routing and wavelength assignment (RWA) algorithms are investigated on an OpenFlow-based SDON testbed for proof-of-concept demonstration. Efficient RWA algorithms are proposed to achieve high performance in achieving network capacity with reduced computation cost, which is a significant attribute in a scalable centralized-control SDON. The proposed heuristic RWA algorithms differ in the orders of request processes and in the procedures of routing table updates. Combined in a shortest-path-based routing algorithm, a hottest-request-first processing policy that considers demand intensity and end-to-end distance information offers both the highest throughput of networks and acceptable computation scalability. We further investigate trade-off relationship between network throughput and computation complexity in routing table update procedure by a simulation study.

Distributed Large Data-Object Environments: End-to-End Performance Analysis of High Speed Distributed Storage Systems in Wide Area ATM Networks

NASA Technical Reports Server (NTRS)

Johnston, William; Tierney, Brian; Lee, Jason; Hoo, Gary; Thompson, Mary

1996-01-01

We have developed and deployed a distributed-parallel storage system (DPSS) in several high speed asynchronous transfer mode (ATM) wide area networks (WAN) testbeds to support several different types of data-intensive applications. Architecturally, the DPSS is a network striped disk array, but is fairly unique in that its implementation allows applications complete freedom to determine optimal data layout, replication and/or coding redundancy strategy, security policy, and dynamic reconfiguration. In conjunction with the DPSS, we have developed a 'top-to-bottom, end-to-end' performance monitoring and analysis methodology that has allowed us to characterize all aspects of the DPSS operating in high speed ATM networks. In particular, we have run a variety of performance monitoring experiments involving the DPSS in the MAGIC testbed, which is a large scale, high speed, ATM network and we describe our experience using the monitoring methodology to identify and correct problems that limit the performance of high speed distributed applications. Finally, the DPSS is part of an overall architecture for using high speed, WAN's for enabling the routine, location independent use of large data-objects. Since this is part of the motivation for a distributed storage system, we describe this architecture.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonachea, Dan; Hargrove, P.

GASNet is a language-independent, low-level networking layer that provides network-independent, high-performance communication primitives tailored for implementing parallel global address space SPMD languages and libraries such as UPC, UPC++, Co-Array Fortran, Legion, Chapel, and many others. The interface is primarily intended as a compilation target and for use by runtime library writers (as opposed to end users), and the primary goals are high performance, interface portability, and expressiveness. GASNet stands for "Global-Address Space Networking".

DOE Office of Scientific and Technical Information (OSTI.GOV)

Welcome, Michael L.; Bell, Christian S.

GASNet (Global-Address Space Networking) is a language-independent, low-level networking layer that provides network-independent, high-performance communication primitives tailored for implementing parallel global address space SPMD languages such as UPC and Titanium. The interface is primarily intended as a compilation target and for use by runtime library writers (as opposed to end users), and the primary goals are high performance, interface portability, and expressiveness. GASNet is designed specifically to support high-performance, portable implementations of global address space languages on modern high-end communication networks. The interface provides the flexibility and extensibility required to express a wide variety of communication patterns without sacrificing performancemore » by imposing large computational overheads in the interface. The design of the GASNet interface is partitioned into two layers to maximize porting ease without sacrificing performance: the lower level is a narrow but very general interface called the GASNet core API - the design is basedheavily on Active Messages, and is implemented directly on top of each individual network architecture. The upper level is a wider and more expressive interface called GASNet extended API, which provides high-level operations such as remote memory access and various collective operations. This release implements GASNet over MPI, the Quadrics "elan" API, the Myrinet "GM" API and the "LAPI" interface to the IBM SP switch. A template is provided for adding support for additional network interfaces.« less

Medical applications for high-performance computers in SKIF-GRID network.

PubMed

Zhuchkov, Alexey; Tverdokhlebov, Nikolay

2009-01-01

The paper presents a set of software services for massive mammography image processing by using high-performance parallel computers of SKIF-family which are linked into a service-oriented grid-network. An experience of a prototype system implementation in two medical institutions is also described.

Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI.

PubMed

Magnuson, Matthew Evan; Thompson, Garth John; Schwarb, Hillary; Pan, Wen-Ju; McKinley, Andy; Schumacher, Eric H; Keilholz, Shella Dawn

2015-12-01

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

Diversity Performance Analysis on Multiple HAP Networks.

PubMed

Dong, Feihong; Li, Min; Gong, Xiangwu; Li, Hongjun; Gao, Fengyue

2015-06-30

One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.

Understanding Charge Transport in Mixed Networks of Semiconducting Carbon Nanotubes

PubMed Central

2016-01-01

The ability to select and enrich semiconducting single-walled carbon nanotubes (SWNT) with high purity has led to a fast rise of solution-processed nanotube network field-effect transistors (FETs) with high carrier mobilities and on/off current ratios. However, it remains an open question whether it is best to use a network of only one nanotube species (monochiral) or whether a mix of purely semiconducting nanotubes but with different bandgaps is sufficient for high performance FETs. For a range of different polymer-sorted semiconducting SWNT networks, we demonstrate that a very small amount of narrow bandgap nanotubes within a dense network of large bandgap nanotubes can dominate the transport and thus severely limit on-currents and effective carrier mobility. Using gate-voltage-dependent electroluminescence, we spatially and spectrally reveal preferential charge transport that does not depend on nominal network density but on the energy level distribution within the network and carrier density. On the basis of these results, we outline rational guidelines for the use of mixed SWNT networks to obtain high performance FETs while reducing the cost for purification. PMID:26867006

Sampling Approaches for Multi-Domain Internet Performance Measurement Infrastructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Calyam, Prasad

2014-09-15

The next-generation of high-performance networks being developed in DOE communities are critical for supporting current and emerging data-intensive science applications. The goal of this project is to investigate multi-domain network status sampling techniques and tools to measure/analyze performance, and thereby provide “network awareness” to end-users and network operators in DOE communities. We leverage the infrastructure and datasets available through perfSONAR, which is a multi-domain measurement framework that has been widely deployed in high-performance computing and networking communities; the DOE community is a core developer and the largest adopter of perfSONAR. Our investigations include development of semantic scheduling algorithms, measurement federationmore » policies, and tools to sample multi-domain and multi-layer network status within perfSONAR deployments. We validate our algorithms and policies with end-to-end measurement analysis tools for various monitoring objectives such as network weather forecasting, anomaly detection, and fault-diagnosis. In addition, we develop a multi-domain architecture for an enterprise-specific perfSONAR deployment that can implement monitoring-objective based sampling and that adheres to any domain-specific measurement policies.« less

Performance Evaluation of Communication Software Systems for Distributed Computing

NASA Technical Reports Server (NTRS)

Fatoohi, Rod

1996-01-01

In recent years there has been an increasing interest in object-oriented distributed computing since it is better quipped to deal with complex systems while providing extensibility, maintainability, and reusability. At the same time, several new high-speed network technologies have emerged for local and wide area networks. However, the performance of networking software is not improving as fast as the networking hardware and the workstation microprocessors. This paper gives an overview and evaluates the performance of the Common Object Request Broker Architecture (CORBA) standard in a distributed computing environment at NASA Ames Research Center. The environment consists of two testbeds of SGI workstations connected by four networks: Ethernet, FDDI, HiPPI, and ATM. The performance results for three communication software systems are presented, analyzed and compared. These systems are: BSD socket programming interface, IONA's Orbix, an implementation of the CORBA specification, and the PVM message passing library. The results show that high-level communication interfaces, such as CORBA and PVM, can achieve reasonable performance under certain conditions.

High Performance Computing and Networking for Science--Background Paper.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. Office of Technology Assessment.

The Office of Technology Assessment is conducting an assessment of the effects of new information technologies--including high performance computing, data networking, and mass data archiving--on research and development. This paper offers a view of the issues and their implications for current discussions about Federal supercomputer initiatives…

HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yan

Identifying traffic anomalies and attacks rapidly and accurately is critical for large network operators. With the rapid growth of network bandwidth, such as the next generation DOE UltraScience Network, and fast emergence of new attacks/virus/worms, existing network intrusion detection systems (IDS) are insufficient because they: • Are mostly host-based and not scalable to high-performance networks; • Are mostly signature-based and unable to adaptively recognize flow-level unknown attacks; • Cannot differentiate malicious events from the unintentional anomalies. To address these challenges, we proposed and developed a new paradigm called high-performance network anomaly/intrustion detection and mitigation (HPNAIDM) system. The new paradigm ismore » significantly different from existing IDSes with the following features (research thrusts). • Online traffic recording and analysis on high-speed networks; • Online adaptive flow-level anomaly/intrusion detection and mitigation; • Integrated approach for false positive reduction. Our research prototype and evaluation demonstrate that the HPNAIDM system is highly effective and economically feasible. Beyond satisfying the pre-set goals, we even exceed that significantly (see more details in the next section). Overall, our project harvested 23 publications (2 book chapters, 6 journal papers and 15 peer-reviewed conference/workshop papers). Besides, we built a website for technique dissemination, which hosts two system prototype release to the research community. We also filed a patent application and developed strong international and domestic collaborations which span both academia and industry.« less

Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration

PubMed Central

Cole, Michael W.

2016-01-01

The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability. SIGNIFICANCE STATEMENT The brain's network configuration varies based on current task demands. For example, functional brain connections are organized in one way when one is resting quietly but in another way if one is asked to make a decision. We found that the efficiency of these updates in brain network organization is positively related to general intelligence, the ability to perform a wide variety of cognitively challenging tasks well. Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence. PMID:27535904

Probabilistic Assessment of High-Throughput Wireless Sensor Networks

PubMed Central

Kim, Robin E.; Mechitov, Kirill; Sim, Sung-Han; Spencer, Billie F.; Song, Junho

2016-01-01

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved. PMID:27258270

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

High-Throughput and Low-Latency Network Communication with NetIO

NASA Astrophysics Data System (ADS)

Schumacher, Jörn; Plessl, Christian; Vandelli, Wainer

2017-10-01

HPC network technologies like Infiniband, TrueScale or OmniPath provide low- latency and high-throughput communication between hosts, which makes them attractive options for data-acquisition systems in large-scale high-energy physics experiments. Like HPC networks, DAQ networks are local and include a well specified number of systems. Unfortunately traditional network communication APIs for HPC clusters like MPI or PGAS exclusively target the HPC community and are not suited well for DAQ applications. It is possible to build distributed DAQ applications using low-level system APIs like Infiniband Verbs, but it requires a non-negligible effort and expert knowledge. At the same time, message services like ZeroMQ have gained popularity in the HEP community. They make it possible to build distributed applications with a high-level approach and provide good performance. Unfortunately, their usage usually limits developers to TCP/IP- based networks. While it is possible to operate a TCP/IP stack on top of Infiniband and OmniPath, this approach may not be very efficient compared to a direct use of native APIs. NetIO is a simple, novel asynchronous message service that can operate on Ethernet, Infiniband and similar network fabrics. In this paper the design and implementation of NetIO is presented and described, and its use is evaluated in comparison to other approaches. NetIO supports different high-level programming models and typical workloads of HEP applications. The ATLAS FELIX project [1] successfully uses NetIO as its central communication platform. The architecture of NetIO is described in this paper, including the user-level API and the internal data-flow design. The paper includes a performance evaluation of NetIO including throughput and latency measurements. The performance is compared against the state-of-the- art ZeroMQ message service. Performance measurements are performed in a lab environment with Ethernet and FDR Infiniband networks.

Ngi and Internet2: accelerating the creation of tomorrow's internet.

PubMed

Kratz, M; Ackerman, M; Hanss, T; Corbato, S

2001-01-01

Internet2 is a consortium of leading U.S. universities working in partnership with industry and the U.S. government's Next Generation Internet (NGI) initiative to develop a faster, more reliable Internet for research and education including enhanced, high-performance networking services and the advanced applications that are enabled by those services [1]. By facilitating and coordinating the development, deployment, operation, and technology transfer of advanced, network-based applications and network services, Internet2 and NGI are working together to fundamentally change the way scientists, engineers, clinicians, and others work together. [http://www.internet2.edu] The NGI Program has three tracks: research, network testbeds, and applications. The aim of the research track is to promote experimentation with the next generation of network technologies. The network testbed track aims to develop next generation network testbeds to connect universities and federal research institutions at speeds that are sufficient to demonstrate new technologies and support future research. The aim of the applications track is to demonstrate new applications, enabled by the NGI networks, to meet important national goals and missions [2]. [http://www.ngi.gov/] The Internet2/NGI backbone networks, Abilene and vBNS (very high performance Backbone Network Service), provide the basis of collaboration and development for a new breed of advanced medical applications. Academic medical centers leverage the resources available throughout the Internet2 high-performance networking community for high-capacity broadband and selectable quality of service to make effective use of national repositories. The Internet2 Health Sciences Initiative enables a new generation of emerging medical applications whose architecture and development have been restricted by or are beyond the constraints of traditional Internet environments. These initiatives facilitate a variety of activities to foster the development and deployment of emerging applications that meet the requirements of clinical practice, medical and related biological research, education, and medical awareness throughout the public sector. Medical applications that work with high performance networks and supercomputing capabilities offer exciting new solutions for the medical industry. Internet2 and NGI,strive to combine the expertise of their constituents to establish a distributed knowledge system for achieving innovation in research, teaching, learning, and clinical care.

Performance Evaluation of FAST TCP Traffic-Flows in Multihomed MANETs

NASA Astrophysics Data System (ADS)

Mudassir, Mumajjed Ul; Akram, Adeel

In Mobile Ad hoc Networks (MANETs) an efficient communication protocol is required at the transport layer. Mobile nodes moving around will have temporary and rather short-lived connectivity with each other and the Internet, thus requiring efficient utilization of network resources. Moreover the problems arising due to high mobility, collision and congestion must also be considered. Multihoming allows higher reliability and enhancement of network throughput. FAST TCP is a new promising transport layer protocol developed for high-speed high-latency networks. In this paper, we have analyzed the performance of FAST TCP traffic flows in multihomed MANETs and compared it with standard TCP (TCP Reno) traffic flows in non-multihomed MANETs.

Network protocols for real-time applications

NASA Technical Reports Server (NTRS)

Johnson, Marjory J.

1987-01-01

The Fiber Distributed Data Interface (FDDI) and the SAE AE-9B High Speed Ring Bus (HSRB) are emerging standards for high-performance token ring local area networks. FDDI was designed to be a general-purpose high-performance network. HSRB was designed specifically for military real-time applications. A workshop was conducted at NASA Ames Research Center in January, 1987 to compare and contrast these protocols with respect to their ability to support real-time applications. This report summarizes workshop presentations and includes an independent comparison of the two protocols. A conclusion reached at the workshop was that current protocols for the upper layers of the Open Systems Interconnection (OSI) network model are inadequate for real-time applications.

Devices and circuits for nanoelectronic implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

Turel, Ozgur

Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

Synthesis of carbon nanotube (CNT)-entangled CuO nanotube networks via CNT-catalytic growth and in situ thermal oxidation as additive-free anodes for lithium ion batteries

NASA Astrophysics Data System (ADS)

Cui, Xia; Song, Bo; Cheng, Shisu; Xie, Yun; Shao, Yijiang; Sun, Yueming

2018-01-01

We demonstrated the utility of carbon nanotubes (CNTs) as a catalyst and conductive agent to synthesize CNT-entangled copper nanowire (CuNW-CNT) networks within a melted mixture of hexadecylamine and cetyltrimethy ammounium bromide. The CuNW-CNT networks were further in situ thermally oxidized into CuO nanotube-CNT (CuONT-CNT) with the high retention of network structure. The binder- and conducting-additive-free anodes constructed using the CuONT-CNT networks exhibited high performance, such as high capability (557.7 mAh g-1 at 0.2 °C after 200 cycles), high Coulombic efficiency (near 100%), good rate performance (385.5 mAh g-1 at 5 °C and 310.3 mAh g-1 at 10 °C), and long cycling life.

SCinet Architecture: Featured at the International Conference for High Performance Computing,Networking, Storage and Analysis 2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lyonnais, Marc; Smith, Matt; Mace, Kate P.

SCinet is the purpose-built network that operates during the International Conference for High Performance Computing,Networking, Storage and Analysis (Super Computing or SC). Created each year for the conference, SCinet brings to life a high-capacity network that supports applications and experiments that are a hallmark of the SC conference. The network links the convention center to research and commercial networks around the world. This resource serves as a platform for exhibitors to demonstrate the advanced computing resources of their home institutions and elsewhere by supporting a wide variety of applications. Volunteers from academia, government and industry work together to design andmore » deliver the SCinet infrastructure. Industry vendors and carriers donate millions of dollars in equipment and services needed to build and support the local and wide area networks. Planning begins more than a year in advance of each SC conference and culminates in a high intensity installation in the days leading up to the conference. The SCinet architecture for SC16 illustrates a dramatic increase in participation from the vendor community, particularly those that focus on network equipment. Software-Defined Networking (SDN) and Data Center Networking (DCN) are present in nearly all aspects of the design.« less

High-Performance Satellite/Terrestrial-Network Gateway

NASA Technical Reports Server (NTRS)

Beering, David R.

2005-01-01

A gateway has been developed to enable digital communication between (1) the high-rate receiving equipment at NASA's White Sands complex and (2) a standard terrestrial digital communication network at data rates up to 622 Mb/s. The design of this gateway can also be adapted for use in commercial Earth/satellite and digital communication networks, and in terrestrial digital communication networks that include wireless subnetworks. Gateway as used here signifies an electronic circuit that serves as an interface between two electronic communication networks so that a computer (or other terminal) on one network can communicate with a terminal on the other network. The connection between this gateway and the high-rate receiving equipment is made via a synchronous serial data interface at the emitter-coupled-logic (ECL) level. The connection between this gateway and a standard asynchronous transfer mode (ATM) terrestrial communication network is made via a standard user network interface with a synchronous optical network (SONET) connector. The gateway contains circuitry that performs the conversion between the ECL and SONET interfaces. The data rate of the SONET interface can be either 155.52 or 622.08 Mb/s. The gateway derives its clock signal from a satellite modem in the high-rate receiving equipment and, hence, is agile in the sense that it adapts to the data rate of the serial interface.

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

PubMed

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, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Enabling parallel simulation of large-scale HPC network systems

DOE PAGES

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; ...

2016-04-07

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Enabling parallel simulation of large-scale HPC network systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Livermore Big Artificial Neural Network Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Essen, Brian Van; Jacobs, Sam; Kim, Hyojin

2016-07-01

LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

Diversity Performance Analysis on Multiple HAP Networks

PubMed Central

Dong, Feihong; Li, Min; Gong, Xiangwu; Li, Hongjun; Gao, Fengyue

2015-01-01

One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques. PMID:26134102

National research and education network

NASA Technical Reports Server (NTRS)

Villasenor, Tony

1991-01-01

Some goals of this network are as follows: Extend U.S. technological leadership in high performance computing and computer communications; Provide wide dissemination and application of the technologies both to the speed and the pace of innovation and to serve the national economy, national security, education, and the global environment; and Spur gains in the U.S. productivity and industrial competitiveness by making high performance computing and networking technologies an integral part of the design and production process. Strategies for achieving these goals are as follows: Support solutions to important scientific and technical challenges through a vigorous R and D effort; Reduce the uncertainties to industry for R and D and use of this technology through increased cooperation between government, industry, and universities and by the continued use of government and government funded facilities as a prototype user for early commercial HPCC products; and Support underlying research, network, and computational infrastructures on which U.S. high performance computing technology is based.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.

PubMed

Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku; Zhang, Wei; Kreisberg, Jason F; Tamayo, Pablo; Ideker, Trey

2018-04-25

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiplex network analysis of employee performance and employee social relationships

NASA Astrophysics Data System (ADS)

Cai, Meng; Wang, Wei; Cui, Ying; Stanley, H. Eugene

2018-01-01

In human resource management, employee performance is strongly affected by both formal and informal employee networks. Most previous research on employee performance has focused on monolayer networks that can represent only single categories of employee social relationships. We study employee performance by taking into account the entire multiplex structure of underlying employee social networks. We collect three datasets consisting of five different employee relationship categories in three firms, and predict employee performance using degree centrality and eigenvector centrality in a superimposed multiplex network (SMN) and an unfolded multiplex network (UMN). We use a quadratic assignment procedure (QAP) analysis and a regression analysis to demonstrate that the different categories of relationship are mutually embedded and that the strength of their impact on employee performance differs. We also use weighted/unweighted SMN/UMN to measure the predictive accuracy of this approach and find that employees with high centrality in a weighted UMN are more likely to perform well. Our results shed new light on how social structures affect employee performance.

High Speed All-Optical Data Distribution Network

NASA Astrophysics Data System (ADS)

Braun, Steve; Hodara, Henri

2017-11-01

This article describes the performance and capabilities of an all-optical network featuring low latency, high speed file transfer between serially connected optical nodes. A basic component of the network is a network interface card (NIC) implemented through a unique planar lightwave circuit (PLC) that performs add/drop data and optical signal amplification. The network uses a linear bus topology with nodes in a "T" configuration, as described in the text. The signal is sent optically (hence, no latency) to all nodes via wavelength division multiplexing (WDM), with each node receiver tuned to wavelength of choice via an optical de-multiplexer. Each "T" node routes a portion of the signal to/from the bus through optical couplers, embedded in the network interface card (NIC), to each of the 1 through n computers.

A robust low-rate coding scheme for packet video

NASA Technical Reports Server (NTRS)

Chen, Y. C.; Sayood, Khalid; Nelson, D. J.; Arikan, E. (Editor)

1991-01-01

Due to the rapidly evolving field of image processing and networking, video information promises to be an important part of telecommunication systems. Although up to now video transmission has been transported mainly over circuit-switched networks, it is likely that packet-switched networks will dominate the communication world in the near future. Asynchronous transfer mode (ATM) techniques in broadband-ISDN can provide a flexible, independent and high performance environment for video communication. For this paper, the network simulator was used only as a channel in this simulation. Mixture blocking coding with progressive transmission (MBCPT) has been investigated for use over packet networks and has been found to provide high compression rate with good visual performance, robustness to packet loss, tractable integration with network mechanics and simplicity in parallel implementation.

XUNET experimental high-speed network testbed CRADA 1136, DOE TTI No. 92-MULT-020-B2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Palmer, R.E.

1996-04-01

XUNET is a research program with AT&T and other partners to study high-speed wide area communication between local area networks over a backbone using Asynchronous Transfer Mode (ATM) switches. Important goals of the project are to develop software techniques for network control and management, and applications for high-speed networks. The project entails building a testbed between member sites to explore performance issues for mixed network traffic such as congestion control, multimedia communications protocols, segmentation and reassembly of ATM cells, and overall data throughput rates.

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

PubMed Central

Azad, Ariful; Ouzounis, Christos A; Kyrpides, Nikos C; Buluç, Aydin

2018-01-01

Abstract Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license. PMID:29315405

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

DOE PAGES

Azad, Ariful; Pavlopoulos, Georgios A.; Ouzounis, Christos A.; ...

2018-01-05

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times andmore » memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.« less

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azad, Ariful; Pavlopoulos, Georgios A.; Ouzounis, Christos A.

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times andmore » memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.« less

The Influence of Social Networks on High School Students' Performance

ERIC Educational Resources Information Center

Abu-Shanab, Emad; Al-Tarawneh, Heyam

2015-01-01

Social networks are becoming an integral part of people's lives. Students are spending much time on social media and are considered the largest category that uses such application. This study tries to explore the influence of social media use, and especially Facebook, on high school students' performance. The study used the GPA of students in four…

A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

NASA Technical Reports Server (NTRS)

Gupta, Pramod; Schumann, Johann

2004-01-01

High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

Silicon photonics for high-performance interconnection networks

NASA Astrophysics Data System (ADS)

Biberman, Aleksandr

2011-12-01

We assert in the course of this work that silicon photonics has the potential to be a key disruptive technology in computing and communication industries. The enduring pursuit of performance gains in computing, combined with stringent power constraints, has fostered the ever-growing computational parallelism associated with chip multiprocessors, memory systems, high-performance computing systems, and data centers. Sustaining these parallelism growths introduces unique challenges for on- and off-chip communications, shifting the focus toward novel and fundamentally different communication approaches. This work showcases that chip-scale photonic interconnection networks, enabled by high-performance silicon photonic devices, enable unprecedented bandwidth scalability with reduced power consumption. We demonstrate that the silicon photonic platforms have already produced all the high-performance photonic devices required to realize these types of networks. Through extensive empirical characterization in much of this work, we demonstrate such feasibility of waveguides, modulators, switches, and photodetectors. We also demonstrate systems that simultaneously combine many functionalities to achieve more complex building blocks. Furthermore, we leverage the unique properties of available silicon photonic materials to create novel silicon photonic devices, subsystems, network topologies, and architectures to enable unprecedented performance of these photonic interconnection networks and computing systems. We show that the advantages of photonic interconnection networks extend far beyond the chip, offering advanced communication environments for memory systems, high-performance computing systems, and data centers. Furthermore, we explore the immense potential of all-optical functionalities implemented using parametric processing in the silicon platform, demonstrating unique methods that have the ability to revolutionize computation and communication. Silicon photonics enables new sets of opportunities that we can leverage for performance gains, as well as new sets of challenges that we must solve. Leveraging its inherent compatibility with standard fabrication techniques of the semiconductor industry, combined with its capability of dense integration with advanced microelectronics, silicon photonics also offers a clear path toward commercialization through low-cost mass-volume production. Combining empirical validations of feasibility, demonstrations of massive performance gains in large-scale systems, and the potential for commercial penetration of silicon photonics, the impact of this work will become evident in the many decades that follow.

Mesoporous Li4Ti5O12 nanoclusters anchored on super-aligned carbon nanotubes as high performance electrodes for lithium ion batteries.

PubMed

Sun, Li; Kong, Weibang; Wu, Hengcai; Wu, Yang; Wang, Datao; Zhao, Fei; Jiang, Kaili; Li, Qunqing; Wang, Jiaping; Fan, Shoushan

2016-01-07

Mesoporous lithium titanate (LTO) nanoclusters are in situ synthesized in a network of super aligned carbon nanotubes (SACNTs) via a solution-based method followed by heat treatment in air. In the LTO-CNT composite, SACNTs not only serve as the skeleton to support a binder-free electrode, but also render the composite with high conductivity, flexibility, and mechanical strength. The homogeneously dispersed LTO nanoclusters among the SACNTs allow each LTO grain to effectively access the electrolyte and the conductive network, benefiting both ion and electron transport. By the incorporation of LTO into the CNT network, mechanical reinforcement is also achieved. When serving as a negative electrode for lithium ion batteries, such a robust composite-network architecture provides the electrodes with effective charge transport and structural integrity, leading to high-performance flexible electrodes with high capacity, high rate capability, and excellent cycling stability.

Influence maximization based on partial network structure information: A comparative analysis on seed selection heuristics

NASA Astrophysics Data System (ADS)

Erkol, Şirag; Yücel, Gönenç

In this study, the problem of seed selection is investigated. This problem is mainly treated as an optimization problem, which is proved to be NP-hard. There are several heuristic approaches in the literature which mostly use algorithmic heuristics. These approaches mainly focus on the trade-off between computational complexity and accuracy. Although the accuracy of algorithmic heuristics are high, they also have high computational complexity. Furthermore, in the literature, it is generally assumed that complete information on the structure and features of a network is available, which is not the case in most of the times. For the study, a simulation model is constructed, which is capable of creating networks, performing seed selection heuristics, and simulating diffusion models. Novel metric-based seed selection heuristics that rely only on partial information are proposed and tested using the simulation model. These heuristics use local information available from nodes in the synthetically created networks. The performances of heuristics are comparatively analyzed on three different network types. The results clearly show that the performance of a heuristic depends on the structure of a network. A heuristic to be used should be selected after investigating the properties of the network at hand. More importantly, the approach of partial information provided promising results. In certain cases, selection heuristics that rely only on partial network information perform very close to similar heuristics that require complete network data.

A Highly Flexible and Efficient Passive Optical Network Employing Dynamic Wavelength Allocation

NASA Astrophysics Data System (ADS)

Hsueh, Yu-Li; Rogge, Matthew S.; Yamamoto, Shu; Kazovsky, Leonid G.

2005-01-01

A novel and high-performance passive optical network (PON), the SUCCESS-DWA PON, employs dynamic wavelength allocation to provide bandwidth sharing across multiple physical PONs. In the downstream, tunable lasers, an arrayed waveguide grating, and coarse/fine filtering combine to create a flexible new optical access solution. In the upstream, several distributed and centralized schemes are proposed and investigated. The network performance is compared to conventional TDM-PONs under different traffic models, including the self-similar traffic model and the transaction-oriented model. Broadcast support and deployment issues are addressed. The network's excellent scalability can bridge the gap between conventional TDM-PONs and WDM-PONs. The powerful architecture is a promising candidate for next generation optical access networks.

A tough performance simultaneous semi-interpenetrating polymer network

NASA Technical Reports Server (NTRS)

Pater, Ruth H. (Inventor)

1989-01-01

A semi-interpenetrating polyimide (semi-IPN) network and methods for making and using the same are disclosed. The semi-IPN system comprises a high performance thermosetting polyimide having an acetylene-terminated group acting as a crosslinking site and a high performance linear thermoplastic polyimide. The polymer is made by combining low viscosity precursors and low molecular weight polymers of the thermosetting and thermoplastic polyimides and allowing them to react in the immediate presence of each other to form a simultaneous semi-interpenetrating polyimide network. Provided is a high temperature system having significantly improved processability and damage tolerance while maintaining excellent thermo-oxidative stability, mechanical properties and resistance to humidity, when compared with the commercial high temperature resin, Thermid 600. This material is particularly adapted for use as a molding, adhesive and advanced composite matrix for aerospace structural and electronic applications.

Rapid identifying high-influence nodes in complex networks

NASA Astrophysics Data System (ADS)

Song, Bo; Jiang, Guo-Ping; Song, Yu-Rong; Xia, Ling-Ling

2015-10-01

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method (RIM) to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered (SIR) model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness, and eigenvector centrality methods. Project supported by the National Natural Science Foundation of China (Grant Nos. 61374180 and 61373136), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project, China (Grant No. 12YJAZH120), and the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. RLD201212).

On Adding Structure to Unstructured Overlay Networks

NASA Astrophysics Data System (ADS)

Leitão, João; Carvalho, Nuno A.; Pereira, José; Oliveira, Rui; Rodrigues, Luís

Unstructured peer-to-peer overlay networks are very resilient to churn and topology changes, while requiring little maintenance cost. Therefore, they are an infrastructure to build highly scalable large-scale services in dynamic networks. Typically, the overlay topology is defined by a peer sampling service that aims at maintaining, in each process, a random partial view of peers in the system. The resulting random unstructured topology is suboptimal when a specific performance metric is considered. On the other hand, structured approaches (for instance, a spanning tree) may optimize a given target performance metric but are highly fragile. In fact, the cost for maintaining structures with strong constraints may easily become prohibitive in highly dynamic networks. This chapter discusses different techniques that aim at combining the advantages of unstructured and structured networks. Namely we focus on two distinct approaches, one based on optimizing the overlay and another based on optimizing the gossip mechanism itself.

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

PubMed

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Reproducible, high performance patch antenna array apparatus and method of fabrication

DOEpatents

Strassner, II, Bernd H.

2007-01-23

A reproducible, high-performance patch antenna array apparatus includes a patch antenna array provided on a unitary dielectric substrate, and a feed network provided on the same unitary substrate and proximity coupled to the patch antenna array. The reproducibility is enhanced by using photolithographic patterning and etching to produce both the patch antenna array and the feed network.

Composition and Realization of Source-to-Sink High-Performance Flows: File Systems, Storage, Hosts, LAN and WAN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase Qishi

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. To support such capabilities, significant progress has been made in various components including the deployment of 100 Gbps networks with future 1 Tbps bandwidth, increases in end-host capabilities with multiple cores and buses, capacity improvements in large disk arrays, and deployment of parallel file systems such as Lustre and GPFS. High-performance source-to-sink datamore » flows must be composed of these component systems, which requires significant optimizations of the storage-to-host data and execution paths to match the edge and long-haul network connections. In particular, end systems are currently supported by 10-40 Gbps Network Interface Cards (NIC) and 8-32 Gbps storage Host Channel Adapters (HCAs), which carry the individual flows that collectively must reach network speeds of 100 Gbps and higher. Indeed, such data flows must be synthesized using multicore, multibus hosts connected to high-performance storage systems on one side and to the network on the other side. Current experimental results show that the constituent flows must be optimally composed and preserved from storage systems, across the hosts and the networks with minimal interference. Furthermore, such a capability must be made available transparently to the science users without placing undue demands on them to account for the details of underlying systems and networks. And, this task is expected to become even more complex in the future due to the increasing sophistication of hosts, storage systems, and networks that constitute the high-performance flows. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align and transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections. These solutions will be tested using (1) 100 Gbps connection(s) between Oak Ridge National Laboratory (ORNL) and Argonne National Laboratory (ANL) with storage systems supported by Lustre and GPFS file systems with an asymmetric connection to University of Memphis (UM); (2) ORNL testbed with multicore and multibus hosts, switches with OpenFlow capabilities, and network emulators; and (3) 100 Gbps connections from ESnet and their Openflow testbed, and other experimental connections. This proposal brings together the expertise and facilities of the two national laboratories, ORNL and ANL, and UM. It also represents a collaboration between DOE and the Department of Defense (DOD) projects at ORNL by sharing technical expertise and personnel costs, and leveraging the existing DOD Extreme Scale Systems Center (ESSC) facilities at ORNL.« less

H.R. 1757--High Performance Computing and High Speed Networking Applications Act of 1993. Hearings before the Subcommittee on Science of the Committee on Science, Space, and Technology. House of Representatives, One Hundred Third Congress, First Session (April 27, May 6, May 11, 1993).

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. House Committee on Science, Space and Technology.

This document contains the transcript of three hearings on the High Speed Performance Computing and High Speed Networking Applications Act of 1993 (H.R. 1757). The hearings were designed to obtain specific suggestions for improvements to the legislation and alternative or additional application areas that should be pursued. Testimony and prepared…

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

SINET3: advanced optical and IP hybrid network

NASA Astrophysics Data System (ADS)

Urushidani, Shigeo

2007-11-01

This paper introduces the new Japanese academic backbone network called SINET3, which has been in full-scale operation since June 2007. SINET3 provides a wide variety of network services, such as multi-layer transfer, enriched VPN, enhanced QoS, and layer-1 bandwidth on demand (BoD) services to create an innovative and prolific science infrastructure for more than 700 universities and research institutions. The network applies an advanced hybrid network architecture composed of 75 layer-1 switches and 12 high-performance IP routers to accommodate such diversified services in a single network platform, and provides sufficient bandwidth using Japan's first STM256 (40 Gbps) lines. The network adopts lots of the latest networking technologies, such as next-generation SDH (VCAT/GFP/LCAS), GMPLS, advanced MPLS, and logical-router technologies, for high network convergence, flexible resource assignment, and high service availability. This paper covers the network services, network design, and networking technologies of SINET3.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Compressive sensing of high betweenness centrality nodes in networks

NASA Astrophysics Data System (ADS)

Mahyar, Hamidreza; Hasheminezhad, Rouzbeh; Ghalebi K., Elahe; Nazemian, Ali; Grosu, Radu; Movaghar, Ali; Rabiee, Hamid R.

2018-05-01

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top- k betweenness centrality nodes in networks, using compressive sensing. CS-HiBet can perform as a distributed algorithm by using only the local information at each node. Hence, it is applicable to large real-world and unknown networks in which the global approaches are usually unrealizable. The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that CS-HiBet outperforms the best existing methods with notable improvements.

Cross-layer restoration with software defined networking based on IP over optical transport networks

NASA Astrophysics Data System (ADS)

Yang, Hui; Cheng, Lei; Deng, Junni; Zhao, Yongli; Zhang, Jie; Lee, Young

2015-10-01

The IP over optical transport network is a very promising networking architecture applied to the interconnection of geographically distributed data centers due to the performance guarantee of low delay, huge bandwidth and high reliability at a low cost. It can enable efficient resource utilization and support heterogeneous bandwidth demands in highly-available, cost-effective and energy-effective manner. In case of cross-layer link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In this paper, we propose a novel cross-layer restoration scheme for data center services with software defined networking based on IP over optical network. The cross-layer restoration scheme can enable joint optimization of IP network and optical network resources, and enhance the data center service restoration responsiveness to the dynamic end-to-end service demands. We quantitatively evaluate the feasibility and performances through the simulation under heavy traffic load scenario in terms of path blocking probability and path restoration latency. Numeric results show that the cross-layer restoration scheme improves the recovery success rate and minimizes the overall recovery time.

Asynchronous transfer mode link performance over ground networks

NASA Technical Reports Server (NTRS)

Chow, E. T.; Markley, R. W.

1993-01-01

The results of an experiment to determine the feasibility of using asynchronous transfer mode (ATM) technology to support advanced spacecraft missions that require high-rate ground communications and, in particular, full-motion video are reported. Potential nodes in such a ground network include Deep Space Network (DSN) antenna stations, the Jet Propulsion Laboratory, and a set of national and international end users. The experiment simulated a lunar microrover, lunar lander, the DSN ground communications system, and distributed science users. The users were equipped with video-capable workstations. A key feature was an optical fiber link between two high-performance workstations equipped with ATM interfaces. Video was also transmitted through JPL's institutional network to a user 8 km from the experiment. Variations in video depending on the networks and computers were observed, the results are reported.

A highly crystalline single Au wire network as a high temperature transparent heater

NASA Astrophysics Data System (ADS)

Rao, K. D. M.; Kulkarni, Giridhar U.

2014-05-01

A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. Electronic supplementary information (ESI) available: Optical micrographs, EDAX, XRD, SEM and TEM images of Au metal wires. See DOI: 10.1039/c4nr00869c

Circuit-Switched Memory Access in Photonic Interconnection Networks for High-Performance Embedded Computing

DTIC Science & Technology

2010-07-22

dependent , providing a natural bandwidth match between compute cores and the memory subsystem. • High Bandwidth Dcnsity. Waveguides crossing the chip...simulate this memory access architecture on a 2S6-core chip with a concentrated 64-node network lIsing detailed traces of high-performance embedded...memory modulcs, wc placc memory access poi nts (MAPs) around the pcriphery of the chip connected to thc nctwork. These MAPs, shown in Figure 4, contain

Performance analysis of SS7 congestion controls under sustained overload

NASA Astrophysics Data System (ADS)

Manfield, David R.; Millsteed, Gregory K.; Zukerman, Moshe

1994-04-01

Congestion controls are a key factor in achieving the robust performance required of common channel signaling (CCS) networks in the face of partial network failures and extreme traffic loads, especially as networks become large and carry high traffic volume. The CCITT recommendations define a number of types of congestion control, and the parameters of the controls must be well set in order to ensure their efficacy under transient and sustained signalling network overload. The objective of this paper is to present a modeling approach to the determination of the network parameters that govern the performance of the SS7 congestion controls under sustained overload. Results of the investigation by simulation are presented and discussed.

Design and Performance of the Acts Gigabit Satellite Network High Data-Rate Ground Station

NASA Technical Reports Server (NTRS)

Hoder, Doug; Kearney, Brian

1995-01-01

The ACTS High Data-Rate Ground stations were built to support the ACTS Gigabit Satellite Network (GSN). The ACTS GSN was designed to provide fiber-compatible SONET service to remote nodes and networks through a wideband satellite system. The ACTS satellite is unique in its extremely wide bandwidth, and electronically controlled spot beam antennas. This paper discusses the requirements, design and performance of the RF section of the ACTS High Data-Rate Ground Stations and constituent hardware. The ACTS transponder systems incorporate highly nonlinear hard limiting. This introduced a major complexity in to the design and subsequent modification of the ground stations. A discussion of the peculiarities of the A CTS spacecraft transponder system and their impact is included.

Performance Modeling of Network-Attached Storage Device Based Hierarchical Mass Storage Systems

NASA Technical Reports Server (NTRS)

Menasce, Daniel A.; Pentakalos, Odysseas I.

1995-01-01

Network attached storage devices improve I/O performance by separating control and data paths and eliminating host intervention during the data transfer phase. Devices are attached to both a high speed network for data transfer and to a slower network for control messages. Hierarchical mass storage systems use disks to cache the most recently used files and a combination of robotic and manually mounted tapes to store the bulk of the files in the file system. This paper shows how queuing network models can be used to assess the performance of hierarchical mass storage systems that use network attached storage devices as opposed to host attached storage devices. Simulation was used to validate the model. The analytic model presented here can be used, among other things, to evaluate the protocols involved in 1/0 over network attached devices.

Apparatus for fixing latency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hall, David R; Bartholomew, David B; Moon, Justin

2009-09-08

An apparatus for fixing computational latency within a deterministic region on a network comprises a network interface modem, a high priority module and at least one deterministic peripheral device. The network interface modem is in communication with the network. The high priority module is in communication with the network interface modem. The at least one deterministic peripheral device is connected to the high priority module. The high priority module comprises a packet assembler/disassembler, and hardware for performing at least one operation. Also disclosed is an apparatus for executing at least one instruction on a downhole device within a deterministic region,more » the apparatus comprising a control device, a downhole network, and a downhole device. The control device is near the surface of a downhole tool string. The downhole network is integrated into the tool string. The downhole device is in communication with the downhole network.« less

Enhancing End-to-End Performance of Information Services Over Ka-Band Global Satellite Networks

NASA Technical Reports Server (NTRS)

Bhasin, Kul B.; Glover, Daniel R.; Ivancic, William D.; vonDeak, Thomas C.

1997-01-01

The Internet has been growing at a rapid rate as the key medium to provide information services such as e-mail, WWW and multimedia etc., however its global reach is limited. Ka-band communication satellite networks are being developed to increase the accessibility of information services via the Internet at global scale. There is need to assess satellite networks in their ability to provide these services and interconnect seamlessly with existing and proposed terrestrial telecommunication networks. In this paper the significant issues and requirements in providing end-to-end high performance for the delivery of information services over satellite networks based on various layers in the OSI reference model are identified. Key experiments have been performed to evaluate the performance of digital video and Internet over satellite-like testbeds. The results of the early developments in ATM and TCP protocols over satellite networks are summarized.

Fracture Simulation of Highly Crosslinked Polymer Networks: Triglyceride-Based Adhesives

NASA Astrophysics Data System (ADS)

Lorenz, Christian; Stevens, Mark; Wool, Richard

2003-03-01

The ACRES program at the U. of Delaware has shown that triglyceride oils derived from plants are a favorable alternative to the traditional adhesives. The triglyceride networks are formed from an initial mixture of styrene monomers, free-radical initiators and triglycerides. We have performed simulations to study the effect of physical composition and physical characteristics of the triglyceride network on the strength of triglyceride network. A coarse-grained, bead-spring model of the triglyceride system is used. The average triglyceride consists of 6 beads per chain, the styrenes are represented as a single bead and the initiators are two bead chains. The polymer network is formed using an off-lattice 3D Monte Carlo simulation, in which the initiators activate the styrene and triglyceride reactive sites and then bonds are randomly formed between the styrene and active triglyceride monomers producing a highly crosslinked polymer network. Molecular dynamics simulations of the network under tensile and shear strains were performed to determine the strength as a function of the network composition. The relationship between the network structure and its strength will also be discussed.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Secchi, Simone; Tumeo, Antonino; Villa, Oreste

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 inmore » 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.« less

A three-dimensional carbon nano-network for high performance lithium ion batteries

DOE PAGES

Tian, Miao; Wang, Wei; Liu, Yang; ...

2014-11-20

Three-dimensional (3D) network structure has been envisioned as a superior architecture for lithium ion battery (LIB) electrodes, which enhances both ion and electron transport to significantly improve battery performance. Herein, a 3D carbon nano-network is fabricated through chemical vapor deposition of carbon on a scalably manufactured 3D porous anodic alumina (PAA) template. As a demonstration on the applicability of 3D carbon nano-network for LIB electrodes, the low conductivity active material, TiO 2, is then uniformly coated on the 3D carbon nano-network using atomic layer deposition. High power performance is demonstrated in the 3D C/TiO 2 electrodes, where the parallel tubesmore » and gaps in the 3D carbon nano-network facilitates fast Li ion transport. A large areal capacity of ~0.37 mAh·cm –2 is achieved due to the large TiO 2 mass loading in the 60 µm-thick 3D C/TiO 2 electrodes. At a test rate of C/5, the 3D C/TiO 2 electrode with 18 nm-thick TiO 2 delivers a high gravimetric capacity of ~240 mAh g –1, calculated with the mass of the whole electrode. A long cycle life of over 1000 cycles with a capacity retention of 91% is demonstrated at 1C. In this study, the effects of the electrical conductivity of carbon nano-network, ion diffusion, and the electrolyte permeability on the rate performance of these 3D C/TiO 2 electrodes are systematically studied.« less

A Wireless Communications Laboratory on Cellular Network Planning

ERIC Educational Resources Information Center

Dawy, Z.; Husseini, A.; Yaacoub, E.; Al-Kanj, L.

2010-01-01

The field of radio network planning and optimization (RNPO) is central for wireless cellular network design, deployment, and enhancement. Wireless cellular operators invest huge sums of capital on deploying, launching, and maintaining their networks in order to ensure competitive performance and high user satisfaction. This work presents a lab…

Copercolating Networks: An Approach for Realizing High-Performance Transparent Conductors using Multicomponent Nanostructured Networks

NASA Astrophysics Data System (ADS)

Das, Suprem R.; Sadeque, Sajia; Jeong, Changwook; Chen, Ruiyi; Alam, Muhammad A.; Janes, David B.

2016-06-01

Although transparent conductive oxides such as indium tin oxide (ITO) are widely employed as transparent conducting electrodes (TCEs) for applications such as touch screens and displays, new nanostructured TCEs are of interest for future applications, including emerging transparent and flexible electronics. A number of twodimensional networks of nanostructured elements have been reported, including metallic nanowire networks consisting of silver nanowires, metallic carbon nanotubes (m-CNTs), copper nanowires or gold nanowires, and metallic mesh structures. In these single-component systems, it has generally been difficult to achieve sheet resistances that are comparable to ITO at a given broadband optical transparency. A relatively new third category of TCEs consisting of networks of 1D-1D and 1D-2D nanocomposites (such as silver nanowires and CNTs, silver nanowires and polycrystalline graphene, silver nanowires and reduced graphene oxide) have demonstrated TCE performance comparable to, or better than, ITO. In such hybrid networks, copercolation between the two components can lead to relatively low sheet resistances at nanowire densities corresponding to high optical transmittance. This review provides an overview of reported hybrid networks, including a comparison of the performance regimes achievable with those of ITO and single-component nanostructured networks. The performance is compared to that expected from bulk thin films and analyzed in terms of the copercolation model. In addition, performance characteristics relevant for flexible and transparent applications are discussed. The new TCEs are promising, but significant work must be done to ensure earth abundance, stability, and reliability so that they can eventually replace traditional ITO-based transparent conductors.

Frequency Assignment for Joint Aerial Layer Network High-Capacity Backbone

DTIC Science & Technology

2017-08-11

Department of the Army position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does not constitute an...performance of the proposed approach. Frequency Assignment, JALN, Resource Allocation, Network Optimization, Performance Evaluation 24 Peng Wang 410-278

OpenSHMEM-UCX : Evaluation of UCX for implementing OpenSHMEM Programming Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Matthew B; Gorentla Venkata, Manjunath; Aderholdt, William Ferrol

2016-01-01

The OpenSHMEM reference implementation was developed towards the goal of developing an open source and high-performing Open- SHMEM implementation. To achieve portability and performance across various networks, the OpenSHMEM reference implementation uses GAS- Net and UCCS for network operations. Recently, new network layers have emerged with the promise of providing high-performance, scalabil- ity, and portability for HPC applications. In this paper, we implement the OpenSHMEM reference implementation to use the UCX framework for network operations. Then, we evaluate its performance and scalabil- ity on Cray XK systems to understand UCX s suitability for developing the OpenSHMEM programming model. Further, wemore » develop a bench- mark called SHOMS for evaluating the OpenSHMEM implementation. Our experimental results show that OpenSHMEM-UCX outperforms the vendor supplied OpenSHMEM implementation in most cases on the Cray XK system by up to 40% with respect to message rate and up to 70% for the execution of application kernels.« less

Fault-tolerant bandwidth reservation strategies for data transfers in high-performance networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuo, Liudong; Zhu, Michelle M.; Wu, Chase Q.

2016-11-22

Many next-generation e-science applications need fast and reliable transfer of large volumes of data with guaranteed performance, which is typically enabled by the bandwidth reservation service in high-performance networks. One prominent issue in such network environments with large footprints is that node and link failures are inevitable, hence potentially degrading the quality of data transfer. We consider two generic types of bandwidth reservation requests (BRRs) concerning data transfer reliability: (i) to achieve the highest data transfer reliability under a given data transfer deadline, and (ii) to achieve the earliest data transfer completion time while satisfying a given data transfer reliabilitymore » requirement. We propose two periodic bandwidth reservation algorithms with rigorous optimality proofs to optimize the scheduling of individual BRRs within BRR batches. The efficacy of the proposed algorithms is illustrated through extensive simulations in comparison with scheduling algorithms widely adopted in production networks in terms of various performance metrics.« less

Testing the Feasibility of a Low-Cost Network Performance Measurement Infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chevalier, Scott; Schopf, Jennifer M.; Miller, Kenneth

2016-07-01

Todays science collaborations depend on reliable, high performance networks, but monitoring the end-to-end performance of a network can be costly and difficult. The most accurate approaches involve using measurement equipment in many locations, which can be both expensive and difficult to manage due to immobile or complicated assets. The perfSONAR framework facilitates network measurement making management of the tests more reasonable. Traditional deployments have used over-provisioned servers, which can be expensive to deploy and maintain. As scientific network uses proliferate, there is a desire to instrument more facets of a network to better understand trends. This work explores low costmore » alternatives to assist with network measurement. Benefits include the ability to deploy more resources quickly, and reduced capital and operating expenditures. Finally, we present candidate platforms and a testing scenario that evaluated the relative merits of four types of small form factor equipment to deliver accurate performance measurements.« less

High-Performance, Semi-Interpenetrating Polymer Network

NASA Technical Reports Server (NTRS)

Pater, Ruth H.; Lowther, Sharon E.; Smith, Janice Y.; Cannon, Michelle S.; Whitehead, Fred M.; Ely, Robert M.

1992-01-01

High-performance polymer made by new synthesis in which one or more easy-to-process, but brittle, thermosetting polyimides combined with one or more tough, but difficult-to-process, linear thermoplastics to yield semi-interpenetrating polymer network (semi-IPN) having combination of easy processability and high tolerance to damage. Two commercially available resins combined to form tough, semi-IPN called "LaRC-RP49." Displays improvements in toughness and resistance to microcracking. LaRC-RP49 has potential as high-temperature matrix resin, adhesive, and molding resin. Useful in aerospace, automotive, and electronic industries.

Local-Area-Network Simulator

NASA Technical Reports Server (NTRS)

Gibson, Jim; Jordan, Joe; Grant, Terry

1990-01-01

Local Area Network Extensible Simulator (LANES) computer program provides method for simulating performance of high-speed local-area-network (LAN) technology. Developed as design and analysis software tool for networking computers on board proposed Space Station. Load, network, link, and physical layers of layered network architecture all modeled. Mathematically models according to different lower-layer protocols: Fiber Distributed Data Interface (FDDI) and Star*Bus. Written in FORTRAN 77.

Learning, memory, and the role of neural network architecture.

PubMed

Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

2011-06-01

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

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

PubMed

Andras, Peter

2018-02-01

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

Highly stable multi-wall carbon nanotubes@poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) core-shell composites with three-dimensional porous nano-network for electrochemical capacitors

NASA Astrophysics Data System (ADS)

Zhou, Haihan; Han, Gaoyi; Chang, Yunzhen; Fu, Dongying; Xiao, Yaoming

2015-01-01

A facile and feasible electrochemical polymerization method has been used to construct the multi-wall carbon nanotubes@poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) (MWCNTs@PEDOT/PSS) core-shell composites with three-dimensional (3D) porous nano-network microstructure. The composites are characterized with Fourier transform infrared spectroscopy, scanning electron microscope, and transmission electron microscopy. This special core-shell nanostructure can significantly reduce the ions diffusion distance and the 3D porous nano-network microstructure effectively enlarges the electrode/electrolyte interface. The electrochemical tests including cyclic voltammetry, galvanostatic charge/discharge measurements, and electrochemical impedance spectroscopy tests are performed, the results manifest the MWCNTs@PEDOT/PSS core-shell composites have superior capacitive behaviors and excellent cyclic stability, and a high areal capacitance of 98.1 mF cm-2 is achieved at 5 mV s-1 cyclic voltammetry scan. Furthermore, the MWCNTs@PEDOT/PSS composites exhibit obviously superior capacitive performance than that of PEDOT/PSS and PEDOT/Cl electrodes, indicating the effective composite of MWCNTs and PEDOT noticeably boosts the capacitive performance of PEDOT-based electrodes for electrochemical energy storage. Such a highly stable core-shell 3D network structural composite is very promising to be used as electrode materials for the high-performance electrochemical capacitors.

Direct formation of reduced graphene oxide and 3D lightweight nickel network composite foam by hydrohalic acids and its application for high-performance supercapacitors.

PubMed

Huang, Haifu; Tang, Yanmei; Xu, Lianqiang; Tang, Shaolong; Du, Youwei

2014-07-09

Here, a novel graphene composite foam with 3D lightweight continuous and interconnected nickel network was successfully synthesized by hydroiodic (HI) acid using nickel foam as substrate template. The graphene had closely coated on the backbone of the 3D nickel conductive network to form nickel network supported composite foam without any polymeric binder during the HI reduction of GO process, and the nickel conductive network can be maintained even in only a small amount of nickel with 1.1 mg/cm(2) and had replaced the traditional current collector nickel foam (35 mg/cm(2)). In the electrochemical measurement, a supercapacitor device based on the 3D nickel network and graphene composite foam exhibited high rate capability of 100 F/g at 0.5 A/g and 86.7 F/g at 62.5 A/g, good cycle stability with capacitance retention of 95% after 2000 cycles, low internal resistance (1.68 Ω), and excellent flexible properties. Furthermore, the gravimetric capacitance (calculated using the total mass of the electrode) was high up to 40.9 F/g. Our work not only demonstrates high-quality graphene/nickel composite foam, but also provides a universal route for the rational design of high performance of supercapacitors.

Nanostructured CuS networks composed of interconnected nanoparticles for asymmetric supercapacitors.

PubMed

Fu, Wenbin; Han, Weihua; Zha, Heming; Mei, Junfeng; Li, Yunxia; Zhang, Zemin; Xie, Erqing

2016-09-21

Nanostructured metal sulfides with excellent electrochemical activity and electrical conductivity are particularly promising for applications in high-performance energy storage devices. Here, we report on the facile synthesis of nanostructured CuS networks composed of interconnected nanoparticles as novel battery-type materials for asymmetric supercapacitors. We find that the CuS networks exhibit a high specific capacity of 49.8 mA g(-1) at a current density of 1 A g(-1), good rate capability and cycle stability. The superior performance could be attributed to the interconnected nanoparticles of CuS networks, which can facilitate electrolyte diffusion and provide fast electron pathways. Furthermore, an aqueous asymmetric supercapacitor has been assembled by using the CuS networks as the positive electrode and activated carbon as the negative electrode. The assembled device can work at a high operating voltage of 1.6 V and show a maximum energy density of 17.7 W h kg(-1) at a power density of 504 W kg(-1). This study indicates that the CuS networks have great potential for supercapacitor applications.

Receiver-Assisted Congestion Control to Achieve High Throughput in Lossy Wireless Networks

NASA Astrophysics Data System (ADS)

Shi, Kai; Shu, Yantai; Yang, Oliver; Luo, Jiarong

2010-04-01

Many applications would require fast data transfer in high-speed wireless networks nowadays. However, due to its conservative congestion control algorithm, Transmission Control Protocol (TCP) cannot effectively utilize the network capacity in lossy wireless networks. In this paper, we propose a receiver-assisted congestion control mechanism (RACC) in which the sender performs loss-based control, while the receiver is performing delay-based control. The receiver measures the network bandwidth based on the packet interarrival interval and uses it to compute a congestion window size deemed appropriate for the sender. After receiving the advertised value feedback from the receiver, the sender then uses the additive increase and multiplicative decrease (AIMD) mechanism to compute the correct congestion window size to be used. By integrating the loss-based and the delay-based congestion controls, our mechanism can mitigate the effect of wireless losses, alleviate the timeout effect, and therefore make better use of network bandwidth. Simulation and experiment results in various scenarios show that our mechanism can outperform conventional TCP in high-speed and lossy wireless environments.

Co-scheduling of network resource provisioning and host-to-host bandwidth reservation on high-performance network and storage systems

DOEpatents

Yu, Dantong; Katramatos, Dimitrios; Sim, Alexander; Shoshani, Arie

2014-04-22

A cross-domain network resource reservation scheduler configured to schedule a path from at least one end-site includes a management plane device configured to monitor and provide information representing at least one of functionality, performance, faults, and fault recovery associated with a network resource; a control plane device configured to at least one of schedule the network resource, provision local area network quality of service, provision local area network bandwidth, and provision wide area network bandwidth; and a service plane device configured to interface with the control plane device to reserve the network resource based on a reservation request and the information from the management plane device. Corresponding methods and computer-readable medium are also disclosed.

Regular Topologies for Gigabit Wide-Area Networks. Volume 1

NASA Technical Reports Server (NTRS)

Shacham, Nachum; Denny, Barbara A.; Lee, Diane S.; Khan, Irfan H.; Lee, Danny Y. C.; McKenney, Paul

1994-01-01

In general terms, this project aimed at the analysis and design of techniques for very high-speed networking. The formal objectives of the project were to: (1) Identify switch and network technologies for wide-area networks that interconnect a large number of users and can provide individual data paths at gigabit/s rates; (2) Quantitatively evaluate and compare existing and proposed architectures and protocols, identify their strength and growth potentials, and ascertain the compatibility of competing technologies; and (3) Propose new approaches to existing architectures and protocols, and identify opportunities for research to overcome deficiencies and enhance performance. The project was organized into two parts: 1. The design, analysis, and specification of techniques and protocols for very-high-speed network environments. In this part, SRI has focused on several key high-speed networking areas, including Forward Error Control (FEC) for high-speed networks in which data distortion is the result of packet loss, and the distribution of broadband, real-time traffic in multiple user sessions. 2. Congestion Avoidance Testbed Experiment (CATE). This part of the project was done within the framework of the DARTnet experimental T1 national network. The aim of the work was to advance the state of the art in benchmarking DARTnet's performance and traffic control by developing support tools for network experimentation, by designing benchmarks that allow various algorithms to be meaningfully compared, and by investigating new queueing techniques that better satisfy the needs of best-effort and reserved-resource traffic. This document is the final technical report describing the results obtained by SRI under this project. The report consists of three volumes: Volume 1 contains a technical description of the network techniques developed by SRI in the areas of FEC and multicast of real-time traffic. Volume 2 describes the work performed under CATE. Volume 3 contains the source code of all software developed under CATE.

Evaluation on surface current observing network of high frequency ground wave radars in the Gulf of Thailand

NASA Astrophysics Data System (ADS)

Yin, Xunqiang; Shi, Junqiang; Qiao, Fangli

2018-05-01

Due to the high cost of ocean observation system, the scientific design of observation network becomes much important. The current network of the high frequency radar system in the Gulf of Thailand has been studied using a three-dimensional coastal ocean model. At first, the observations from current radars have been assimilated into this coastal model and the forecast results have improved due to the data assimilation. But the results also show that further optimization of the observing network is necessary. And then, a series of experiments were carried out to assess the performance of the existing high frequency ground wave radar surface current observation system. The simulated surface current data in three regions were assimilated sequentially using an efficient ensemble Kalman filter data assimilation scheme. The experimental results showed that the coastal surface current observation system plays a positive role in improving the numerical simulation of the currents. Compared with the control experiment without assimilation, the simulation precision of surface and subsurface current had been improved after assimilated the surface currents observed at current networks. However, the improvement for three observing regions was quite different and current observing network in the Gulf of Thailand is not effective and a further optimization is required. Based on these evaluations, a manual scheme has been designed by discarding the redundant and inefficient locations and adding new stations where the performance after data assimilation is still low. For comparison, an objective scheme based on the idea of data assimilation has been obtained. Results show that all the two schemes of observing network perform better than the original network and optimal scheme-based data assimilation is much superior to the manual scheme that based on the evaluation of original observing network in the Gulf of Thailand. The distributions of the optimal network of radars could be a useful guidance for future design of observing system in this region.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

PubMed Central

Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

2016-01-01

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. PMID:27629707

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

PubMed

Rosenberg, Monica D; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S; Shen, Xilin; Constable, R Todd; Li, Chiang-Shan R; Chun, Marvin M

2016-09-14

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. Copyright © 2016 the authors 0270-6474/16/369547-11$15.00/0.

Outsourcing Security Services for Low Performance Portable Devices

NASA Astrophysics Data System (ADS)

Szentgyörgyi, Attila; Korn, András

The number of portable devices using wireless network technologies is on the rise. Some of these devices are incapable of, or at a disadvantage at using secure Internet services, because secure communication often requires comparatively high computing capacity. In this paper, we propose a solution which can be used to offer secure network services for low performance portable devices without severely degrading data transmission rates. We also show that using our approach these devices can utilize some secure network services which were so far unavailable to them due to a lack of software support. In order to back up our claims, we present performance measurement results obtained in a test network.

Efficiently passing messages in distributed spiking neural network simulation.

PubMed

Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

2013-01-01

Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

Task-dependent individual differences in prefrontal connectivity.

PubMed

Biswal, Bharat B; Eldreth, Dana A; Motes, Michael A; Rypma, Bart

2010-09-01

Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences, but this burgeoning literature has been characterized by inconsistent results. To test the hypothesis that differences in task demands could contribute to between-study variability in brain-behavior relationships, we had participants perform 2 tasks that varied in the extent of cognitive involvement. We examined connectivity between brain regions during a low-demand vigilance task and a higher-demand digit-symbol visual search task using Granger causality analysis (GCA). Our results showed 1) Significant differences in numbers of frontoparietal connections between low- and high-demand tasks 2) that GCA can detect activity changes that correspond with task-demand changes, and 3) faster participants showed more vigilance-related activity than slower participants, but less visual-search activity. These results suggest that relatively low-demand cognitive performance depends on spontaneous bidirectionally fluctuating network activity, whereas high-demand performance depends on a limited, unidirectional network. The nature of brain-behavior relationships may vary depending on the extent of cognitive demand. High-demand network activity may reflect the extent to which individuals require top-down executive guidance of behavior for successful task performance. Low-demand network activity may reflect task- and performance monitoring that minimizes executive requirements for guidance of behavior.

Task-Dependent Individual Differences in Prefrontal Connectivity

PubMed Central

Biswal, Bharat B.; Eldreth, Dana A.; Motes, Michael A.

2010-01-01

Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences, but this burgeoning literature has been characterized by inconsistent results. To test the hypothesis that differences in task demands could contribute to between-study variability in brain-behavior relationships, we had participants perform 2 tasks that varied in the extent of cognitive involvement. We examined connectivity between brain regions during a low-demand vigilance task and a higher-demand digit–symbol visual search task using Granger causality analysis (GCA). Our results showed 1) Significant differences in numbers of frontoparietal connections between low- and high-demand tasks 2) that GCA can detect activity changes that correspond with task-demand changes, and 3) faster participants showed more vigilance-related activity than slower participants, but less visual-search activity. These results suggest that relatively low-demand cognitive performance depends on spontaneous bidirectionally fluctuating network activity, whereas high-demand performance depends on a limited, unidirectional network. The nature of brain-behavior relationships may vary depending on the extent of cognitive demand. High-demand network activity may reflect the extent to which individuals require top-down executive guidance of behavior for successful task performance. Low-demand network activity may reflect task- and performance monitoring that minimizes executive requirements for guidance of behavior. PMID:20064942

Facile solid-state synthesis of Ni@C nanosheet-assembled hierarchical network for high-performance lithium storage

NASA Astrophysics Data System (ADS)

Gu, Jinghe; Li, Qiyun; Zeng, Pan; Meng, Yulin; Zhang, Xiukui; Wu, Ping; Zhou, Yiming

2017-08-01

Micro/nano-architectured transition-metal@C hybrids possess unique structural and compositional features toward lithium storage, and are thus expected to manifest ideal anodic performances in advanced lithium-ion batteries (LIBs). Herein, we propose a facile and scalable solid-state coordination and subsequent pyrolysis route for the formation of a novel type of micro/nano-architectured transition-metal@C hybrid (i.e., Ni@C nanosheet-assembled hierarchical network, Ni@C network). Moreover, this coordination-pyrolysis route has also been applied for the construction of bare carbon network using zinc salts instead of nickel salts as precursors. When applied as potential anodic materials in LIBs, the Ni@C network exhibits Ni-content-dependent electrochemical performances, and the partially-etched Ni@C network manifests markedly enhanced Li-storage performances in terms of specific capacities, cycle life, and rate capability than the pristine Ni@C network and carbon network. The proposed solid-state coordination and pyrolysis strategy would open up new opportunities for constructing micro/nano-architectured transition-metal@C hybrids as advanced anode materials for LIBs.

Effects of the bipartite structure of a network on performance of recommenders

NASA Astrophysics Data System (ADS)

Wang, Qing-Xian; Li, Jian; Luo, Xin; Xu, Jian-Jun; Shang, Ming-Sheng

2018-02-01

Recommender systems aim to predict people's preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender's performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender's performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics.

Mesh Network Architecture for Enabling Inter-Spacecraft Communication

NASA Technical Reports Server (NTRS)

Becker, Christopher; Merrill, Garrick

2017-01-01

To enable communication between spacecraft operating in a formation or small constellation, a mesh network architecture was developed and tested using a time division multiple access (TDMA) communication scheme. The network is designed to allow for the exchange of telemetry and other data between spacecraft to enable collaboration between small spacecraft. The system uses a peer-to-peer topology with no central router, so that it does not have a single point of failure. The mesh network is dynamically configurable to allow for addition and subtraction of new spacecraft into the communication network. Flight testing was performed using an unmanned aerial system (UAS) formation acting as a spacecraft analogue and providing a stressing environment to prove mesh network performance. The mesh network was primarily devised to provide low latency, high frequency communication but is flexible and can also be configured to provide higher bandwidth for applications desiring high data throughput. The network includes a relay functionality that extends the maximum range between spacecraft in the network by relaying data from node to node. The mesh network control is implemented completely in software making it hardware agnostic, thereby allowing it to function with a wide variety of existing radios and computing platforms..

Centrality Measures and Academic Achievement in Computerized Classroom Social Networks: An Empirical Investigation

ERIC Educational Resources Information Center

Reychav, Iris; Raban, Daphne Ruth; McHaney, Roger

2018-01-01

The current empirical study examines relationships between network measures and learning performance from a social network analysis perspective. We collected computerized, networking data to analyze how 401 junior high students connected to classroom peers using text- and video-based material on iPads. Following a period of computerized…

The application of neural networks to the SSME startup transient

NASA Technical Reports Server (NTRS)

Meyer, Claudia M.; Maul, William A.

1991-01-01

Feedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter, the high pressure oxidizer turbine discharge temperature, a redlined parameter, and the high pressure fuel pump discharge pressure, a failure-indicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set.

Silicon Modulators, Switches and Sub-systems for Optical Interconnect

NASA Astrophysics Data System (ADS)

Li, Qi

Silicon photonics is emerging as a promising platform for manufacturing and integrating photonic devices for light generation, modulation, switching and detection. The compatibility with existing CMOS microelectronic foundries and high index contrast in silicon could enable low cost and high performance photonic systems, which find many applications in optical communication, data center networking and photonic network-on-chip. This thesis first develops and demonstrates several experimental work on high speed silicon modulators and switches with record performance and novel functionality. A 8x40 Gb/s transmitter based on silicon microrings is first presented. Then an end-to-end link using microrings for Binary Phase Shift Keying (BPSK) modulation and demodulation is shown, and its performance with conventional BPSK modulation/ demodulation techniques is compared. Next, a silicon traveling-wave Mach- Zehnder modulator is demonstrated at data rate up to 56 Gb/s for OOK modulation and 48 Gb/s for BPSK modulation, showing its capability at high speed communication systems. Then a single silicon microring is shown with 2x2 full crossbar switching functionality, enabling optical interconnects with ultra small footprint. Then several other experiments in the silicon platform are presented, including a fully integrated in-band Optical Signal to Noise Ratio (OSNR) monitor, characterization of optical power upper bound in a silicon microring modulator, and wavelength conversion in a dispersion-engineered waveguide. The last part of this thesis is on network-level application of photonics, specically a broadcast-and-select network based on star coupler is introduced, and its scalability performance is studied. Finally a novel switch architecture for data center networks is discussed, and its benefits as a disaggregated network are presented.

Dynamic bandwidth allocation based on multiservice in software-defined wavelength-division multiplexing time-division multiplexing passive optical network

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Jin, Feifei; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun

2017-03-01

The wavelength-division multiplexing passive optical network (WDM-PON) is a potential technology to carry multiple services in an optical access network. However, it has the disadvantages of high cost and an immature technique for users. A software-defined WDM/time-division multiplexing PON was proposed to meet the requirements of high bandwidth, high performance, and multiple services. A reasonable and effective uplink dynamic bandwidth allocation algorithm was proposed. A controller with dynamic wavelength and slot assignment was introduced, and a different optical dynamic bandwidth management strategy was formulated flexibly for services of different priorities according to the network loading. The simulation compares the proposed algorithm with the interleaved polling with adaptive cycle time algorithm. The algorithm shows better performance in average delay, throughput, and bandwidth utilization. The results show that the delay is reduced to 62% and the throughput is improved by 35%.

RoCoMAR: robots' controllable mobility aided routing and relay architecture for mobile sensor networks.

PubMed

Le, Duc Van; Oh, Hoon; Yoon, Seokhoon

2013-07-05

In a practical deployment, mobile sensor network (MSN) suffers from a low performance due to high node mobility, time-varying wireless channel properties, and obstacles between communicating nodes. In order to tackle the problem of low network performance and provide a desired end-to-end data transfer quality, in this paper we propose a novel ad hoc routing and relaying architecture, namely RoCoMAR (Robots' Controllable Mobility Aided Routing) that uses robotic nodes' controllable mobility. RoCoMAR repeatedly performs link reinforcement process with the objective of maximizing the network throughput, in which the link with the lowest quality on the path is identified and replaced with high quality links by placing a robotic node as a relay at an optimal position. The robotic node resigns as a relay if the objective is achieved or no more gain can be obtained with a new relay. Once placed as a relay, the robotic node performs adaptive link maintenance by adjusting its position according to the movements of regular nodes. The simulation results show that RoCoMAR outperforms existing ad hoc routing protocols for MSN in terms of network throughput and end-to-end delay.

RoCoMAR: Robots' Controllable Mobility Aided Routing and Relay Architecture for Mobile Sensor Networks

PubMed Central

Van Le, Duc; Oh, Hoon; Yoon, Seokhoon

2013-01-01

In a practical deployment, mobile sensor network (MSN) suffers from a low performance due to high node mobility, time-varying wireless channel properties, and obstacles between communicating nodes. In order to tackle the problem of low network performance and provide a desired end-to-end data transfer quality, in this paper we propose a novel ad hoc routing and relaying architecture, namely RoCoMAR (Robots' Controllable Mobility Aided Routing) that uses robotic nodes' controllable mobility. RoCoMAR repeatedly performs link reinforcement process with the objective of maximizing the network throughput, in which the link with the lowest quality on the path is identified and replaced with high quality links by placing a robotic node as a relay at an optimal position. The robotic node resigns as a relay if the objective is achieved or no more gain can be obtained with a new relay. Once placed as a relay, the robotic node performs adaptive link maintenance by adjusting its position according to the movements of regular nodes. The simulation results show that RoCoMAR outperforms existing ad hoc routing protocols for MSN in terms of network throughput and end-to-end delay. PMID:23881134

Hypothesis generation using network structures on community health center cancer-screening performance.

PubMed

Carney, Timothy Jay; Morgan, Geoffrey P; Jones, Josette; McDaniel, Anna M; Weaver, Michael T; Weiner, Bryan; Haggstrom, David A

2015-10-01

Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Highly sensitive piezo-resistive graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone composites with improved conductive network construction.

PubMed

Zhao, Hang; Bai, Jinbo

2015-05-13

The constructions of internal conductive network are dependent on microstructures of conductive fillers, determining various electrical performances of composites. Here, we present the advanced graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone (GCHs/PDMS) composites with high piezo-resistive performance. GCH particles were synthesized by the catalyst chemical vapor deposition approach. The synthesized GCHs can be well dispersed in the matrix through the mechanical blending process. Due to the exfoliated GNP and aligned CNTs coupling structure, the flexible composite shows an ultralow percolation threshold (0.64 vol %) and high piezo-resistive sensitivity (gauge factor ∼ 10(3) and pressure sensitivity ∼ 0.6 kPa(-1)). Slight motions of finger can be detected and distinguished accurately using the composite film as a typical wearable sensor. These results indicate that designing the internal conductive network could be a reasonable strategy to improve the piezo-resistive performance of composites.

A Space Operations Network Alternative: Using Globally Connected Research and Education Networks for Space-Based Science Operations

NASA Technical Reports Server (NTRS)

Bradford, Robert N.

2006-01-01

Earth based networking in support of various space agency projects has been based on leased service/circuits which has a high associated cost. This cost is almost always taken from the science side resulting in less science. This is a proposal to use Research and Education Networks (RENs) worldwide to support space flight operations in general and space-based science operations in particular. The RENs were developed to support scientific and educational endeavors. They do not provide support for general Internet traffic. The connectivity and performance of the research and education networks is superb. The connectivity at Layer 3 (IP) virtually encompasses the globe. Most third world countries and all developed countries have their own research and education networks, which are connected globally. Performance of the RENs especially in the developed countries is exceptional. Bandwidth capacity currently exists and future expansion promises that this capacity will continue. REN performance statistics has always exceeded minimum requirements for spaceflight support. Research and Education networks are more loosely managed than a corporate network but are highly managed when compared to the commodity Internet. Management of RENs on an international level is accomplished by the International Network Operations Center at Indiana University at Indianapolis. With few exceptions, each regional and national REN has its own network ops center. The acceptable use policies (AUP), although differing by country, allows any scientific program or project the use of their networks. Once in compliance with the first RENs AUP, all others will accept that specific traffic including regional and transoceanic networks. RENs can support spaceflight related scientific programs and projects. Getting the science to the researcher is obviously key to any scientific project. RENs provide a pathway to virtually any college or university in the world, as well as many governmental institutes and science centers. RENs are not to be used for mission critical types of network traffic, even though RENs performance characteristics would support it.

Mass storage technology in networks

NASA Astrophysics Data System (ADS)

Ishii, Katsunori; Takeda, Toru; Itao, Kiyoshi; Kaneko, Reizo

1990-08-01

Trends and features of mass storage subsystems in network are surveyed and their key technologies spotlighted. Storage subsystems are becoming increasingly important in new network systems in which communications and data processing are systematically combined. These systems require a new class of high-performance mass-information storage in order to effectively utilize their processing power. The requirements of high transfer rates, high transactional rates and large storage capacities, coupled with high functionality, fault tolerance and flexibility in configuration, are major challenges in storage subsystems. Recent progress in optical disk technology has resulted in improved performance of on-line external memories to optical disk drives, which are competing with mid-range magnetic disks. Optical disks are more effective than magnetic disks in using low-traffic random-access file storing multimedia data that requires large capacity, such as in archive use and in information distribution use by ROM disks. Finally, it demonstrates image coded document file servers for local area network use that employ 130mm rewritable magneto-optical disk subsystems.

High-Performance Complementary Transistors and Medium-Scale Integrated Circuits Based on Carbon Nanotube Thin Films.

PubMed

Yang, Yingjun; Ding, Li; Han, Jie; Zhang, Zhiyong; Peng, Lian-Mao

2017-04-25

Solution-derived carbon nanotube (CNT) network films with high semiconducting purity are suitable materials for the wafer-scale fabrication of field-effect transistors (FETs) and integrated circuits (ICs). However, it is challenging to realize high-performance complementary metal-oxide semiconductor (CMOS) FETs with high yield and stability on such CNT network films, and this difficulty hinders the development of CNT-film-based ICs. In this work, we developed a doping-free process for the fabrication of CMOS FETs based on solution-processed CNT network films, in which the polarity of the FETs was controlled using Sc or Pd as the source/drain contacts to selectively inject carriers into the channels. The fabricated top-gated CMOS FETs showed high symmetry between the characteristics of n- and p-type devices and exhibited high-performance uniformity and excellent scalability down to a gate length of 1 μm. Many common types of CMOS ICs, including typical logic gates, sequential circuits, and arithmetic units, were constructed based on CNT films, and the fabricated ICs exhibited rail-to-rail outputs because of the high noise margin of CMOS circuits. In particular, 4-bit full adders consisting of 132 CMOS FETs were realized with 100% yield, thereby demonstrating that this CMOS technology shows the potential to advance the development of medium-scale CNT-network-film-based ICs.

Collective network for computer structures

DOEpatents

Blumrich, Matthias A; Coteus, Paul W; Chen, Dong; Gara, Alan; Giampapa, Mark E; Heidelberger, Philip; Hoenicke, Dirk; Takken, Todd E; Steinmacher-Burow, Burkhard D; Vranas, Pavlos M

2014-01-07

A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to the needs of a processing algorithm.

Collective network for computer structures

DOEpatents

Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

2011-08-16

A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

Global interrupt and barrier networks

DOEpatents

Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E; Heidelberger, Philip; Kopcsay, Gerard V.; Steinmacher-Burow, Burkhard D.; Takken, Todd E.

2008-10-28

A system and method for generating global asynchronous signals in a computing structure. Particularly, a global interrupt and barrier network is implemented that implements logic for generating global interrupt and barrier signals for controlling global asynchronous operations performed by processing elements at selected processing nodes of a computing structure in accordance with a processing algorithm; and includes the physical interconnecting of the processing nodes for communicating the global interrupt and barrier signals to the elements via low-latency paths. The global asynchronous signals respectively initiate interrupt and barrier operations at the processing nodes at times selected for optimizing performance of the processing algorithms. In one embodiment, the global interrupt and barrier network is implemented in a scalable, massively parallel supercomputing device structure comprising a plurality of processing nodes interconnected by multiple independent networks, with each node including one or more processing elements for performing computation or communication activity as required when performing parallel algorithm operations. One multiple independent network includes a global tree network for enabling high-speed global tree communications among global tree network nodes or sub-trees thereof. The global interrupt and barrier network may operate in parallel with the global tree network for providing global asynchronous sideband signals.

Tough high performance composite matrix

NASA Technical Reports Server (NTRS)

Pater, Ruth H. (Inventor); Johnston, Norman J. (Inventor)

1994-01-01

This invention is a semi-interpentrating polymer network which includes a high performance thermosetting polyimide having a nadic end group acting as a crosslinking site and a high performance linear thermoplastic polyimide. Provided is an improved high temperature matrix resin which is capable of performing in the 200 to 300 C range. This resin has significantly improved toughness and microcracking resistance, excellent processability, mechanical performance, and moisture and solvent resistances.

The Strength of the Strongest Ties in Collaborative Problem Solving

NASA Astrophysics Data System (ADS)

de Montjoye, Yves-Alexandre; Stopczynski, Arkadiusz; Shmueli, Erez; Pentland, Alex; Lehmann, Sune

2014-06-01

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.

The strength of the strongest ties in collaborative problem solving.

PubMed

de Montjoye, Yves-Alexandre; Stopczynski, Arkadiusz; Shmueli, Erez; Pentland, Alex; Lehmann, Sune

2014-06-20

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.

Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs

PubMed Central

Javaid, Nadeem; Ahmed, Farwa; Wadud, Zahid; Alrajeh, Nabil; Alabed, Mohamad Souheil; Ilahi, Manzoor

2017-01-01

Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network. PMID:28763014

Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs.

PubMed

Javaid, Nadeem; Ahmed, Farwa; Wadud, Zahid; Alrajeh, Nabil; Alabed, Mohamad Souheil; Ilahi, Manzoor

2017-08-01

Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network.

Towards a Methodology for Validation of Centrality Measures in Complex Networks

PubMed Central

2014-01-01

Background Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? Purpose The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. Method We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. Results Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes. PMID:24709999

Routing optimization in networks based on traffic gravitational field model

NASA Astrophysics Data System (ADS)

Liu, Longgeng; Luo, Guangchun

2017-04-01

For research on the gravitational field routing mechanism on complex networks, we further analyze the gravitational effect of paths. In this study, we introduce the concept of path confidence degree to evaluate the unblocked reliability of paths that it takes the traffic state of all nodes on the path into account from the overall. On the basis of this, we propose an improved gravitational field routing protocol considering all the nodes’ gravities on the path and the path confidence degree. In order to evaluate the transmission performance of the routing strategy, an order parameter is introduced to measure the network throughput by the critical value of phase transition from a free-flow phase to a jammed phase, and the betweenness centrality is used to evaluate the transmission performance and traffic congestion of the network. Simulation results show that compared with the shortest-path routing strategy and the previous gravitational field routing strategy, the proposed algorithm improves the network throughput considerably and effectively balances the traffic load within the network, and all nodes in the network are utilized high efficiently. As long as γ ≥ α, the transmission performance can reach the maximum and remains unchanged for different α and γ, which ensures that the proposed routing protocol is high efficient and stable.

Interconnected V2O5 nanoporous network for high-performance supercapacitors.

PubMed

Saravanakumar, B; Purushothaman, Kamatchi K; Muralidharan, G

2012-09-26

Vanadium pentoxide (V(2)O(5)) has attracted attention for supercapcitor applications because of its extensive multifunctional properties. In the present study, V(2)O(5) nanoporous network was synthesized via simple capping-agent-assisted precipitation technique and it is further annealed at different temperatures. The effect of annealing temperature on the morphology, electrochemical and structural properties, and stability upon oxidation-reduction cycling has been analyzed for supercapacitor application. We achieved highest specific capacitance of 316 F g(-1) for interconnected V(2)O(5) nanoporous network. This interconnected nanoporous network creates facile nanochannels for ion diffusion and facilitates the easy accessibility of ions. Moreover, after six hundred consecutive cycling processes the specific capacitance has changed only by 24%. A simple cost-effective preparation technique of V(2)O(5) nanoporous network with excellent capacitive behavior, energy density, and stability encourages its possible commercial exploitation for the development of high-performance supercapacitors.

The GÉANT network: addressing current and future needs of the HEP community

NASA Astrophysics Data System (ADS)

Capone, Vincenzo; Usman, Mian

2015-12-01

The GÉANT infrastructure is the backbone that serves the scientific communities in Europe for their data movement needs and their access to international research and education networks. Using the extensive fibre footprint and infrastructure in Europe the GÉANT network delivers a portfolio of services aimed to best fit the specific needs of the users, including Authentication and Authorization Infrastructure, end-to-end performance monitoring, advanced network services (dynamic circuits, L2-L3VPN, MD-VPN). This talk will outline the factors that help the GÉANT network to respond to the needs of the High Energy Physics community, both in Europe and worldwide. The Pan-European network provides the connectivity between 40 European national research and education networks. In addition, GÉANT also connects the European NRENs to the R&E networks in other world region and has reach to over 110 NREN worldwide, making GÉANT the best connected Research and Education network, with its multiple intercontinental links to different continents e.g. North and South America, Africa and Asia-Pacific. The High Energy Physics computational needs have always had (and will keep having) a leading role among the scientific user groups of the GÉANT network: the LHCONE overlay network has been built, in collaboration with the other big world REN, specifically to address the peculiar needs of the LHC data movement. Recently, as a result of a series of coordinated efforts, the LHCONE network has been expanded to the Asia-Pacific area, and is going to include some of the main regional R&E network in the area. The LHC community is not the only one that is actively using a distributed computing model (hence the need for a high-performance network); new communities are arising, as BELLE II. GÉANT is deeply involved also with the BELLE II Experiment, to provide full support to their distributed computing model, along with a perfSONAR-based network monitoring system. GÉANT has also coordinated the setup of the network infrastructure to perform the BELLE II Trans-Atlantic Data Challenge, and has been active on helping the BELLE II community to sort out their end-to-end performance issues. In this talk we will provide information about the current GÉANT network architecture and of the international connectivity, along with the upcoming upgrades and the planned and foreseeable improvements. We will also describe the implementation of the solutions provided to support the LHC and BELLE II experiments.

Client/Server data serving for high performance computing

NASA Technical Reports Server (NTRS)

Wood, Chris

1994-01-01

This paper will attempt to examine the industry requirements for shared network data storage and sustained high speed (10's to 100's to thousands of megabytes per second) network data serving via the NFS and FTP protocol suite. It will discuss the current structural and architectural impediments to achieving these sorts of data rates cost effectively today on many general purpose servers and will describe and architecture and resulting product family that addresses these problems. The sustained performance levels that were achieved in the lab will be shown as well as a discussion of early customer experiences utilizing both the HIPPI-IP and ATM OC3-IP network interfaces.

Neural network for control of rearrangeable Clos networks.

PubMed

Park, Y K; Cherkassky, V

1994-09-01

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

Grand Challenges: High Performance Computing and Communications. The FY 1992 U.S. Research and Development Program.

ERIC Educational Resources Information Center

Federal Coordinating Council for Science, Engineering and Technology, Washington, DC.

This report presents a review of the High Performance Computing and Communications (HPCC) Program, which has as its goal the acceleration of the commercial availability and utilization of the next generation of high performance computers and networks in order to: (1) extend U.S. technological leadership in high performance computing and computer…

Classification of Company Performance using Weighted Probabilistic Neural Network

NASA Astrophysics Data System (ADS)

Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

2018-05-01

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

Performance analysis and improvement of WPAN MAC for home networks.

PubMed

Mehta, Saurabh; Kwak, Kyung Sup

2010-01-01

The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking.

Performance Analysis and Improvement of WPAN MAC for Home Networks

PubMed Central

Mehta, Saurabh; Kwak, Kyung Sup

2010-01-01

The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking. PMID:22319274

Construction of Blaze at the University of Illinois at Chicago: A Shared, High-Performance, Visual Computer for Next-Generation Cyberinfrastructure-Accelerated Scientific, Engineering, Medical and Public Policy Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Maxine D.; Leigh, Jason

2014-02-17

The Blaze high-performance visual computing system serves the high-performance computing research and education needs of University of Illinois at Chicago (UIC). Blaze consists of a state-of-the-art, networked, computer cluster and ultra-high-resolution visualization system called CAVE2(TM) that is currently not available anywhere in Illinois. This system is connected via a high-speed 100-Gigabit network to the State of Illinois' I-WIRE optical network, as well as to national and international high speed networks, such as the Internet2, and the Global Lambda Integrated Facility. This enables Blaze to serve as an on-ramp to national cyberinfrastructure, such as the National Science Foundation’s Blue Waters petascalemore » computer at the National Center for Supercomputing Applications at the University of Illinois at Chicago and the Department of Energy’s Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. DOE award # DE-SC005067, leveraged with NSF award #CNS-0959053 for “Development of the Next-Generation CAVE Virtual Environment (NG-CAVE),” enabled us to create a first-of-its-kind high-performance visual computing system. The UIC Electronic Visualization Laboratory (EVL) worked with two U.S. companies to advance their commercial products and maintain U.S. leadership in the global information technology economy. New applications are being enabled with the CAVE2/Blaze visual computing system that is advancing scientific research and education in the U.S. and globally, and help train the next-generation workforce.« less

Applications considerations in the system design of highly concurrent multiprocessors

NASA Technical Reports Server (NTRS)

Lundstrom, Stephen F.

1987-01-01

A flow model processor approach to parallel processing is described, using very-high-performance individual processors, high-speed circuit switched interconnection networks, and a high-speed synchronization capability to minimize the effect of the inherently serial portions of applications on performance. Design studies related to the determination of the number of processors, the memory organization, and the structure of the networks used to interconnect the processor and memory resources are discussed. Simulations indicate that applications centered on the large shared data memory should be able to sustain over 500 million floating point operations per second.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michelogiannakis, George; Ibrahim, Khaled Z.; Shalf, John

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

Stellar Oscillations Network Group

NASA Astrophysics Data System (ADS)

Grundahl, F.; Kjeldsen, H.; Christensen-Dalsgaard, J.; Arentoft, T.; Frandsen, S.

2007-06-01

Stellar Oscillations Network Group (SONG) is an initiative aimed at designing and building a network of 1m-class telescopes dedicated to asteroseismology and planet hunting. SONG will have 8 identical telescope nodes each equipped with a high-resolution spectrograph and an iodine cell for obtaining precision radial velocities and a CCD camera for guiding and imaging purposes. The main asteroseismology targets for the network are the brightest (V < 6) stars. In order to improve performance and reduce maintenance costs the instrumentation will only have very few modes of operation. In this contribution we describe the motivations for establishing a network, the basic outline of SONG and the expected performance.

High-Performance Computing: Industry Uses of Supercomputers and High-Speed Networks. Report to Congressional Requesters.

ERIC Educational Resources Information Center

General Accounting Office, Washington, DC. Information Management and Technology Div.

This report was prepared in response to a request for information on supercomputers and high-speed networks from the Senate Committee on Commerce, Science, and Transportation, and the House Committee on Science, Space, and Technology. The following information was requested: (1) examples of how various industries are using supercomputers to…

Identification of functional modules using network topology and high-throughput data.

PubMed

Ulitsky, Igor; Shamir, Ron

2007-01-26

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data. We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity. We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Interactions Between Structure and Processing that Control Moisture Uptake in High-Performance Polycyanurates (Briefing Charts)

DTIC Science & Technology

2015-03-24

distribution is unlimited.  . Interactions Between Structure and Processing that Control Moisture Uptake in High-Performance Polycyanurates Presenter: Dr...Edwards AFB, CA 4 California State University, Long Beach, CA 90840 2 Outline: Basic Studies of Moisture Uptake in Cyanate Ester Networks • Background...Motivation • SOTA Theories of Moisture Uptake in Thermosetting Networks • New Tools and New Discoveries • Unresolved Issues and Ways to Address Them

High-accuracy local positioning network for the alignment of the Mu2e experiment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hejdukova, Jana B.

This Diploma thesis describes the establishment of a high-precision local positioning network and accelerator alignment for the Mu2e physics experiment. The process of establishing new network consists of few steps: design of the network, pre-analysis, installation works, measurements of the network and making adjustments. Adjustments were performed using two approaches. First is a geodetic approach of taking into account the Earth’s curvature and the metrological approach of a pure 3D Cartesian system on the other side. The comparison of those two approaches is performed and evaluated in the results and compared with expected differences. The effect of the Earth’s curvaturemore » was found to be significant for this kind of network and should not be neglected. The measurements were obtained with Absolute Tracker AT401, leveling instrument Leica DNA03 and gyrotheodolite DMT Gyromat 2000. The coordinates of the points of the reference network were determined by the Least Square Meth od and the overall view is attached as Annexes.« less

Cognitive and default-mode resting state networks: do male and female brains "rest" differently?

PubMed

Weissman-Fogel, Irit; Moayedi, Massieh; Taylor, Keri S; Pope, Geoff; Davis, Karen D

2010-11-01

Variability in human behavior related to sex is supported by neuroimaging studies showing differences in brain activation patterns during cognitive task performance. An emerging field is examining the human connectome, including networks of brain regions that are not only temporally-correlated during different task conditions, but also networks that show highly correlated spontaneous activity during a task-free state. Both task-related and task-free network activity has been associated with individual task performance and behavior under certain conditions. Therefore, our aim was to determine whether sex differences exist during a task-free resting state for two networks associated with cognitive task performance (executive control network (ECN), salience network (SN)) and the default mode network (DMN). Forty-nine healthy subjects (26 females, 23 males) underwent a 5-min task-free fMRI scan in a 3T MRI. An independent components analysis (ICA) was performed to identify the best-fit IC for each network based on specific spatial nodes defined in previous studies. To determine the consistency of these networks across subjects we performed self-organizing group-level ICA analyses. There were no significant differences between sexes in the functional connectivity of the brain areas within the ECN, SN, or the DMN. These important findings highlight the robustness of intrinsic connectivity of these resting state networks and their similarity between sexes. Furthermore, our findings suggest that resting state fMRI studies do not need to be controlled for sex. © 2010 Wiley-Liss, Inc.

Practical End-to-End Performance Testing Tool for High Speed 3G-Based Networks

NASA Astrophysics Data System (ADS)

Shinbo, Hiroyuki; Tagami, Atsushi; Ano, Shigehiro; Hasegawa, Toru; Suzuki, Kenji

High speed IP communication is a killer application for 3rd generation (3G) mobile systems. Thus 3G network operators should perform extensive tests to check whether expected end-to-end performances are provided to customers under various environments. An important objective of such tests is to check whether network nodes fulfill requirements to durations of processing packets because a long duration of such processing causes performance degradation. This requires testers (persons who do tests) to precisely know how long a packet is hold by various network nodes. Without any tool's help, this task is time-consuming and error prone. Thus we propose a multi-point packet header analysis tool which extracts and records packet headers with synchronized timestamps at multiple observation points. Such recorded packet headers enable testers to calculate such holding durations. The notable feature of this tool is that it is implemented on off-the shelf hardware platforms, i.e., lap-top personal computers. The key challenges of the implementation are precise clock synchronization without any special hardware and a sophisticated header extraction algorithm without any drop.

Passive and Active Monitoring on a High Performance Research Network.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matthews, Warren

2001-05-01

The bold network challenges described in ''Internet End-to-end Performance Monitoring for the High Energy and Nuclear Physics Community'' presented at PAM 2000 have been tackled by the intrepid administrators and engineers providing the network services. After less than a year, the BaBar collaboration has collected almost 100 million particle collision events in a database approaching 165TB (Tera=10{sup 12}). Around 20TB has been exported via the Internet to the BaBar regional center at IN2P3 in Lyon, France, for processing and around 40 TB of simulated events have been imported to SLAC from Lawrence Livermore National Laboratory (LLNL). An unforseen challenge hasmore » arisen due to recent events and highlighted security concerns at DoE funded labs. New rules and regulations suggest it is only a matter of time before many active performance measurements may not be possible between many sites. Yet, at the same time, the importance of understanding every aspect of the network and eradicating packet loss for high throughput data transfers has become apparent. Work at SLAC to employ passive monitoring using netflow and OC3MON is underway and techniques to supplement and possibly replace the active measurements are being considered. This paper will detail the special needs and traffic characterization of a remarkable research project, and how the networking hurdles have been resolved (or not!) to achieve the required high data throughput. Results from active and passive measurements will be compared, and methods for achieving high throughput and the effect on the network will be assessed along with tools that directly measure throughput and applications used to actually transfer data.« less

A tough high performance composite matrix

NASA Technical Reports Server (NTRS)

Pater, Ruth H. (Inventor); Johnston, Norman J. (Inventor)

1992-01-01

This invention is a semi-interpenetrating polymer network which includes a high performance thermosetting polyimide having a nadic end group acting as a crosslinking site and a high performance linear thermoplastic polyimide. An improved high temperature matrix resin is provided which is capable of performing in the 200 to 300 C range. This resin has significantly improved toughness and microcracking resistance, excellent processability, mechanical performance and moisture and solvent resistances.

LADS: Optimizing Data Transfers using Layout-Aware Data Scheduling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Youngjae; Atchley, Scott; Vallee, Geoffroy R

While future terabit networks hold the promise of signifi- cantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today s 100 gigabit networks to real- ize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink. Data stor- age infrastructure at both the source and sink and its in- terplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this paper, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network en- vironment, and we presentmore » a new bulk data movement framework called LADS for terabit networks. LADS ex- ploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to use zero-copy, OS-bypass hardware when available. It can further im- prove data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared stor- age resource, improving I/O bandwidth, and data transfer rates across the high speed networks.« less

Mobility based multicast routing in wireless mesh networks

NASA Astrophysics Data System (ADS)

Jain, Sanjeev; Tripathi, Vijay S.; Tiwari, Sudarshan

2013-01-01

There exist two fundamental approaches to multicast routing namely minimum cost trees and shortest path trees. The (MCT's) minimum cost tree is one which connects receiver and sources by providing a minimum number of transmissions (MNTs) the MNTs approach is generally used for energy constraint sensor and mobile ad hoc networks. In this paper we have considered node mobility and try to find out simulation based comparison of the (SPT's) shortest path tree, (MST's) minimum steiner trees and minimum number of transmission trees in wireless mesh networks by using the performance metrics like as an end to end delay, average jitter, throughput and packet delivery ratio, average unicast packet delivery ratio, etc. We have also evaluated multicast performance in the small and large wireless mesh networks. In case of multicast performance in the small networks we have found that when the traffic load is moderate or high the SPTs outperform the MSTs and MNTs in all cases. The SPTs have lowest end to end delay and average jitter in almost all cases. In case of multicast performance in the large network we have seen that the MSTs provide minimum total edge cost and minimum number of transmissions. We have also found that the one drawback of SPTs, when the group size is large and rate of multicast sending is high SPTs causes more packet losses to other flows as MCTs.

Concept development and needs identification for intelligent network flow optimization (INFLO) : functional and performance requirements, and high-level data and communication needs.

DOT National Transportation Integrated Search

2012-11-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

Floating compression of Ag nanowire networks for effective strain release of stretchable transparent electrodes

NASA Astrophysics Data System (ADS)

Pyo, Jun Beom; Kim, Byoung Soo; Park, Hyunchul; Kim, Tae Ann; Koo, Chong Min; Lee, Jonghwi; Son, Jeong Gon; Lee, Sang-Soo; Park, Jong Hyuk

2015-10-01

Manipulation of the configuration of Ag nanowire (NW) networks has been pursued to enhance the performance of stretchable transparent electrodes. However, it has remained challenging due to the high Young's modulus and low yield strain of Ag NWs, which lead to their mechanical failure when subjected to structural deformation. We demonstrate that floating a Ag NW network on water and subsequent in-plane compression allows convenient development of a wavy configuration in the Ag NW network, which can release the applied strain. A greatly enhanced electromechanical stability of Ag NW networks can be achieved due to their wavy configuration, while the NW networks maintain the desirable optical and electrical properties. Moreover, the produced NW networks can be transferred to a variety of substrates, offering flexibility for device fabrication. The Ag NW networks with wavy configurations are used as compliant electrodes for dielectric elastomer actuators. The study demonstrates their promising potential to provide improved performance for soft electronic devices.Manipulation of the configuration of Ag nanowire (NW) networks has been pursued to enhance the performance of stretchable transparent electrodes. However, it has remained challenging due to the high Young's modulus and low yield strain of Ag NWs, which lead to their mechanical failure when subjected to structural deformation. We demonstrate that floating a Ag NW network on water and subsequent in-plane compression allows convenient development of a wavy configuration in the Ag NW network, which can release the applied strain. A greatly enhanced electromechanical stability of Ag NW networks can be achieved due to their wavy configuration, while the NW networks maintain the desirable optical and electrical properties. Moreover, the produced NW networks can be transferred to a variety of substrates, offering flexibility for device fabrication. The Ag NW networks with wavy configurations are used as compliant electrodes for dielectric elastomer actuators. The study demonstrates their promising potential to provide improved performance for soft electronic devices. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr03814f

Network anomaly detection system with optimized DS evidence theory.

PubMed

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

Detection performance of three different lightning location networks in Beijing area based on accurate fast antenna records

NASA Astrophysics Data System (ADS)

Srivastava, A.; Tian, Y.; Wang, D.; Yuan, S.; Chen, Z.; Sun, Z.; Qie, X.

2016-12-01

Scientists have developed the regional and worldwide lightning location network to study the lightning physics and locating the lightning stroke. One of the key issue in all the networks; to recognize the performance of the network. The performance of each network would be different based on the regional geographic conditions and the instrumental limitation. To improve the performance of the network. it is necessary to know the ground truth of the network and to discuss about the detection efficiency (DE) and location accuracy (LA). A comparative study has been discussed among World Wide Lightning Location Network (WWLLN), ADvanced TOA and Direction system (ADTD) and Beijing Lightning NETwork (BLNET) lightning detection network in Beijing area. WWLLN locate the cloud to ground (CG) and strong inter cloud (IC) globally without demonstrating any differences. ADTD locate the CG strokes in the entire China as regional. Both these networks are long range detection system that does not provide the focused details of a thunderstorm. BLNET can locate the CG and IC and is focused on thunderstorm detection. The waveform of fast antenna checked manually and the relative DE among the three networks has been obtained based on the CG strokes. The relative LA has been obtained using the matched flashes among these networks as well as LA obtained using the strike on the tower. The relative DE of BLNET is much higher than the ADTD and WWLLN as these networks has approximately similar relative DE. The relative LA of WWLLN and ADTD location is eastward and northward respectively from the BLNET. The LA based on tower observation is relatively high-quality in favor of BLNET. The ground truth of WWLLN, ADTD and BLNET has been obtained and found the performance of BLNET network is much better. This study is helpful to improve the performance of the networks and to provide a belief of LA that can follow the thunderstorm path with the prediction and forecasting of thunderstorm and lightning.

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

A carrier sensed multiple access protocol for high data base rate ring networks

NASA Technical Reports Server (NTRS)

Foudriat, E. C.; Maly, Kurt J.; Overstreet, C. Michael; Khanna, S.; Paterra, Frank

1990-01-01

The results of the study of a simple but effective media access protocol for high data rate networks are presented. The protocol is based on the fact that at high data rates networks can contain multiple messages simultaneously over their span, and that in a ring, nodes used to detect the presence of a message arriving from the immediate upstream neighbor. When an incoming signal is detected, the node must either abort or truncate a message it is presently sending. Thus, the protocol with local carrier sensing and multiple access is designated CSMA/RN. The performance of CSMA/RN with TTattempt and truncate is studied using analytic and simulation models. Three performance factors, wait or access time, service time and response or end-to-end travel time are presented. The service time is basically a function of the network rate, it changes by a factor of 1 between no load and full load. Wait time, which is zero for no load, remains small for load factors up to 70 percent of full load. Response time, which adds travel time while on the network to wait and service time, is mainly a function of network length, especially for longer distance networks. Simulation results are shown for CSMA/RN where messages are removed at the destination. A wide range of local and metropolitan area network parameters including variations in message size, network length, and node count are studied. Finally, a scaling factor based upon the ratio of message to network length demonstrates that the results, and hence, the CSMA/RN protocol, are applicable to wide area networks.

Topology-Aware Performance Optimization and Modeling of Adaptive Mesh Refinement Codes for Exascale

DOE PAGES

Chan, Cy P.; Bachan, John D.; Kenny, Joseph P.; ...

2017-01-26

Here, we introduce a topology-aware performance optimization and modeling workflow for AMR simulation that includes two new modeling tools, ProgrAMR and Mota Mapper, which interface with the BoxLib AMR framework and the SSTmacro network simulator. ProgrAMR allows us to generate and model the execution of task dependency graphs from high-level specifications of AMR-based applications, which we demonstrate by analyzing two example AMR-based multigrid solvers with varying degrees of asynchrony. Mota Mapper generates multiobjective, network topology-aware box mappings, which we apply to optimize the data layout for the example multigrid solvers. While the sensitivity of these solvers to layout and executionmore » strategy appears to be modest for balanced scenarios, the impact of better mapping algorithms can be significant when performance is highly constrained by network hop latency. Furthermore, we show that network latency in the multigrid bottom solve is the main contributing factor preventing good scaling on exascale-class machines.« less

Topology-Aware Performance Optimization and Modeling of Adaptive Mesh Refinement Codes for Exascale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, Cy P.; Bachan, John D.; Kenny, Joseph P.

Here, we introduce a topology-aware performance optimization and modeling workflow for AMR simulation that includes two new modeling tools, ProgrAMR and Mota Mapper, which interface with the BoxLib AMR framework and the SSTmacro network simulator. ProgrAMR allows us to generate and model the execution of task dependency graphs from high-level specifications of AMR-based applications, which we demonstrate by analyzing two example AMR-based multigrid solvers with varying degrees of asynchrony. Mota Mapper generates multiobjective, network topology-aware box mappings, which we apply to optimize the data layout for the example multigrid solvers. While the sensitivity of these solvers to layout and executionmore » strategy appears to be modest for balanced scenarios, the impact of better mapping algorithms can be significant when performance is highly constrained by network hop latency. Furthermore, we show that network latency in the multigrid bottom solve is the main contributing factor preventing good scaling on exascale-class machines.« less

Three-dimensional interconnected network of graphene-wrapped porous silicon spheres: in situ magnesiothermic-reduction synthesis and enhanced lithium-storage capabilities.

PubMed

Wu, Ping; Wang, Hui; Tang, Yawen; Zhou, Yiming; Lu, Tianhong

2014-03-12

A novel type of 3D porous Si-G micro/nanostructure (i.e., 3D interconnected network of graphene-wrapped porous silicon spheres, Si@G network) was constructed through layer-by-layer assembly and subsequent in situ magnesiothermic-reduction methodology. Compared with bare Si spheres, the as-synthesized Si@G network exhibits markedly enhanced anodic performance in terms of specific capacity, cycling stability, and rate capability, making it an ideal anode candidate for high-energy, long-life, and high-power lithium-ion batteries.

Analysis of multiuser mixed RF/FSO relay networks for performance improvements in Cloud Computing-Based Radio Access Networks (CC-RANs)

NASA Astrophysics Data System (ADS)

Alimi, Isiaka A.; Monteiro, Paulo P.; Teixeira, António L.

2017-11-01

The key paths toward the fifth generation (5G) network requirements are towards centralized processing and small-cell densification systems that are implemented on the cloud computing-based radio access networks (CC-RANs). The increasing recognitions of the CC-RANs can be attributed to their valuable features regarding system performance optimization and cost-effectiveness. Nevertheless, realization of the stringent requirements of the fronthaul that connects the network elements is highly demanding. In this paper, considering the small-cell network architectures, we present multiuser mixed radio-frequency/free-space optical (RF/FSO) relay networks as feasible technologies for the alleviation of the stringent requirements in the CC-RANs. In this study, we use the end-to-end (e2e) outage probability, average symbol error probability (ASEP), and ergodic channel capacity as the performance metrics in our analysis. Simulation results show the suitability of deployment of mixed RF/FSO schemes in the real-life scenarios.

MEG Network Differences between Low- and High-Grade Glioma Related to Epilepsy and Cognition

PubMed Central

van Dellen, Edwin; Douw, Linda; Hillebrand, Arjan; Ris-Hilgersom, Irene H. M.; Schoonheim, Menno M.; Baayen, Johannes C.; De Witt Hamer, Philip C.; Velis, Demetrios N.; Klein, Martin; Heimans, Jan J.; Stam, Cornelis J.; Reijneveld, Jaap C.

2012-01-01

Objective To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups. Methods We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients. Results LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4–8 Hz), similar to NGL patients. HGG patients’ networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients. Conclusion Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients’ networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline. PMID:23166829

High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil

NASA Technical Reports Server (NTRS)

Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)

1998-01-01

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Robust Network Design - Connectivity and Beyond

DTIC Science & Technology

2015-01-15

utilize a heterogeneous set of physical links (RF, Optical/Laser and SATCOM), for interconnecting a set of terrestrial, space and highly mobile airborne...design of mobility patterns of airborne platforms to provide stable operating conditions,  the design of networks that enable graceful performance...research effort, Airborne Network research was primarily directed towards Mobile Ad-hoc Networks (MANET). From our experience in design and

Reviewing the Differences in Size, Composition and Structure between the Personal Networks of High-and Low-Performing Students

ERIC Educational Resources Information Center

Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel

2015-01-01

An interesting aspect in the current literature about learning networks is the shift of focus from the understanding of the "whole network" of a course to the examination of the "personal networks" of individual students. This line of research is relatively new, based on small-scale studies and diverse analysis techniques,…

Initial Characterization of Optical Communications with Disruption-Tolerant Network Protocols

NASA Technical Reports Server (NTRS)

Schoolcraft, Joshua; Wilson, Keith

2011-01-01

Disruption-tolerant networks (DTNs) are groups of network assets connected with a suite of communication protocol technologies designed to mitigate the effects of link delay and disruption. Application of DTN protocols to diverse groups of network resources in multiple sub-networks results in an overlay network-of-networks with autonomous data routing capability. In space environments where delay or disruption is expected, performance of this type of architecture (such as an interplanetary internet) can increase with the inclusion of new communications mediums and techniques. Space-based optical communication links are therefore an excellent building block of space DTN architectures. When compared to traditional radio frequency (RF) communications, optical systems can provide extremely power-efficient and high bandwidth links bridging sub-networks. Because optical links are more susceptible to link disruption and experience the same light-speed delays as RF, optical-enabled DTN architectures can lessen potential drawbacks and maintain the benefits of autonomous optical communications over deep space distances. These environment-driven expectations - link delay and interruption, along with asymmetric data rates - are the purpose of the proof-of-concept experiment outlined herein. In recognizing the potential of these two technologies, we report an initial experiment and characterization of the performance of a DTN-enabled space optical link. The experiment design employs a point-to-point free-space optical link configured to have asymmetric bandwidth. This link connects two networked systems running a DTN protocol implementation designed and written at JPL for use on spacecraft, and further configured for higher bandwidth performance. Comparing baseline data transmission metrics with and without periodic optical link interruptions, the experiment confirmed the DTN protocols' ability to handle real-world unexpected link outages while maintaining capability of reliably delivering data at relatively high rates. Finally, performance characterizations from this data suggest performance optimizations to configuration and protocols for future optical-specific DTN space link scenarios.

The neural basis of impaired self-awareness after traumatic brain injury

PubMed Central

Ham, Timothy E.; Bonnelle, Valerie; Hellyer, Peter; Jilka, Sagar; Robertson, Ian H.; Leech, Robert

2014-01-01

Self-awareness is commonly impaired after traumatic brain injury. This is an important clinical issue as awareness affects long-term outcome and limits attempts at rehabilitation. It can be investigated by studying how patients respond to their errors and monitor their performance on tasks. As awareness is thought to be an emergent property of network activity, we tested the hypothesis that impaired self-awareness is associated with abnormal brain network function. We investigated a group of subjects with traumatic brain injury (n = 63) split into low and high performance-monitoring groups based on their ability to recognize and correct their own errors. Brain network function was assessed using resting-state and event-related functional magnetic resonance imaging. This allowed us to investigate baseline network function, as well as the evoked response of networks to specific events including errors. The low performance-monitoring group underestimated their disability and showed broad attentional deficits. Neural activity within what has been termed the fronto-parietal control network was abnormal in patients with impaired self-awareness. The dorsal anterior cingulate cortex is a key part of this network that is involved in performance-monitoring. This region showed reduced functional connectivity to the rest of the fronto-parietal control network at ‘rest’. In addition, the anterior insulae, which are normally tightly linked to the dorsal anterior cingulate cortex, showed increased activity following errors in the impaired group. Interestingly, the traumatic brain injury patient group with normal performance-monitoring showed abnormally high activation of the right middle frontal gyrus, putamen and caudate in response to errors. The impairment of self-awareness was not explained either by the location of focal brain injury, or the amount of traumatic axonal injury as demonstrated by diffusion tensor imaging. The results suggest that impairments of self-awareness after traumatic brain injury result from breakdown of functional interactions between nodes within the fronto-parietal control network. PMID:24371217

The neural basis of impaired self-awareness after traumatic brain injury.

PubMed

Ham, Timothy E; Bonnelle, Valerie; Hellyer, Peter; Jilka, Sagar; Robertson, Ian H; Leech, Robert; Sharp, David J

2014-02-01

Self-awareness is commonly impaired after traumatic brain injury. This is an important clinical issue as awareness affects long-term outcome and limits attempts at rehabilitation. It can be investigated by studying how patients respond to their errors and monitor their performance on tasks. As awareness is thought to be an emergent property of network activity, we tested the hypothesis that impaired self-awareness is associated with abnormal brain network function. We investigated a group of subjects with traumatic brain injury (n = 63) split into low and high performance-monitoring groups based on their ability to recognize and correct their own errors. Brain network function was assessed using resting-state and event-related functional magnetic resonance imaging. This allowed us to investigate baseline network function, as well as the evoked response of networks to specific events including errors. The low performance-monitoring group underestimated their disability and showed broad attentional deficits. Neural activity within what has been termed the fronto-parietal control network was abnormal in patients with impaired self-awareness. The dorsal anterior cingulate cortex is a key part of this network that is involved in performance-monitoring. This region showed reduced functional connectivity to the rest of the fronto-parietal control network at 'rest'. In addition, the anterior insulae, which are normally tightly linked to the dorsal anterior cingulate cortex, showed increased activity following errors in the impaired group. Interestingly, the traumatic brain injury patient group with normal performance-monitoring showed abnormally high activation of the right middle frontal gyrus, putamen and caudate in response to errors. The impairment of self-awareness was not explained either by the location of focal brain injury, or the amount of traumatic axonal injury as demonstrated by diffusion tensor imaging. The results suggest that impairments of self-awareness after traumatic brain injury result from breakdown of functional interactions between nodes within the fronto-parietal control network.

Network bandwidth utilization forecast model on high bandwidth networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wuchert; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

A Culture of High Expectations: Teacher Leadership at Pritzker College Prep

ERIC Educational Resources Information Center

Aspen Institute, 2014

2014-01-01

Relying on teachers as culture leaders is a solution embraced by many high-performing charter schools. This profile focuses on the design of the Grade Level Lead roles at Pritzker College Prep, a member of the Noble Network of Schools in Chicago. The successes of this school and network are well-documented: Of non-selective public high schools in…

High-Performance Computing: High-Speed Computer Networks in the United States, Europe, and Japan. Report to Congressional Requesters.

ERIC Educational Resources Information Center

General Accounting Office, Washington, DC. Information Management and Technology Div.

This report was prepared in response to a request from the Senate Committee on Commerce, Science, and Transportation, and from the House Committee on Science, Space, and Technology, for information on efforts to develop high-speed computer networks in the United States, Europe (limited to France, Germany, Italy, the Netherlands, and the United…

Peregrine System Configuration | High-Performance Computing | NREL

Science.gov Websites

nodes and storage are connected by a high speed InfiniBand network. Compute nodes are diskless with an directories are mounted on all nodes, along with a file system dedicated to shared projects. A brief processors with 64 GB of memory. All nodes are connected to the high speed Infiniband network and and a

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

Scientific Visualization in High Speed Network Environments

NASA Technical Reports Server (NTRS)

Vaziri, Arsi; Kutler, Paul (Technical Monitor)

1997-01-01

In several cases, new visualization techniques have vastly increased the researcher's ability to analyze and comprehend data. Similarly, the role of networks in providing an efficient supercomputing environment have become more critical and continue to grow at a faster rate than the increase in the processing capabilities of supercomputers. A close relationship between scientific visualization and high-speed networks in providing an important link to support efficient supercomputing is identified. The two technologies are driven by the increasing complexities and volume of supercomputer data. The interaction of scientific visualization and high-speed networks in a Computational Fluid Dynamics simulation/visualization environment are given. Current capabilities supported by high speed networks, supercomputers, and high-performance graphics workstations at the Numerical Aerodynamic Simulation Facility (NAS) at NASA Ames Research Center are described. Applied research in providing a supercomputer visualization environment to support future computational requirements are summarized.

Programmable on-chip and off-chip network architecture on demand for flexible optical intra-datacenters.

PubMed

Rofoee, Bijan Rahimzadeh; Zervas, Georgios; Yan, Yan; Amaya, Norberto; Qin, Yixuan; Simeonidou, Dimitra

2013-03-11

The paper presents a novel network architecture on demand approach using on-chip and-off chip implementations, enabling programmable, highly efficient and transparent networking, well suited for intra-datacenter communications. The implemented FPGA-based adaptable line-card with on-chip design along with an architecture on demand (AoD) based off-chip flexible switching node, deliver single chip dual L2-Packet/L1-time shared optical network (TSON) server Network Interface Cards (NIC) interconnected through transparent AoD based switch. It enables hitless adaptation between Ethernet over wavelength switched network (EoWSON), and TSON based sub-wavelength switching, providing flexible bitrates, while meeting strict bandwidth, QoS requirements. The on and off-chip performance results show high throughput (9.86Ethernet, 8.68Gbps TSON), high QoS, as well as hitless switch-over.

Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

PubMed

Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

2015-01-01

Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system throughput performance.

The Robustness Analysis of Wireless Sensor Networks under Uncertain Interference

PubMed Central

Deng, Changjian

2013-01-01

Based on the complex network theory, robustness analysis of condition monitoring wireless sensor network under uncertain interference is present. In the evolution of the topology of sensor networks, the density weighted algebraic connectivity is taken into account, and the phenomenon of removing and repairing the link and node in the network is discussed. Numerical simulation is conducted to explore algebraic connectivity characteristics and network robustness performance. It is found that nodes density has the effect on algebraic connectivity distribution in the random graph model; high density nodes carry more connections, use more throughputs, and may be more unreliable. Moreover, the results show that, when network should be more error tolerant or robust by repairing nodes or adding new nodes, the network should be better clustered in median and high scale wireless sensor networks and be meshing topology in small scale networks. PMID:24363613

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

Network survivability performance (computer diskette)

NASA Astrophysics Data System (ADS)

1993-11-01

File characteristics: Data file; 1 file. Physical description: 1 computer diskette; 3 1/2 in.; high density; 2.0MB. System requirements: Mac; Word. This technical report has been developed to address the survivability of telecommunications networks including services. It responds to the need for a common understanding of, and assessment techniques for network survivability, availability, integrity, and reliability. It provides a basis for designing and operating telecommunication networks to user expectations for network survivability.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-03-01

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

An adaptive cryptographic accelerator for network storage security on dynamically reconfigurable platform

NASA Astrophysics Data System (ADS)

Tang, Li; Liu, Jing-Ning; Feng, Dan; Tong, Wei

2008-12-01

Existing security solutions in network storage environment perform poorly because cryptographic operations (encryption and decryption) implemented in software can dramatically reduce system performance. In this paper we propose a cryptographic hardware accelerator on dynamically reconfigurable platform for the security of high performance network storage system. We employ a dynamic reconfigurable platform based on a FPGA to implement a PowerPCbased embedded system, which executes cryptographic algorithms. To reduce the reconfiguration latency, we apply prefetch scheduling. Moreover, the processing elements could be dynamically configured to support different cryptographic algorithms according to the request received by the accelerator. In the experiment, we have implemented AES (Rijndael) and 3DES cryptographic algorithms in the reconfigurable accelerator. Our proposed reconfigurable cryptographic accelerator could dramatically increase the performance comparing with the traditional software-based network storage systems.

A Simulation Study of Paced TCP

NASA Technical Reports Server (NTRS)

Kulik, Joanna; Coulter, Robert; Rockwell, Dennis; Partridge, Craig

2000-01-01

In this paper, we study the performance of paced TCP, a modified version of TCP designed especially for high delay- bandwidth networks. In typical networks, TCP optimizes its send-rate by transmitting increasingly large bursts, or windows, of packets, one burst per round-trip time, until it reaches a maximum window-size, which corresponds to the full capacity of the network. In a network with a high delay-bandwidth product, however, Transmission Control Protocol's (TCPs) maximum window-size may be larger than the queue size of the intermediate routers, and routers will begin to drop packets as soon as the windows become too large for the router queues. The TCP sender then concludes that the bottleneck capacity of the network has been reached, and it limits its send-rate accordingly. Partridge proposed paced TCP as a means of solving the problem of queueing bottlenecks. A sender using paced TCP would release packets in multiple, small bursts during a round-trip time in which ordinary TCP would release a single, large burst of packets. This approach allows the sender to increase its send-rate to the maximum window size without encountering queueing bottlenecks. This paper describes the performance of paced TCP in a simulated network and discusses implementation details that can affect the performance of paced TCP.

Robustness of airline route networks

NASA Astrophysics Data System (ADS)

Lordan, Oriol; Sallan, Jose M.; Escorihuela, Nuria; Gonzalez-Prieto, David

2016-03-01

Airlines shape their route network by defining their routes through supply and demand considerations, paying little attention to network performance indicators, such as network robustness. However, the collapse of an airline network can produce high financial costs for the airline and all its geographical area of influence. The aim of this study is to analyze the topology and robustness of the network route of airlines following Low Cost Carriers (LCCs) and Full Service Carriers (FSCs) business models. Results show that FSC hubs are more central than LCC bases in their route network. As a result, LCC route networks are more robust than FSC networks.

Impact of leakage delay on bifurcation in high-order fractional BAM neural networks.

PubMed

Huang, Chengdai; Cao, Jinde

2018-02-01

The effects of leakage delay on the dynamics of neural networks with integer-order have lately been received considerable attention. It has been confirmed that fractional neural networks more appropriately uncover the dynamical properties of neural networks, but the results of fractional neural networks with leakage delay are relatively few. This paper primarily concentrates on the issue of bifurcation for high-order fractional bidirectional associative memory(BAM) neural networks involving leakage delay. The first attempt is made to tackle the stability and bifurcation of high-order fractional BAM neural networks with time delay in leakage terms in this paper. The conditions for the appearance of bifurcation for the proposed systems with leakage delay are firstly established by adopting time delay as a bifurcation parameter. Then, the bifurcation criteria of such system without leakage delay are successfully acquired. Comparative analysis wondrously detects that the stability performance of the proposed high-order fractional neural networks is critically weakened by leakage delay, they cannot be overlooked. Numerical examples are ultimately exhibited to attest the efficiency of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transferable self-welding silver nanowire network as high performance transparent flexible electrode.

PubMed

Zhu, Siwei; Gao, Yuan; Hu, Bin; Li, Jia; Su, Jun; Fan, Zhiyong; Zhou, Jun

2013-08-23

High performance transparent electrodes (TEs) with figures-of-merit as high as 471 were assembled using ultralong silver nanowires (Ag NWs). A room-temperature plasma was employed to enhance the conductivity of the Ag NW TEs by simultaneously removing the insulating PVP layer coating on the NWs and welding the junctions tightly. Furthermore, we developed a general way to fabricate TEs regardless of substrate limitations by transferring the as-fabricated Ag NW network onto various substrates directly, and the transmittance can remain as high as 91% with a sheet resistivity of 13 Ω/sq. The highly robust and stable flexible TEs will have broad applications in flexible optoelectronic and electronic devices.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Network Anomaly Detection System with Optimized DS Evidence Theory

PubMed Central

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

Brain Modularity Mediates the Relation between Task Complexity and Performance

NASA Astrophysics Data System (ADS)

Ye, Fengdan; Yue, Qiuhai; Martin, Randi; Fischer-Baum, Simon; Ramos-Nuã+/-Ez, Aurora; Deem, Michael

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases, and other tasks showing worse performance. A recent theoretical model suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of behavioral tasks. Complex and simple tasks were defined on the basis of whether they drew on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on the complex tasks but a positive correlation with performance on the simple tasks. The results presented here provide a framework for linking measures of whole brain organization to cognitive processing.

Germanium microflower-on-nanostem as a high-performance lithium ion battery electrode

PubMed Central

Lee, Gwang-Hee; Kwon, S. Joon; Park, Kyung-Soo; Kang, Jin-Gu; Park, Jae-Gwan; Lee, Sungjun; Kim, Jae-Chan; Shim, Hyun-Woo; Kim, Dong-Wan

2014-01-01

We demonstrate a new design of Ge-based electrodes comprising three-dimensional (3-D) spherical microflowers containing crystalline nanorod networks on sturdy 1-D nanostems directly grown on a metallic current collector by facile thermal evaporation. The Ge nanorod networks were observed to self-replicate their tetrahedron structures and form a diamond cubic lattice-like inner network. After etching and subsequent carbon coating, the treated Ge nanostructures provide good electrical conductivity and are resistant to gradual deterioration, resulting in superior electrochemical performance as anode materials for LIBs, with a charge capacity retention of 96% after 100 cycles and a high specific capacity of 1360 mA h g−1 at 1 C and a high-rate capability with reversible capacities of 1080 and 850 mA h g−1 at the rates of 5 and 10 C, respectively. The improved electrochemical performance can be attributed to the fast electron transport and good strain accommodation of the carbon-filled Ge microflower-on-nanostem hybrid electrode. PMID:25363317

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

CdS-Nanowires Flexible Photo-detector with Ag-Nanowires Electrode Based on Non-transfer Process

PubMed Central

Pei, Yanli; Pei, Ruihan; Liang, Xiaoci; Wang, Yuhao; Liu, Ling; Chen, Haibiao; Liang, Jun

2016-01-01

In this study, UV-visible flexible resistivity-type photo-detectors were demonstrated with CdS-nanowires (NWs) percolation network channel and Ag-NWs percolation network electrode. The devices were fabricated on Mixed Cellulose Esters (MCE) membrane using a lithographic filtration method combined with a facile non-transfer process. The photo-detectors demonstrated strong adhesion, fast response time, fast decay time, and high photo sensitivity. The high performance could be attributed to the high quality single crystalline CdS-NWs, encapsulation of NWs in MCE matrix and excellent interconnection of the NWs. Furthermore, the sensing performance was maintained even the device was bent at an angle of 90°. This research may pave the way for the facile fabrication of flexible photo-detectors with high performances. PMID:26899726

An Overview of High-performance Parallel Big Data transfers over multiple network channels with Transport Layer Security (TLS) and TLS plus Perfect Forward Secrecy (PFS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Chin; Corttrell, R. A.

This Technical Note provides an overview of high-performance parallel Big Data transfers with and without encryption for data in-transit over multiple network channels. It shows that with the parallel approach, it is feasible to carry out high-performance parallel "encrypted" Big Data transfers without serious impact to throughput. But other impacts, e.g. the energy-consumption part should be investigated. It also explains our rationales of using a statistics-based approach for gaining understanding from test results and for improving the system. The presentation is of high-level nature. Nevertheless, at the end we will pose some questions and identify potentially fruitful directions for futuremore » work.« less

Neural Network Model For Fast Learning And Retrieval

NASA Astrophysics Data System (ADS)

Arsenault, Henri H.; Macukow, Bohdan

1989-05-01

An approach to learning in a multilayer neural network is presented. The proposed network learns by creating interconnections between the input layer and the intermediate layer. In one of the new storage prescriptions proposed, interconnections are excitatory (positive) only and the weights depend on the stored patterns. In the intermediate layer each mother cell is responsible for one stored pattern. Mutually interconnected neurons in the intermediate layer perform a winner-take-all operation, taking into account correlations between stored vectors. The performance of networks using this interconnection prescription is compared with two previously proposed schemes, one using inhibitory connections at the output and one using all-or-nothing interconnections. The network can be used as a content-addressable memory or as a symbolic substitution system that yields an arbitrarily defined output for any input. The training of a model to perform Boolean logical operations is also described. Computer simulations using the network as an autoassociative content-addressable memory show the model to be efficient. Content-addressable associative memories and neural logic modules can be combined to perform logic operations on highly corrupted data.

Principle and verification of novel optical virtual private networks over multiprotocol label switching/optical packet switching networks

NASA Astrophysics Data System (ADS)

Zhang, Chongfu; Wang, Zhengsuan; Jin, Wei; Qiu, Kun

2012-11-01

A novel realization method of the optical virtual private networks (OVPN) over multiprotocol label switching/optical packet switching (MPLS/OPS) networks is proposed. In this scheme, the introduction of MPLS control plane makes OVPN over OPS networks more reliable and easier; OVPN makes use of the concept of high reconfiguration of light-paths offered by MPLS, to set up secure tunnels of high bandwidth across intelligent OPS networks. Through resource management, the signal mechanism, connection control, and the architecture of the creation and maintenance of OVPN are efficiently realized. We also present an OVPN architecture with two traffic priorities, which is used to analyze the capacity, throughput, delay time of the proposed networks, and the packet loss rate performance of the OVPN over MPLS/OPS networks based on full mesh topology. The results validate the applicability of such reliable connectivity to high quality services in the OVPN over MPLS/OPS networks. Along with the results, the feasibility of the approach as the basis for the next generation networks is demonstrated and discussed.

Semiconducting carbon nanotube network thin-film transistors with enhanced inkjet-printed source and drain contact interfaces

NASA Astrophysics Data System (ADS)

Lee, Yongwoo; Yoon, Jinsu; Choi, Bongsik; Lee, Heesung; Park, Jinhee; Jeon, Minsu; Han, Jungmin; Lee, Jieun; Kim, Yeamin; Kim, Dae Hwan; Kim, Dong Myong; Choi, Sung-Jin

2017-10-01

Carbon nanotubes (CNTs) are emerging materials for semiconducting channels in high-performance thin-film transistor (TFT) technology. However, there are concerns regarding the contact resistance (Rcontact) in CNT-TFTs, which limits the ultimate performance, especially the CNT-TFTs with the inkjet-printed source/drain (S/D) electrodes. Thus, the contact interfaces comprising the overlap between CNTs and metal S/D electrodes play a particularly dominant role in determining the performances and degree of variability in the CNT-TFTs with inkjet-printed S/D electrodes. In this work, the CNT-TFTs with improved device performance are demonstrated to enhance contact interfaces by controlling the CNT density at the network channel and underneath the inkjet-printed S/D electrodes during the formation of a CNT network channel. The origin of the improved device performance was systematically investigated by extracting Rcontact in the CNT-TFTs with the enhanced contact interfaces by depositing a high density of CNTs underneath the S/D electrodes, resulting in a 59% reduction in Rcontact; hence, the key performance metrics were correspondingly improved without sacrificing any other device metrics.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

A comparative study of 11 local health department organizational networks.

PubMed

Merrill, Jacqueline; Keeling, Jonathan W; Carley, Kathleen M

2010-01-01

Although the nation's local health departments (LHDs) share a common mission, variability in administrative structures is a barrier to identifying common, optimal management strategies. There is a gap in understanding what unifying features LHDs share as organizations that could be leveraged systematically for achieving high performance. To explore sources of commonality and variability in a range of LHDs by comparing intraorganizational networks. We used organizational network analysis to document relationships between employees, tasks, knowledge, and resources within LHDs, which may exist regardless of formal administrative structure. A national sample of 11 LHDs from seven states that differed in size, geographic location, and governance. Relational network data were collected via an on-line survey of all employees in 11 LHDs. A total of 1062 out of 1239 employees responded (84% response rate). Network measurements were compared using coefficient of variation. Measurements were correlated with scores from the National Public Health Performance Assessment and with LHD demographics. Rankings of tasks, knowledge, and resources were correlated across pairs of LHDs. We found that 11 LHDs exhibited compound organizational structures in which centralized hierarchies were coupled with distributed networks at the point of service. Local health departments were distinguished from random networks by a pattern of high centralization and clustering. Network measurements were positively associated with performance for 3 of 10 essential services (r > 0.65). Patterns in the measurements suggest how LHDs adapt to the population served. Shared network patterns across LHDs suggest where common organizational management strategies are feasible. This evidence supports national efforts to promote uniform standards for service delivery to diverse populations.

Design and implementation of a high performance network security processor

NASA Astrophysics Data System (ADS)

Wang, Haixin; Bai, Guoqiang; Chen, Hongyi

2010-03-01

The last few years have seen many significant progresses in the field of application-specific processors. One example is network security processors (NSPs) that perform various cryptographic operations specified by network security protocols and help to offload the computation intensive burdens from network processors (NPs). This article presents a high performance NSP system architecture implementation intended for both internet protocol security (IPSec) and secure socket layer (SSL) protocol acceleration, which are widely employed in virtual private network (VPN) and e-commerce applications. The efficient dual one-way pipelined data transfer skeleton and optimised integration scheme of the heterogenous parallel crypto engine arrays lead to a Gbps rate NSP, which is programmable with domain specific descriptor-based instructions. The descriptor-based control flow fragments large data packets and distributes them to the crypto engine arrays, which fully utilises the parallel computation resources and improves the overall system data throughput. A prototyping platform for this NSP design is implemented with a Xilinx XC3S5000 based FPGA chip set. Results show that the design gives a peak throughput for the IPSec ESP tunnel mode of 2.85 Gbps with over 2100 full SSL handshakes per second at a clock rate of 95 MHz.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

PubMed

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

PubMed Central

Cheung, Kit; Schultz, Simon R.; Luk, Wayne

2016-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542

Ultrascale collaborative visualization using a display-rich global cyberinfrastructure.

PubMed

Jeong, Byungil; Leigh, Jason; Johnson, Andrew; Renambot, Luc; Brown, Maxine; Jagodic, Ratko; Nam, Sungwon; Hur, Hyejung

2010-01-01

The scalable adaptive graphics environment (SAGE) is high-performance graphics middleware for ultrascale collaborative visualization using a display-rich global cyberinfrastructure. Dozens of sites worldwide use this cyberinfrastructure middleware, which connects high-performance-computing resources over high-speed networks to distributed ultraresolution displays.

Department of Defense High Performance Computing Modernization Program. 2008 Annual Report

DTIC Science & Technology

2009-04-01

place to another on the network. Without it, a computer could only talk to itself - no email, no web browsing, and no iTunes . Most of the Internet...Your SecurID Card ), Ken Renard Secure Wireless, Rob Scott and Stephen Bowman Securing Today’s Networks, Rich Whittney, Juniper Networks, Federal

LLL Octopus network: some lessons and future directions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watson, R.W.

1978-06-27

The Octopus network, designed and developed by the Lawrence Livermore Laboratory, is a pioneering, high-performance, local computer network. Several lessons derived from the 14 years of experience in the evolution of Octopus are described, and some of the directions to be taken in its medium-term future are indicated. 3 figures.

Going Social: The Impact of Social Networking in Promoting Education

ERIC Educational Resources Information Center

Jain, Neelesh Kumar; Verma, Ashish; Verma, Rama Shankar; Tiwari, Prashant

2012-01-01

The growth and the popularity of the Social networks has a high impact on the development of the students in the field of Personality, Attitudes, Knowledge and on its whole academic performance in classroom and society. This paper envisage on the impact of Social Network on Education and Training of the students.

Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance.

PubMed

Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

2017-03-14

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance

PubMed Central

Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

2017-01-01

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization. DOI: http://dx.doi.org/10.7554/eLife.22001.001 PMID:28288700

Sign: large-scale gene network estimation environment for high performance computing.

PubMed

Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks.

PubMed

Jurdak, Raja; Nafaa, Abdelhamid; Barbirato, Alessio

2008-11-24

Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

Semantic Segmentation of Convolutional Neural Network for Supervised Classification of Multispectral Remote Sensing

NASA Astrophysics Data System (ADS)

Xue, L.; Liu, C.; Wu, Y.; Li, H.

2018-04-01

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

Experiments with arbitrary networks in time-multiplexed delay systems

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Schmadel, Don C.; Murphy, Thomas E.; Roy, Rajarshi

2017-12-01

We report a new experimental approach using an optoelectronic feedback loop to investigate the dynamics of oscillators coupled on large complex networks with arbitrary topology. Our implementation is based on a single optoelectronic feedback loop with time delays. We use the space-time interpretation of systems with time delay to create large networks of coupled maps. Others have performed similar experiments using high-pass filters to implement the coupling; this restricts the network topology to the coupling of only a few nearest neighbors. In our experiment, the time delays and coupling are implemented on a field-programmable gate array, allowing the creation of networks with arbitrary coupling topology. This system has many advantages: the network nodes are truly identical, the network is easily reconfigurable, and the network dynamics occur at high speeds. We use this system to study cluster synchronization and chimera states in both small and large networks of different topologies.

Ad hoc Laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Ma, Zongfeng; Shen, Jingshi

2016-03-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

Ad hoc laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Shen, Jingshi

2017-10-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

High-performance mass storage system for workstations

NASA Technical Reports Server (NTRS)

Chiang, T.; Tang, Y.; Gupta, L.; Cooperman, S.

1993-01-01

Reduced Instruction Set Computer (RISC) workstations and Personnel Computers (PC) are very popular tools for office automation, command and control, scientific analysis, database management, and many other applications. However, when using Input/Output (I/O) intensive applications, the RISC workstations and PC's are often overburdened with the tasks of collecting, staging, storing, and distributing data. Also, by using standard high-performance peripherals and storage devices, the I/O function can still be a common bottleneck process. Therefore, the high-performance mass storage system, developed by Loral AeroSys' Independent Research and Development (IR&D) engineers, can offload a RISC workstation of I/O related functions and provide high-performance I/O functions and external interfaces. The high-performance mass storage system has the capabilities to ingest high-speed real-time data, perform signal or image processing, and stage, archive, and distribute the data. This mass storage system uses a hierarchical storage structure, thus reducing the total data storage cost, while maintaining high-I/O performance. The high-performance mass storage system is a network of low-cost parallel processors and storage devices. The nodes in the network have special I/O functions such as: SCSI controller, Ethernet controller, gateway controller, RS232 controller, IEEE488 controller, and digital/analog converter. The nodes are interconnected through high-speed direct memory access links to form a network. The topology of the network is easily reconfigurable to maximize system throughput for various applications. This high-performance mass storage system takes advantage of a 'busless' architecture for maximum expandability. The mass storage system consists of magnetic disks, a WORM optical disk jukebox, and an 8mm helical scan tape to form a hierarchical storage structure. Commonly used files are kept in the magnetic disk for fast retrieval. The optical disks are used as archive media, and the tapes are used as backup media. The storage system is managed by the IEEE mass storage reference model-based UniTree software package. UniTree software will keep track of all files in the system, will automatically migrate the lesser used files to archive media, and will stage the files when needed by the system. The user can access the files without knowledge of their physical location. The high-performance mass storage system developed by Loral AeroSys will significantly boost the system I/O performance and reduce the overall data storage cost. This storage system provides a highly flexible and cost-effective architecture for a variety of applications (e.g., realtime data acquisition with a signal and image processing requirement, long-term data archiving and distribution, and image analysis and enhancement).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align andmore » transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections.« less

Co-Percolating Graphene-Wrapped Silver Nanowire Network for High Performance, Highly Stable, Transparent Conducting Electrodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Ruiyi; Das, Suprem R.; Jeong, Changwook

Transparent conducting electrodes (TCEs) require high transparency and low sheet resistance for applications in photovoltaics, photodetectors, flat panel displays, touch screen devices and imagers. Indium tin oxide (ITO), or other transparent conductive oxides, have typically been used, and provide a baseline sheet resistance (RS) vs. transparency (T) relationship. However, ITO is relatively expensive (due to limited abundance of Indium), brittle, unstable, and inflexible; moreover, ITO transparency drops rapidly for wavelengths above 1000 nm. Motivated by a need for transparent conductors with comparable (or better) RS at a given T, as well as flexible structures, several alternative material systems have beenmore » investigated. Single-layer graphene (SLG) or few-layer graphene provide sufficiently high transparency (≈97% per layer) to be a potential replacement for ITO. However, large-area synthesis approaches, including chemical vapor deposition (CVD), typically yield films with relatively high sheet resistance due to small grain sizes and high-resistance grain boundaries (HGBs). In this paper, we report a hybrid structure employing a CVD SLG film and a network of silver nanowires (AgNWs): RS as low as 22 Ω/ (stabilized to 13 Ω/ after 4 months) have been observed at high transparency (88% at λ = 550 nm) in hybrid structures employing relatively low-cost commercial graphene with a starting RS of 770 Ω/. This sheet resistance is superior to typical reported values for ITO, comparable to the best reported TCEs employing graphene and/or random nanowire networks, and the film properties exhibit impressive stability under mechanical pressure, mechanical bending and over time. The design is inspired by the theory of a co-percolating network where conduction bottlenecks of a 2D film (e.g., SLG, MoS2) are circumvented by a 1D network (e.g., AgNWs, CNTs) and vice versa. The development of these high-performance hybrid structures provides a route towards robust, scalable and low-cost approaches for realizing high-performance TCE.« less

Simple arithmetic: not so simple for highly math anxious individuals.

PubMed

Chang, Hyesang; Sprute, Lisa; Maloney, Erin A; Beilock, Sian L; Berman, Marc G

2017-12-01

Fluency with simple arithmetic, typically achieved in early elementary school, is thought to be one of the building blocks of mathematical competence. Behavioral studies with adults indicate that math anxiety (feelings of tension or apprehension about math) is associated with poor performance on cognitively demanding math problems. However, it remains unclear whether there are fundamental differences in how high and low math anxious individuals approach overlearned simple arithmetic problems that are less reliant on cognitive control. The current study used functional magnetic resonance imaging to examine the neural correlates of simple arithmetic performance across high and low math anxious individuals. We implemented a partial least squares analysis, a data-driven, multivariate analysis method to measure distributed patterns of whole-brain activity associated with performance. Despite overall high simple arithmetic performance across high and low math anxious individuals, performance was differentially dependent on the fronto-parietal attentional network as a function of math anxiety. Specifically, low-compared to high-math anxious individuals perform better when they activate this network less-a potential indication of more automatic problem-solving. These findings suggest that low and high math anxious individuals approach even the most fundamental math problems differently. © The Author (2017). Published by Oxford University Press.

Coupled Triboelectric Nanogenerator Networks for Efficient Water Wave Energy Harvesting.

PubMed

Xu, Liang; Jiang, Tao; Lin, Pei; Shao, Jia Jia; He, Chuan; Zhong, Wei; Chen, Xiang Yu; Wang, Zhong Lin

2018-02-27

Water wave energy is a promising clean energy source, which is abundant but hard to scavenge economically. Triboelectric nanogenerator (TENG) networks provide an effective approach toward massive harvesting of water wave energy in oceans. In this work, a coupling design in TENG networks for such purposes is reported. The charge output of the rationally linked units is over 10 times of that without linkage. TENG networks of three different connecting methods are fabricated and show better performance for the ones with flexible connections. The network is based on an optimized ball-shell structured TENG unit with high responsivity to small agitations. The dynamic behavior of single and multiple TENG units is also investigated comprehensively to fully understand their performance in water. The study shows that a rational design on the linkage among the units could be an effective strategy for TENG clusters to operate collaboratively for reaching a higher performance.

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.

PubMed

Martínez-Blanco, Ma Del Rosario; Ornelas-Vargas, Gerardo; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, José M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

2016-11-01

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN. Copyright © 2016 Elsevier Ltd. All rights reserved.

Integrated healthcare networks' performance: a growth curve modeling approach.

PubMed

Wan, Thomas T H; Wang, Bill B L

2003-05-01

This study examines the effects of integration on the performance ratings of the top 100 integrated healthcare networks (IHNs) in the United States. A strategic-contingency theory is used to identify the relationship of IHNs' performance to their structural and operational characteristics and integration strategies. To create a database for the panel study, the top 100 IHNs selected by the SMG Marketing Group in 1998 were followed up in 1999 and 2000. The data were merged with the Dorenfest data on information system integration. A growth curve model was developed and validated by the Mplus statistical program. Factors influencing the top 100 IHNs' performance in 1998 and their subsequent rankings in the consecutive years were analyzed. IHNs' initial performance scores were positively influenced by network size, number of affiliated physicians and profit margin, and were negatively associated with average length of stay and technical efficiency. The continuing high performance, judged by maintaining higher performance scores, tended to be enhanced by the use of more managerial or executive decision-support systems. Future studies should include time-varying operational indicators to serve as predictors of network performance.

Hyperswitch Communication Network Computer

NASA Technical Reports Server (NTRS)

Peterson, John C.; Chow, Edward T.; Priel, Moshe; Upchurch, Edwin T.

1993-01-01

Hyperswitch Communications Network (HCN) computer is prototype multiple-processor computer being developed. Incorporates improved version of hyperswitch communication network described in "Hyperswitch Network For Hypercube Computer" (NPO-16905). Designed to support high-level software and expansion of itself. HCN computer is message-passing, multiple-instruction/multiple-data computer offering significant advantages over older single-processor and bus-based multiple-processor computers, with respect to price/performance ratio, reliability, availability, and manufacturing. Design of HCN operating-system software provides flexible computing environment accommodating both parallel and distributed processing. Also achieves balance among following competing factors; performance in processing and communications, ease of use, and tolerance of (and recovery from) faults.

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

PubMed

Jordan, Jakob; Ippen, Tammo; Helias, Moritz; Kitayama, Itaru; Sato, Mitsuhisa; Igarashi, Jun; Diesmann, Markus; Kunkel, Susanne

2018-01-01

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers

PubMed Central

Jordan, Jakob; Ippen, Tammo; Helias, Moritz; Kitayama, Itaru; Sato, Mitsuhisa; Igarashi, Jun; Diesmann, Markus; Kunkel, Susanne

2018-01-01

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems. PMID:29503613

Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure

DOE PAGES

Kim, Youngjae; Atchley, Scott; Vallee, Geoffroy R.; ...

2016-04-05

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this study, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulkmore » data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. Finally, with our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.« less

Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Youngjae; Atchley, Scott; Vallee, Geoffroy R.

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this study, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulkmore » data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. Finally, with our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.« less

Transparent, Flexible Silicon Nanostructured Wire Networks with Seamless Junctions for High-Performance Photodetector Applications.

PubMed

Hossain, Mozakkar; Kumar, Gundam Sandeep; Barimar Prabhava, S N; Sheerin, Emmet D; McCloskey, David; Acharya, Somobrata; Rao, K D M; Boland, John J

2018-05-22

Optically transparent photodetectors are crucial in next-generation optoelectronic applications including smart windows and transparent image sensors. Designing photodetectors with high transparency, photoresponsivity, and robust mechanical flexibility remains a significant challenge, as is managing the inevitable trade-off between high transparency and strong photoresponse. Here we report a scalable method to produce flexible crystalline Si nanostructured wire (NW) networks fabricated from silicon-on-insulator (SOI) with seamless junctions and highly responsive porous Si segments that combine to deliver exceptional performance. These networks show high transparency (∼92% at 550 nm), broadband photodetection (350 to 950 nm) with excellent responsivity (25 A/W), optical response time (0.58 ms), and mechanical flexibility (1000 cycles). Temperature-dependent photocurrent measurements indicate the presence of localized electronic states in the porous Si segments, which play a crucial role in light harvesting and photocarrier generation. The scalable low-cost approach based on SOI has the potential to deliver new classes of flexible optoelectronic devices, including next-generation photodetectors and solar cells.

Particle simulation on heterogeneous distributed supercomputers

NASA Technical Reports Server (NTRS)

Becker, Jeffrey C.; Dagum, Leonardo

1993-01-01

We describe the implementation and performance of a three dimensional particle simulation distributed between a Thinking Machines CM-2 and a Cray Y-MP. These are connected by a combination of two high-speed networks: a high-performance parallel interface (HIPPI) and an optical network (UltraNet). This is the first application to use this configuration at NASA Ames Research Center. We describe our experience implementing and using the application and report the results of several timing measurements. We show that the distribution of applications across disparate supercomputing platforms is feasible and has reasonable performance. In addition, several practical aspects of the computing environment are discussed.

Japanese Ubiquotous Network Project: Ubila

NASA Astrophysics Data System (ADS)

Ohashi, Masayoshi

Recently, the advent of sophisticated technologies has stimulated ambient paradigms that may include high-performance CPU, compact real-time operating systems, a variety of devices/sensors, low power and high-speed radio communications, and in particular, third generation mobile phones. In addition, due to the spread of broadband ccess networks, various ubiquitous terminals and sensors can be connected closely.

Prediction of missing links and reconstruction of complex networks

NASA Astrophysics Data System (ADS)

Zhang, Cheng-Jun; Zeng, An

2016-04-01

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

A High Performance VLSI Computer Architecture For Computer Graphics

NASA Astrophysics Data System (ADS)

Chin, Chi-Yuan; Lin, Wen-Tai

1988-10-01

A VLSI computer architecture, consisting of multiple processors, is presented in this paper to satisfy the modern computer graphics demands, e.g. high resolution, realistic animation, real-time display etc.. All processors share a global memory which are partitioned into multiple banks. Through a crossbar network, data from one memory bank can be broadcasted to many processors. Processors are physically interconnected through a hyper-crossbar network (a crossbar-like network). By programming the network, the topology of communication links among processors can be reconfigurated to satisfy specific dataflows of different applications. Each processor consists of a controller, arithmetic operators, local memory, a local crossbar network, and I/O ports to communicate with other processors, memory banks, and a system controller. Operations in each processor are characterized into two modes, i.e. object domain and space domain, to fully utilize the data-independency characteristics of graphics processing. Special graphics features such as 3D-to-2D conversion, shadow generation, texturing, and reflection, can be easily handled. With the current high density interconnection (MI) technology, it is feasible to implement a 64-processor system to achieve 2.5 billion operations per second, a performance needed in most advanced graphics applications.

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

PubMed Central

Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-01-01

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods. PMID:29652838

Capillary Printing of Highly Aligned Silver Nanowire Transparent Electrodes for High-Performance Optoelectronic Devices.

PubMed

Kang, Saewon; Kim, Taehyo; Cho, Seungse; Lee, Youngoh; Choe, Ayoung; Walker, Bright; Ko, Seo-Jin; Kim, Jin Young; Ko, Hyunhyub

2015-12-09

Percolation networks of silver nanowires (AgNWs) are commonly used as transparent conductive electrodes (TCEs) for a variety of optoelectronic applications, but there have been no attempts to precisely control the percolation networks of AgNWs that critically affect the performances of TCEs. Here, we introduce a capillary printing technique to precisely control the NW alignment and the percolation behavior of AgNW networks. Notably, partially aligned AgNW networks exhibit a greatly lower percolation threshold, which leads to the substantial improvement of optical transmittance (96.7%) at a similar sheet resistance (19.5 Ω sq(-1)) as compared to random AgNW networks (92.9%, 20 Ω sq(-1)). Polymer light-emitting diodes (PLEDs) using aligned AgNW electrodes show a 30% enhanced maximum luminance (33068 cd m(-2)) compared to that with random AgNWs and a high luminance efficiency (14.25 cd A(-1)), which is the highest value reported so far using indium-free transparent electrodes for fluorescent PLEDs. In addition, polymer solar cells (PSCs) using aligned AgNW electrodes exhibit a power conversion efficiency (PCE) of 8.57%, the highest value ever reported to date for PSCs using AgNW electrodes.

Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

PubMed

Xiao, Aoran; Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-04-13

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method's practicality. Experimental results on "Jilin-1" satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Networks for image acquisition, processing and display

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.

1990-01-01

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Advice networks in teams: the role of transformational leadership and members' core self-evaluations.

PubMed

Zhang, Zhen; Peterson, Suzanne J

2011-09-01

This article examines the team-level factors promoting advice exchange networks in teams. Drawing upon theory and research on transformational leadership, team diversity, and social networks, we hypothesized that transformational leadership positively influences advice network density in teams and that advice network density serves as a mediating mechanism linking transformational leadership to team performance. We further hypothesized a 3-way interaction in which members' mean core self-evaluation (CSE) and diversity in CSE jointly moderate the transformational leadership-advice network density relationship, such that the relationship is positive and stronger for teams with low diversity in CSE and high mean CSE. In addition, we expected that advice network centralization attenuates the positive influence of network density on team performance. Results based on multisource data from 79 business unit management teams showed support for these hypotheses. The results highlight the pivotal role played by transformational leadership and team members' CSEs in enhancing team social networks and, ultimately, team effectiveness. PsycINFO Database Record (c) 2011 APA, all rights reserved

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Visuospatial working memory in very preterm and term born children--impact of age and performance.

PubMed

Mürner-Lavanchy, I; Ritter, B C; Spencer-Smith, M M; Perrig, W J; Schroth, G; Steinlin, M; Everts, R

2014-07-01

Working memory is crucial for meeting the challenges of daily life and performing academic tasks, such as reading or arithmetic. Very preterm born children are at risk of low working memory capacity. The aim of this study was to examine the visuospatial working memory network of school-aged preterm children and to determine the effect of age and performance on the neural working memory network. Working memory was assessed in 41 very preterm born children and 36 term born controls (aged 7-12 years) using functional magnetic resonance imaging (fMRI) and neuropsychological assessment. While preterm children and controls showed equal working memory performance, preterm children showed less involvement of the right middle frontal gyrus, but higher fMRI activation in superior frontal regions than controls. The younger and low-performing preterm children presented an atypical working memory network whereas the older high-performing preterm children recruited a working memory network similar to the controls. Results suggest that younger and low-performing preterm children show signs of less neural efficiency in frontal brain areas. With increasing age and performance, compensational mechanisms seem to occur, so that in preterm children, the typical visuospatial working memory network is established by the age of 12 years. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Self-assembled Ag nanoparticle network passivated by a nano-sized ZnO layer for transparent and flexible film heaters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seo, Ki-Won; Kim, Han-Ki, E-mail: imdlhkkim@khu.ac.kr; Kim, Min-Yi

2015-12-15

We investigated a self-assembled Ag nanoparticle network electrode passivated by a nano-sized ZnO layer for use in high-performance transparent and flexible film heaters (TFFHs). The low temperature atomic layer deposition of a nano-sized ZnO layer effectively filled the uncovered area of Ag network and improved the current spreading in the self-assembled Ag network without a change in the sheet resistance and optical transmittance as well as mechanical flexibility. The time-temperature profiles and heat distribution analysis demonstrate that the performance of the TFTH with the ZnO/Ag network is superior to that of a TFFH with Ag nanowire electrodes. In addition, themore » TFTHs with ZnO/Ag network exhibited better stability than the TFFH with a bare Ag network due to the effective current spreading through the nano-sized ZnO layer.« less

Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks.

PubMed

Gorochowski, Thomas E; Grierson, Claire S; di Bernardo, Mario

2018-03-01

Network motifs are significantly overrepresented subgraphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue, we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organization. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli . Our findings have implications for understanding how networked systems are constructed from motif parts and elucidate constraints that guide their evolution.

Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks

PubMed Central

Grierson, Claire S.

2018-01-01

Network motifs are significantly overrepresented subgraphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue, we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organization. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli. Our findings have implications for understanding how networked systems are constructed from motif parts and elucidate constraints that guide their evolution. PMID:29670941

Internet end-to-end performance monitoring for the High Energy Nuclear and Particle Physics community

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matthews, W.

2000-02-22

Modern High Energy Nuclear and Particle Physics (HENP) experiments at Laboratories around the world present a significant challenge to wide area networks. Petabytes (1015) or exabytes (1018) of data will be generated during the lifetime of the experiment. Much of this data will be distributed via the Internet to the experiment's collaborators at Universities and Institutes throughout the world for analysis. In order to assess the feasibility of the computing goals of these and future experiments, the HENP networking community is actively monitoring performance across a large part of the Internet used by its collaborators. Since 1995, the pingER projectmore » has been collecting data on ping packet loss and round trip times. In January 2000, there are 28 monitoring sites in 15 countries gathering data on over 2,000 end-to-end pairs. HENP labs such as SLAC, Fermi Lab and CERN are using Advanced Network's Surveyor project and monitoring performance from one-way delay of UDP packets. More recently several HENP sites have become involved with NLANR's active measurement program (AMP). In addition SLAC and CERN are part of the RIPE test-traffic project and SLAC is home for a NIMI machine. The large End-to-end performance monitoring infrastructure allows the HENP networking community to chart long term trends and closely examine short term glitches across a wide range of networks and connections. The different methodologies provide opportunities to compare results based on different protocols and statistical samples. Understanding agreement and discrepancies between results provides particular insight into the nature of the network. This paper will highlight the practical side of monitoring by reviewing the special needs of High Energy Nuclear and Particle Physics experiments and provide an overview of the experience of measuring performance across a large number of interconnected networks throughout the world with various methodologies. In particular, results from each project will be compared and disagreement will be analyzed. The goal is to address issues for improving understanding for gathering and analysis of accurate monitoring data, but the outlook for the computing goals of HENP will also be examined.« less

Differential network as an indicator of osteoporosis with network entropy.

PubMed

Ma, Lili; Du, Hongmei; Chen, Guangdong

2018-07-01

Osteoporosis is a common skeletal disorder characterized by a decrease in bone mass and density. The peak bone mass (PBM) is a significant determinant of osteoporosis. To gain insights into the indicating effect of PBM to osteoporosis, this study focused on characterizing the PBM networks and identifying key genes. One biological data set with 12 monocyte low PBM samples and 11 high PBM samples was derived to construct protein-protein interaction networks (PPINs). Based on clique-merging, module-identification algorithm was used to identify modules from PPINs. The systematic calculation and comparison were performed to test whether the network entropy can discriminate the low PBM network from high PBM network. We constructed 32 destination networks with 66 modules divided from monocyte low and high PBM networks. Among them, network 11 was the only significantly differential one (P<0.05) with 8 nodes and 28 edges. All genes belonged to precursors of osteoclasts, which were related to calcium transport as well as blood monocytes. In conclusion, based on the entropy in PBM PPINs, the differential network appears to be a novel therapeutic indicator for osteoporosis during the bone monocyte progression; these findings are helpful in disclosing the pathogenetic mechanisms of osteoporosis.

High End Computer Network Testbedding at NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Gary, James Patrick

1998-01-01

The Earth & Space Data Computing (ESDC) Division, at the Goddard Space Flight Center, is involved in development and demonstrating various high end computer networking capabilities. The ESDC has several high end super computers. These are used to run: (1) computer simulation of the climate systems; (2) to support the Earth and Space Sciences (ESS) project; (3) to support the Grand Challenge (GC) Science, which is aimed at understanding the turbulent convection and dynamos in stars. GC research occurs in many sites throughout the country, and this research is enabled by, in part, the multiple high performance network interconnections. The application drivers for High End Computer Networking use distributed supercomputing to support virtual reality applications, such as TerraVision, (i.e., three dimensional browser of remotely accessed data), and Cave Automatic Virtual Environments (CAVE). Workstations can access and display data from multiple CAVE's with video servers, which allows for group/project collaborations using a combination of video, data, voice and shared white boarding. The ESDC is also developing and demonstrating the high degree of interoperability between satellite and terrestrial-based networks. To this end, the ESDC is conducting research and evaluations of new computer networking protocols and related technologies which improve the interoperability of satellite and terrestrial networks. The ESDC is also involved in the Security Proof of Concept Keystone (SPOCK) program sponsored by National Security Agency (NSA). The SPOCK activity provides a forum for government users and security technology providers to share information on security requirements, emerging technologies and new product developments. Also, the ESDC is involved in the Trans-Pacific Digital Library Experiment, which aims to demonstrate and evaluate the use of high performance satellite communications and advanced data communications protocols to enable interactive digital library data access between the U. S. Library of Congress, the National Library of Japan and other digital library sites at 155 MegaBytes Per Second. The ESDC participation in this program is the Trans-Pacific access to GLOBE visualizations in real time. ESDC is participating in the Department of Defense's ATDNet with Multiwavelength Optical Network (MONET) a fully switched Wavelength Division Networking testbed. This presentation is in viewgraph format.

Comparison of High Performance Network Options: EDR InfiniBand vs.100Gb RDMA Capable Ethernet

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kachelmeier, Luke Anthony; Van Wig, Faith Virginia; Erickson, Kari Natania

These are the slides for a presentation at the HPC Mini Showcase. This is a comparison of two different high performance network options: EDR InfiniBand and 100Gb RDMA capable ethernet. The conclusion of this comparison is the following: there is good potential, as shown with the direct results; 100Gb technology is too new and not standardized, thus deployment effort is complex for both options; different companies are not necessarily compatible; if you want 100Gb/s, you must get it all from one place.

The role of human-automation consensus in multiple unmanned vehicle scheduling.

PubMed

Cummings, M L; Clare, Andrew; Hart, Christin

2010-02-01

This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation's suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. In decentralized unmanned vehicle networks, operators who ignore the automation's requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian W.; Hemmert, K. Scott; Underwood, Keith Douglas

Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require a significant advancements in several fundamental areas. Recent studies have outlined many of the challenges in hardware and software that will be needed. In this paper, we examine these challenges with respect to high-performance networking. We describe the repercussions of anticipated changes to computing and networking hardware and discuss the impact that alternative parallel programming models will have on the network software stack. We also present some ideas on possible approaches that address some of these challenges.

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code.

PubMed

Kunkel, Susanne; Schenck, Wolfram

2017-01-01

NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

DOE PAGES

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott; ...

2017-08-29

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

PubMed Central

Kunkel, Susanne; Schenck, Wolfram

2017-01-01

NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling. PMID:28701946

Design, Implementation, and Evaluation of a Virtual Shared Memory System in a Multi-Transputer Network.

DTIC Science & Technology

1987-12-01

Synchronization and Data Passing Mechanism ........ 50 4. System Shut Down .................................................................. 51 5...high performance, fault tolerance, and extensibility. These features are attained by synchronizing and coordinating the dis- tributed multicomputer... synchronizing all processors in the network. In a multitransputer network, processes that communicate with each other do so synchronously . This makes

Wide-Area Network Resources for Teacher Education.

ERIC Educational Resources Information Center

Aust, Ronald

A central feature of the High Performance Computing Act of 1991 is the establishment of a National Research and Education Network (NREN). The level of access that teachers and teacher educators will need to benefit from the NREN and the types of network resources that are most useful for educators are explored, along with design issues that are…

A complex valued radial basis function network for equalization of fast time varying channels.

PubMed

Gan, Q; Saratchandran, P; Sundararajan, N; Subramanian, K R

1999-01-01

This paper presents a complex valued radial basis function (RBF) network for equalization of fast time varying channels. A new method for calculating the centers of the RBF network is given. The method allows fixing the number of RBF centers even as the equalizer order is increased so that a good performance is obtained by a high-order RBF equalizer with small number of centers. Simulations are performed on time varying channels using a Rayleigh fading channel model to compare the performance of our RBF with an adaptive maximum-likelihood sequence estimator (MLSE) consisting of a channel estimator and a MLSE implemented by the Viterbi algorithm. The results show that the RBF equalizer produces superior performance with less computational complexity.

Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

PubMed

Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

2017-10-01

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

Promoting High-Performance Computing and Communications. A CBO Study.

ERIC Educational Resources Information Center

Webre, Philip

In 1991 the Federal Government initiated the multiagency High Performance Computing and Communications program (HPCC) to further the development of U.S. supercomputer technology and high-speed computer network technology. This overview by the Congressional Budget Office (CBO) concentrates on obstacles that might prevent the growth of the…

Simple arithmetic: not so simple for highly math anxious individuals

PubMed Central

Sprute, Lisa; Maloney, Erin A; Beilock, Sian L; Berman, Marc G

2017-01-01

Abstract Fluency with simple arithmetic, typically achieved in early elementary school, is thought to be one of the building blocks of mathematical competence. Behavioral studies with adults indicate that math anxiety (feelings of tension or apprehension about math) is associated with poor performance on cognitively demanding math problems. However, it remains unclear whether there are fundamental differences in how high and low math anxious individuals approach overlearned simple arithmetic problems that are less reliant on cognitive control. The current study used functional magnetic resonance imaging to examine the neural correlates of simple arithmetic performance across high and low math anxious individuals. We implemented a partial least squares analysis, a data-driven, multivariate analysis method to measure distributed patterns of whole-brain activity associated with performance. Despite overall high simple arithmetic performance across high and low math anxious individuals, performance was differentially dependent on the fronto-parietal attentional network as a function of math anxiety. Specifically, low—compared to high—math anxious individuals perform better when they activate this network less—a potential indication of more automatic problem-solving. These findings suggest that low and high math anxious individuals approach even the most fundamental math problems differently. PMID:29140499

A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.

PubMed

Şenbabaoğlu, Yasin; Sümer, Selçuk Onur; Sánchez-Vega, Francisco; Bemis, Debra; Ciriello, Giovanni; Schultz, Nikolaus; Sander, Chris

2016-02-01

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

Link prediction in multiplex online social networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

Link prediction in multiplex online social networks.

PubMed

Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

A parallel input composite transimpedance amplifier.

PubMed

Kim, D J; Kim, C

2018-01-01

A new approach to high performance current to voltage preamplifier design is presented. The design using multiple operational amplifiers (op-amps) has a parasitic capacitance compensation network and a composite amplifier topology for fast, precision, and low noise performance. The input stage consisting of a parallel linked JFET op-amps and a high-speed bipolar junction transistor (BJT) gain stage driving the output in the composite amplifier topology, cooperating with the capacitance compensation feedback network, ensures wide bandwidth stability in the presence of input capacitance above 40 nF. The design is ideal for any two-probe measurement, including high impedance transport and scanning tunneling microscopy measurements.

A parallel input composite transimpedance amplifier

NASA Astrophysics Data System (ADS)

Kim, D. J.; Kim, C.

2018-01-01

A new approach to high performance current to voltage preamplifier design is presented. The design using multiple operational amplifiers (op-amps) has a parasitic capacitance compensation network and a composite amplifier topology for fast, precision, and low noise performance. The input stage consisting of a parallel linked JFET op-amps and a high-speed bipolar junction transistor (BJT) gain stage driving the output in the composite amplifier topology, cooperating with the capacitance compensation feedback network, ensures wide bandwidth stability in the presence of input capacitance above 40 nF. The design is ideal for any two-probe measurement, including high impedance transport and scanning tunneling microscopy measurements.

Thin Watts-Strogatz networks.

PubMed

de Moura, Alessandro P S

2006-01-01

A modified version of the Watts-Strogatz (WS) network model is proposed, in which the number of shortcuts scales with the network size N as Nalpha, with alpha < 1. In these networks, the ratio of the number of shortcuts to the network size approaches zero as N --> infinity, whereas in the original WS model, this ratio is constant. We call such networks "thin Watts-Strogatz networks." We show that even though the fraction of shortcuts becomes vanishingly small for large networks, they still cause a kind of small-world effect, in the sense that the length L of the network increases sublinearly with the size. We develop a mean-field theory for these networks, which predicts that the length scales as N1-alpha ln N for large N. We also study how a search using only local information works in thin WS networks. We find that the search performance is enhanced compared to the regular network, and we predict that the search time tau scales as N1-alpha/2. These theoretical results are tested using numerical simulations. We comment on the possible relevance of thin WS networks for the design of high-performance low-cost communication networks.

AD HOC Networks for the Autonomous Car

NASA Astrophysics Data System (ADS)

Ron, Davidescu; Negrus, Eugen

2017-10-01

The future of the vehicle is made of cars, roads and infrastructures connected in a two way automated communication in a holistic system. It is a mandatory to use Encryption to maintain Confidentiality, Integrity and Availability in an ad hoc vehicle network. Vehicle to Vehicle communication, requires multichannel interaction between mobile, moving and changing parties to insure the full benefit from data sharing and real time decision making, a network of such users referred as mobile ad hoc network (MANET), however as ad hoc networks were not implemented in such a scale, it is not clear what is the best method and protocol to apply. Furthermore the visibility of secure preferred asymmetric encrypted ad hoc networks in a real time environment of dense moving autonomous vehicles has to be demonstrated, In order to evaluate the performance of Ad Hoc networks in changing conditions a simulation of multiple protocols was performed on large number of mobile nodes. The following common routing protocols were tested, DSDV is a proactive protocol, every mobile station maintains a routing table with all available destinations, DSR is a reactive routing protocol which allows nodes in the MANET to dynamically discover a source route across multiple network hops, AODV is a reactive routing protocol Instead of being proactive. It minimizes the number of broadcasts by creating routes based on demand, SAODV is a secure version of AODV, requires heavyweight asymmetric cryptographic, ARIANDE is a routing protocol that relies on highly efficient symmetric cryptography the concept is primarily based on DSR. A methodical evolution was performed in a various density of transportation, based on known communication bench mark parameters including, Throughput Vs. time, Routing Load per packets and bytes. Out of the none encrypted protocols, It is clear that in terms of performance of throughput and routing load DSR protocol has a clear advantage the high node number mode. The encrypted protocols show lower performance with ARIANDE being superior to SAODV and SRP. Nevertheless all protocol simulation proved it to match required real time performance.

Predicting efficiency of solar cells based on transparent conducting electrodes

NASA Astrophysics Data System (ADS)

Kumar, Ankush

2017-01-01

Efficiency of a solar cell is directly correlated with the performance of its transparent conducting electrodes (TCEs) which dictates its two core processes, viz., absorption and collection efficiencies. Emerging designs of a TCE involve active networks of carbon nanotubes, silver nanowires and various template-based techniques providing diverse structures; here, voids are transparent for optical transmittance while the conducting network acts as a charge collector. However, it is still not well understood as to which kind of network structure leads to an optimum solar cell performance; therefore, mostly an arbitrary network is chosen as a solar cell electrode. Herein, we propose a new generic approach for understanding the role of TCEs in determining the solar cell efficiency based on analysis of shadowing and recombination losses. A random network of wires encloses void regions of different sizes and shapes which permit light transmission; two terms, void fraction and equivalent radius, are defined to represent the TCE transmittance and wire spacings, respectively. The approach has been applied to various literature examples and their solar cell performance has been compared. To obtain high-efficiency solar cells, optimum density of the wires and their aspect ratio as well as active layer thickness are calculated. Our findings show that a TCE well suitable for one solar cell may not be suitable for another. For high diffusion length based solar cells, the void fraction of the network should be low while for low diffusion length based solar cells, the equivalent radius should be lower. The network with less wire spacing compared to the diffusion length behaves similar to continuous film based TCEs (such as indium tin oxide). The present work will be useful for architectural as well as material engineering of transparent electrodes for improvisation of solar cell performance.

A Comparative Study of 11 Local Health Department Organizational Networks

PubMed Central

Merrill, Jacqueline; Keeling, Jonathan W.; Carley, Kathleen M.

2013-01-01

Context Although the nation’s local health departments (LHDs) share a common mission, variability in administrative structures is a barrier to identifying common, optimal management strategies. There is a gap in understanding what unifying features LHDs share as organizations that could be leveraged systematically for achieving high performance. Objective To explore sources of commonality and variability in a range of LHDs by comparing intraorganizational networks. Intervention We used organizational network analysis to document relationships between employees, tasks, knowledge, and resources within LHDs, which may exist regardless of formal administrative structure. Setting A national sample of 11 LHDs from seven states that differed in size, geographic location, and governance. Participants Relational network data were collected via an on-line survey of all employees in 11 LHDs. A total of 1 062 out of 1 239 employees responded (84% response rate). Outcome Measures Network measurements were compared using coefficient of variation. Measurements were correlated with scores from the National Public Health Performance Assessment and with LHD demographics. Rankings of tasks, knowledge, and resources were correlated across pairs of LHDs. Results We found that 11 LHDs exhibited compound organizational structures in which centralized hierarchies were coupled with distributed networks at the point of service. Local health departments were distinguished from random networks by a pattern of high centralization and clustering. Network measurements were positively associated with performance for 3 of 10 essential services (r > 0.65). Patterns in the measurements suggest how LHDs adapt to the population served. Conclusions Shared network patterns across LHDs suggest where common organizational management strategies are feasible. This evidence supports national efforts to promote uniform standards for service delivery to diverse populations. PMID:20445462

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

PubMed

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

2015-09-07

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

Hierarchy Bayesian model based services awareness of high-speed optical access networks

NASA Astrophysics Data System (ADS)

Bai, Hui-feng

2018-03-01

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit (ONU) and to perform complex services awareness from the whole view of system in optical line terminal (OLT). Simulation results show that the proposed scheme is able to achieve better quality of services (QoS), in terms of packet loss rate and time delay.

Software/hardware distributed processing network supporting the Ada environment

NASA Astrophysics Data System (ADS)

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.

The medical science DMZ: a network design pattern for data-intensive medical science.

PubMed

Peisert, Sean; Dart, Eli; Barnett, William; Balas, Edward; Cuff, James; Grossman, Robert L; Berman, Ari; Shankar, Anurag; Tierney, Brian

2017-10-06

We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. High-end networking, packet-filter firewalls, network intrusion-detection systems. We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs. The exponentially increasing amounts of "omics" data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research "Big Data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows. By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Biological modelling of a computational spiking neural network with neuronal avalanches.

PubMed

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-06-28

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. © 2017 The Author(s).

Biological modelling of a computational spiking neural network with neuronal avalanches

NASA Astrophysics Data System (ADS)

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-05-01

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Studies of pointing, acquisition, and tracking of agile optical wireless transceivers for free-space optical communication networks

NASA Astrophysics Data System (ADS)

Ho, Tzung-Hsien; Trisno, Sugianto; Smolyaninov, Igor I.; Milner, Stuart D.; Davis, Christopher C.

2004-02-01

Free space, dynamic, optical wireless communications will require topology control for optimization of network performance. Such networks may need to be configured for bi- or multiple-connectedness, reliability and quality-of-service. Topology control involves the introduction of new links and/or nodes into the network to achieve such performance objectives through autonomous reconfiguration as well as precise pointing, acquisition, tracking, and steering of laser beams. Reconfiguration may be required because of link degradation resulting from obscuration or node loss. As a result, the optical transceivers may need to be re-directed to new or existing nodes within the network and tracked on moving nodes. The redirection of transceivers may require operation over a whole sphere, so that small-angle beam steering techniques cannot be applied. In this context, we are studying the performance of optical wireless links using lightweight, bi-static transceivers mounted on high-performance stepping motor driven stages. These motors provide an angular resolution of 0.00072 degree at up to 80,000 steps per second. This paper focuses on the performance characteristics of these agile transceivers for pointing, acquisition, and tracking (PAT), including the influence of acceleration/deceleration time, motor angular speed, and angular re-adjustment, on latency and packet loss in small free space optical (FSO) wireless test networks.

Ultrafine manganese dioxide nanowire network for high-performance supercapacitors.

PubMed

Jiang, Hao; Zhao, Ting; Ma, Jan; Yan, Chaoyi; Li, Chunzhong

2011-01-28

Ultrafine MnO(2) nanowires with sub-10 nm diameters have been synthesized by a simple process of hydrothermal treatment with subsequent calcinations to form networks that exhibit an enhanced specific capacitance (279 F g(-1) at 1 A g(-1)), high rate capability (54.5% retention at 20 A g(-1)) and good cycling stability (1.7% loss after 1000 cycles).

Gigabit Wireless for Network Connectivity

ERIC Educational Resources Information Center

Schoedel, Eric

2009-01-01

Uninterrupted, high-bandwidth network connectivity is crucial for higher education. Colleges and universities increasingly adopt gigabit wireless solutions because of their fiber-equivalent performance, quick implementation, and significant return on investment. For just those reasons, Rush University Medical Center switched from free space optics…

Brain and effort: brain activation and effort-related working memory in healthy participants and patients with working memory deficits.

PubMed

Engström, Maria; Landtblom, Anne-Marie; Karlsson, Thomas

2013-01-01

Despite the interest in the neuroimaging of working memory, little is still known about the neurobiology of complex working memory in tasks that require simultaneous manipulation and storage of information. In addition to the central executive network, we assumed that the recently described salience network [involving the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC)] might be of particular importance to working memory tasks that require complex, effortful processing. Healthy participants (n = 26) and participants suffering from working memory problems related to the Kleine-Levin syndrome (KLS) (a specific form of periodic idiopathic hypersomnia; n = 18) participated in the study. Participants were further divided into a high- and low-capacity group, according to performance on a working memory task (listening span). In a functional magnetic resonance imaging (fMRI) study, participants were administered the reading span complex working memory task tapping cognitive effort. The fMRI-derived blood oxygen level dependent (BOLD) signal was modulated by (1) effort in both the central executive and the salience network and (2) capacity in the salience network in that high performers evidenced a weaker BOLD signal than low performers. In the salience network there was a dichotomy between the left and the right hemisphere; the right hemisphere elicited a steeper increase of the BOLD signal as a function of increasing effort. There was also a stronger functional connectivity within the central executive network because of increased task difficulty. The ability to allocate cognitive effort in complex working memory is contingent upon focused resources in the executive and in particular the salience network. Individual capacity during the complex working memory task is related to activity in the salience (but not the executive) network so that high-capacity participants evidence a lower signal and possibly hence a larger dynamic response.

Brain and effort: brain activation and effort-related working memory in healthy participants and patients with working memory deficits

PubMed Central

Engström, Maria; Landtblom, Anne-Marie; Karlsson, Thomas

2013-01-01

Despite the interest in the neuroimaging of working memory, little is still known about the neurobiology of complex working memory in tasks that require simultaneous manipulation and storage of information. In addition to the central executive network, we assumed that the recently described salience network [involving the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC)] might be of particular importance to working memory tasks that require complex, effortful processing. Method: Healthy participants (n = 26) and participants suffering from working memory problems related to the Kleine–Levin syndrome (KLS) (a specific form of periodic idiopathic hypersomnia; n = 18) participated in the study. Participants were further divided into a high- and low-capacity group, according to performance on a working memory task (listening span). In a functional magnetic resonance imaging (fMRI) study, participants were administered the reading span complex working memory task tapping cognitive effort. Principal findings: The fMRI-derived blood oxygen level dependent (BOLD) signal was modulated by (1) effort in both the central executive and the salience network and (2) capacity in the salience network in that high performers evidenced a weaker BOLD signal than low performers. In the salience network there was a dichotomy between the left and the right hemisphere; the right hemisphere elicited a steeper increase of the BOLD signal as a function of increasing effort. There was also a stronger functional connectivity within the central executive network because of increased task difficulty. Conclusion: The ability to allocate cognitive effort in complex working memory is contingent upon focused resources in the executive and in particular the salience network. Individual capacity during the complex working memory task is related to activity in the salience (but not the executive) network so that high-capacity participants evidence a lower signal and possibly hence a larger dynamic response. PMID:23616756

Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison

NASA Astrophysics Data System (ADS)

De Domenico, Manlio; Biamonte, Jacob

2016-10-01

Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.

Optimized star sensors laboratory calibration method using a regularization neural network.

PubMed

Zhang, Chengfen; Niu, Yanxiong; Zhang, Hao; Lu, Jiazhen

2018-02-10

High-precision ground calibration is essential to ensure the performance of star sensors. However, the complex distortion and multi-error coupling have brought great difficulties to traditional calibration methods, especially for large field of view (FOV) star sensors. Although increasing the complexity of models is an effective way to improve the calibration accuracy, it significantly increases the demand for calibration data. In order to achieve high-precision calibration of star sensors with large FOV, a novel laboratory calibration method based on a regularization neural network is proposed. A multi-layer structure neural network is designed to represent the mapping of the star vector and the corresponding star point coordinate directly. To ensure the generalization performance of the network, regularization strategies are incorporated into the net structure and the training algorithm. Simulation and experiment results demonstrate that the proposed method can achieve high precision with less calibration data and without any other priori information. Compared with traditional methods, the calibration error of the star sensor decreased by about 30%. The proposed method can satisfy the precision requirement for large FOV star sensors.

A neural networks application for the study of the influence of transport conditions on the working performance

NASA Astrophysics Data System (ADS)

Anghel, D.-C.; Ene, A.; Ştirbu, C.; Sicoe, G.

2017-10-01

This paper presents a study about the factors that influence the working performances of workers in the automotive industry. These factors regard mainly the transportations conditions, taking into account the fact that a large number of workers live in places that are far away of the enterprise. The quantitative data obtained from this study will be generalized by using a neural network, software simulated. The neural network is able to estimate the performance of workers even for the combinations of input factors that had been not recorded by the study. The experimental data obtained from the study will be divided in two classes. The first class that contains approximately 80% of data will be used by the Java software for the training of the neural network. The weights resulted from the training process will be saved in a text file. The other class that contains the rest of the 20% of experimental data will be used to validate the neural network. The training and the validation of the networks are performed in a Java software (TrainAndValidate java class). We designed another java class, Test.java that will be used with new input data, for new situations. The experimental data collected from the study. The software that simulated the neural network. The software that estimates the working performance, when new situations are met. This application is useful for human resources department of an enterprise. The output results are not quantitative. They are qualitative (from low performance to high performance, divided in five classes).

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

PubMed

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

High-Gain AlxGa1-xAs/GaAs Transistors For Neural Networks

NASA Technical Reports Server (NTRS)

Kim, Jae-Hoon; Lin, Steven H.

1991-01-01

High-gain AlxGa1-xAs/GaAs npn double heterojunction bipolar transistors developed for use as phototransistors in optoelectronic integrated circuits, especially in artificial neural networks. Transistors perform both photodetection and saturating-amplification functions of neurons. Good candidates for such application because structurally compatible with laser diodes and light-emitting diodes, detect light, and provide high current gain needed to compensate for losses in holographic optical elements.

Dynamic autonomous routing technology for IP-based satellite ad hoc networks

NASA Astrophysics Data System (ADS)

Wang, Xiaofei; Deng, Jing; Kostas, Theresa; Rajappan, Gowri

2014-06-01

IP-based routing for military LEO/MEO satellite ad hoc networks is very challenging due to network and traffic heterogeneity, network topology and traffic dynamics. In this paper, we describe a traffic priority-aware routing scheme for such networks, namely Dynamic Autonomous Routing Technology (DART) for satellite ad hoc networks. DART has a cross-layer design, and conducts routing and resource reservation concurrently for optimal performance in the fluid but predictable satellite ad hoc networks. DART ensures end-to-end data delivery with QoS assurances by only choosing routing paths that have sufficient resources, supporting different packet priority levels. In order to do so, DART incorporates several resource management and innovative routing mechanisms, which dynamically adapt to best fit the prevailing conditions. In particular, DART integrates a resource reservation mechanism to reserve network bandwidth resources; a proactive routing mechanism to set up non-overlapping spanning trees to segregate high priority traffic flows from lower priority flows so that the high priority flows do not face contention from low priority flows; a reactive routing mechanism to arbitrate resources between various traffic priorities when needed; a predictive routing mechanism to set up routes for scheduled missions and for anticipated topology changes for QoS assurance. We present simulation results showing the performance of DART. We have conducted these simulations using the Iridium constellation and trajectories as well as realistic military communications scenarios. The simulation results demonstrate DART's ability to discriminate between high-priority and low-priority traffic flows and ensure disparate QoS requirements of these traffic flows.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Performance evaluation of time-aware enhanced software defined networking (TeSDN) for elastic data center optical interconnection.

PubMed

Yang, Hui; Zhang, Jie; Zhao, Yongli; Ji, Yuefeng; Li, Hui; Lin, Yi; Li, Gang; Han, Jianrui; Lee, Young; Ma, Teng

2014-07-28

Data center interconnection with elastic optical networks is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. We previously implemented enhanced software defined networking over elastic optical network for data center application [Opt. Express 21, 26990 (2013)]. On the basis of it, this study extends to consider the time-aware data center service scheduling with elastic service time and service bandwidth according to the various time sensitivity requirements. A novel time-aware enhanced software defined networking (TeSDN) architecture for elastic data center optical interconnection has been proposed in this paper, by introducing a time-aware resources scheduling (TaRS) scheme. The TeSDN can accommodate the data center services with required QoS considering the time dimensionality, and enhance cross stratum optimization of application and elastic optical network stratums resources based on spectrum elasticity, application elasticity and time elasticity. The overall feasibility and efficiency of the proposed architecture is experimentally verified on our OpenFlow-based testbed. The performance of TaRS scheme under heavy traffic load scenario is also quantitatively evaluated based on TeSDN architecture in terms of blocking probability and resource occupation rate.

Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks

PubMed Central

Jurdak, Raja; Nafaa, Abdelhamid; Barbirato, Alessio

2008-01-01

Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper. PMID:27873941

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

How the initial level of visibility and limited resource affect the evolution of cooperation

NASA Astrophysics Data System (ADS)

Han, Dun; Li, Dandan; Sun, Mei

2016-06-01

This work sheds important light on how the initial level of visibility and limited resource might affect the evolution of the players’ strategies under different network structure. We perform the prisoner’s dilemma game in the lattice network and the scale-free network, the simulation results indicate that the average density of death in lattice network decreases with the increases of the initial proportion of visibility. However, the contrary phenomenon is observed in the scale-free network. Further results reflect that the individuals’ payoff in lattice network is significantly larger than the one in the scale-free network. In the lattice network, the visibility individuals could earn much more than the invisibility one. However, the difference is not apparent in the scale-free network. We also find that a high Successful-Defection-Payoff (SDB) and a rich natural environment have relatively larger deleterious cooperation effects. A high SDB is beneficial to raising the level of visibility in the heterogeneous network, however, that has adverse visibility consequences in homogeneous network. Our result reveals that players are more likely to cooperate voluntarily under homogeneous network structure.

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

PubMed

Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

2007-12-01

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

Implementing direct, spatially isolated problems on transputer networks

NASA Technical Reports Server (NTRS)

Ellis, Graham K.

1988-01-01

Parametric studies were performed on transputer networks of up to 40 processors to determine how to implement and maximize the performance of the solution of problems where no processor-to-processor data transfer is required for the problem solution (spatially isolated). Two types of problems are investigated a computationally intensive problem where the solution required the transmission of 160 bytes of data through the parallel network, and a communication intensive example that required the transmission of 3 Mbytes of data through the network. This data consists of solutions being sent back to the host processor and not intermediate results for another processor to work on. Studies were performed on both integer and floating-point transputers. The latter features an on-chip floating-point math unit and offers approximately an order of magnitude performance increase over the integer transputer on real valued computations. The results indicate that a minimum amount of work is required on each node per communication to achieve high network speedups (efficiencies). The floating-point processor requires approximately an order of magnitude more work per communication than the integer processor because of the floating-point unit's increased computing capacity.

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

NASA Astrophysics Data System (ADS)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network

NASA Astrophysics Data System (ADS)

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-07-01

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-07-28

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes.

Performance evaluation of multi-stratum resources integration based on network function virtualization in software defined elastic data center optical interconnect.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; Tian, Rui; Han, Jianrui; Lee, Young

2015-11-30

Data center interconnect with elastic optical network is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. In our previous work, we implemented multi-stratum resilience between IP and elastic optical networks that allows to accommodate data center services. In view of this, this study extends to consider the resource integration by breaking the limit of network device, which can enhance the resource utilization. We propose a novel multi-stratum resources integration (MSRI) architecture based on network function virtualization in software defined elastic data center optical interconnect. A resource integrated mapping (RIM) scheme for MSRI is introduced in the proposed architecture. The MSRI can accommodate the data center services with resources integration when the single function or resource is relatively scarce to provision the services, and enhance globally integrated optimization of optical network and application resources. The overall feasibility and efficiency of the proposed architecture are experimentally verified on the control plane of OpenFlow-based enhanced software defined networking (eSDN) testbed. The performance of RIM scheme under heavy traffic load scenario is also quantitatively evaluated based on MSRI architecture in terms of path blocking probability, provisioning latency and resource utilization, compared with other provisioning schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network

PubMed Central

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-01-01

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes. PMID:27465296

PROPER: global protein interaction network alignment through percolation matching.

PubMed

Kazemi, Ehsan; Hassani, Hamed; Grossglauser, Matthias; Pezeshgi Modarres, Hassan

2016-12-12

The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch .

Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?

PubMed Central

Craddock, Travis J. A.; Fletcher, Mary Ann; Klimas, Nancy G.

2015-01-01

There is a growing appreciation for the network biology that regulates the coordinated expression of molecular and cellular markers however questions persist regarding the identifiability of these networks. Here we explore some of the issues relevant to recovering directed regulatory networks from time course data collected under experimental constraints typical of in vivo studies. NetSim simulations of sparsely connected biological networks were used to evaluate two simple feature selection techniques used in the construction of linear Ordinary Differential Equation (ODE) models, namely truncation of terms versus latent vector projection. Performance was compared with ODE-based Time Series Network Identification (TSNI) integral, and the information-theoretic Time-Delay ARACNE (TD-ARACNE). Projection-based techniques and TSNI integral outperformed truncation-based selection and TD-ARACNE on aggregate networks with edge densities of 10-30%, i.e. transcription factor, protein-protein cliques and immune signaling networks. All were more robust to noise than truncation-based feature selection. Performance was comparable on the in silico 10-node DREAM 3 network, a 5-node Yeast synthetic network designed for In vivo Reverse-engineering and Modeling Assessment (IRMA) and a 9-node human HeLa cell cycle network of similar size and edge density. Performance was more sensitive to the number of time courses than to sample frequency and extrapolated better to larger networks by grouping experiments. In all cases performance declined rapidly in larger networks with lower edge density. Limited recovery and high false positive rates obtained overall bring into question our ability to generate informative time course data rather than the design of any particular reverse engineering algorithm. PMID:25984725

Effect of tumor resection on the characteristics of functional brain networks.

PubMed

Wang, H; Douw, L; Hernández, J M; Reijneveld, J C; Stam, C J; Van Mieghem, P

2010-08-01

Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted network analysis to understand the effect of neurosurgery on the characteristics of functional brain networks. Statistically significant changes in network features have been discovered in the beta (13-30 Hz) band after neurosurgery: the link weight correlation around nodes and within triangles increases which implies improvement in local efficiency of information transfer and robustness; the clustering of high link weights in a subgraph becomes stronger, which enhances the global transport capability; and the decrease in the synchronization or virus spreading threshold, revealed by the increase in the largest eigenvalue of the adjacency matrix, which suggests again the improvement of information dissemination.

The Science DMZ: A Network Design Pattern for Data-Intensive Science

DOE PAGES

Dart, Eli; Rotman, Lauren; Tierney, Brian; ...

2014-01-01

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers andmore » research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.« less

Data systems and computer science space data systems: Onboard networking and testbeds

NASA Technical Reports Server (NTRS)

Dalton, Dan

1991-01-01

The technical objectives are to develop high-performance, space-qualifiable, onboard computing, storage, and networking technologies. The topics are presented in viewgraph form and include the following: justification; technology challenges; program description; and state-of-the-art assessment.

Influences of social network sites on the occupational performance of adolescents in a secondary school in Cape Town, South Africa: a phenomenological study.

PubMed

Mthembu, Thuli G; Beets, Charmaine; Davids, Gafeedha; Malyon, Kelly; Pekeur, Marchelle; Rabinowitz, Avital

2014-06-01

The habit of using social networking sites among adolescents has grown exponentially; there is little accompanying research to understand the influences on adolescents' occupational performance with this technology. The majority of adolescents are engaging in social network as part of their daily routines. Occupational performance is the act of doing and accomplishing a selected occupation that results from the dynamic transaction among the person, the environment and the occupation components. This study aimed to explore the influences of social networking on occupational performance of adolescents in a high school in Western Cape Province, South Africa. A phenomenological approach was used. Adolescents aged 13-17 years in a high school were purposively recruited for the study. Semi-structured interviews were conducted with four participants (two adolescents and two teachers) and two focus groups were undertaken with adolescents, analysed using thematic analysis. Four major themes emerged: 'It's a good way to keep in touch', 'It's part of me and it's not a bad thing', 'There is a time and place for it' and 'There's an urgency to be on the phone'. This study highlighted that social networking sites play a major role in the social life of adolescents, though it can result in occupational imbalance on their occupational performance. Furthermore, this study contributes to the knowledge of occupational therapists who work with adolescents in communities and health promoting school settings. Thus, collaboration between teachers, parents and occupational therapists can help to develop adolescents' time management and learning skills. © 2013 Occupational Therapy Australia.

Joint Sensing/Sampling Optimization for Surface Drifting Mine Detection with High-Resolution Drift Model

DTIC Science & Technology

2012-09-01

as potential tools for large area detection coverage while being moderately inexpensive (Wettergren, Performance of Search via Track - Before - Detect for...via Track - Before - Detect for Distribute 34 Sensor Networks, 2008). These statements highlight three specific needs to further sensor network research...Bay hydrography. Journal of Marine Systems, 12, 221–236. Wettergren, T. A. (2008). Performance of search via track - before - detect for distributed

System, apparatus and methods to implement high-speed network analyzers

DOEpatents

Ezick, James; Lethin, Richard; Ros-Giralt, Jordi; Szilagyi, Peter; Wohlford, David E

2015-11-10

Systems, apparatus and methods for the implementation of high-speed network analyzers are provided. A set of high-level specifications is used to define the behavior of the network analyzer emitted by a compiler. An optimized inline workflow to process regular expressions is presented without sacrificing the semantic capabilities of the processing engine. An optimized packet dispatcher implements a subset of the functions implemented by the network analyzer, providing a fast and slow path workflow used to accelerate specific processing units. Such dispatcher facility can also be used as a cache of policies, wherein if a policy is found, then packet manipulations associated with the policy can be quickly performed. An optimized method of generating DFA specifications for network signatures is also presented. The method accepts several optimization criteria, such as min-max allocations or optimal allocations based on the probability of occurrence of each signature input bit.

Electrodeposited Structurally Stable V2O5 Inverse Opal Networks as High Performance Thin Film Lithium Batteries.

PubMed

Armstrong, Eileen; McNulty, David; Geaney, Hugh; O'Dwyer, Colm

2015-12-09

High performance thin film lithium batteries using structurally stable electrodeposited V2O5 inverse opal (IO) networks as cathodes provide high capacity and outstanding cycling capability and also were demonstrated on transparent conducting oxide current collectors. The superior electrochemical performance of the inverse opal structures was evaluated through galvanostatic and potentiodynamic cycling, and the IO thin film battery offers increased capacity retention compared to micron-scale bulk particles from improved mechanical stability and electrical contact to stainless steel or transparent conducting current collectors from bottom-up electrodeposition growth. Li(+) is inserted into planar and IO structures at different potentials, and correlated to a preferential exposure of insertion sites of the IO network to the electrolyte. Additionally, potentiodynamic testing quantified the portion of the capacity stored as surface bound capacitive charge. Raman scattering and XRD characterization showed how the IO allows swelling into the pore volume rather than away from the current collector. V2O5 IO coin cells offer high initial capacities, but capacity fading can occur with limited electrolyte. Finally, we demonstrate that a V2O5 IO thin film battery prepared on a transparent conducting current collector with excess electrolyte exhibits high capacities (∼200 mAh g(-1)) and outstanding capacity retention and rate capability.

A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks.

PubMed

Le, Duc-Hau

2015-01-01

Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist. In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes. Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with prostate cancer and selected the top 100 highly ranked candidate protein complexes. Interestingly, 69 of them were evidenced since at least one of their protein elements are known to be associated with prostate cancer. Our proposed method, including the framework to construct functional similarity protein complex networks and the neighborhood-based algorithm on these networks, could be used for identification of novel disease-protein complex associations.

Global tree network for computing structures enabling global processing operations

DOEpatents

Blumrich; Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

2010-01-19

A system and method for enabling high-speed, low-latency global tree network communications among processing nodes interconnected according to a tree network structure. The global tree network enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the tree via links to facilitate performance of low-latency global processing operations at nodes of the virtual tree and sub-tree structures. The global operations performed include one or more of: broadcast operations downstream from a root node to leaf nodes of a virtual tree, reduction operations upstream from leaf nodes to the root node in the virtual tree, and point-to-point message passing from any node to the root node. The global tree network is configurable to provide global barrier and interrupt functionality in asynchronous or synchronized manner, and, is physically and logically partitionable.

In-network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection

NASA Astrophysics Data System (ADS)

Albano, Michele; Gao, Jie

In a sensor network of n nodes in which k of them have sensed interesting data, we perform in-network erasure coding such that each node stores a linear combination of all the network data with random coefficients. This scheme greatly improves data resilience to node failures: as long as there are k nodes that survive an attack, all the data produced in the sensor network can be recovered with high probability. The in-network coding storage scheme also improves data collection rate by mobile mules and allows for easy scheduling of data mules.

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

PubMed Central

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

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

The ASCI Network for SC 2000: Gigabyte Per Second Networking

DOE Office of Scientific and Technical Information (OSTI.GOV)

PRATT, THOMAS J.; NAEGLE, JOHN H.; MARTINEZ JR., LUIS G.

2001-11-01

This document highlights the Discom's Distance computing and communication team activities at the 2000 Supercomputing conference in Dallas Texas. This conference is sponsored by the IEEE and ACM. Sandia's participation in the conference has now spanned a decade, for the last five years Sandia National Laboratories, Los Alamos National Lab and Lawrence Livermore National Lab have come together at the conference under the DOE's ASCI, Accelerated Strategic Computing Initiatives, Program rubric to demonstrate ASCI's emerging capabilities in computational science and our combined expertise in high performance computer science and communication networking developments within the program. At SC 2000, DISCOM demonstratedmore » an infrastructure. DISCOM2 uses this forum to demonstrate and focus communication and pre-standard implementation of 10 Gigabit Ethernet, the first gigabyte per second data IP network transfer application, and VPN technology that enabled a remote Distributed Resource Management tools demonstration. Additionally a national OC48 POS network was constructed to support applications running between the show floor and home facilities. This network created the opportunity to test PSE's Parallel File Transfer Protocol (PFTP) across a network that had similar speed and distances as the then proposed DISCOM WAN. The SCINET SC2000 showcased wireless networking and the networking team had the opportunity to explore this emerging technology while on the booth. This paper documents those accomplishments, discusses the details of their convention exhibit floor. We also supported the production networking needs of the implementation, and describes how these demonstrations supports DISCOM overall strategies in high performance computing networking.« less

Architectural and engineering issues for building an optical Internet

NASA Astrophysics Data System (ADS)

St. Arnaud, Bill

1998-10-01

Recent developments in high density Wave Division Multiplexing fiber systems allows for the deployment of a dedicated optical Internet network for large volume backbone pipes that does not require an underlying multi-service SONET/SDH and ATM transport protocol. Some intrinsic characteristics of Internet traffic such as its self similar nature, server bound congestion, routing and data asymmetry allow for highly optimized traffic engineered networks using individual wavelengths. By transmitting GigaBit Ethernet or SONET/SDH frames natively over WDM wavelengths that directly interconnect high performance routers the original concept of the Internet as an intrinsically survivable datagram network is possible. Traffic engineering, restoral, protection and bandwidth management of the network must now be carried out at the IP layer and so new routing or switching protocols such as MPLS that allow for uni- directional paths with fast restoral and protection at the IP layer become essential for a reliable production network. The deployment of high density WDM municipal and campus networks also gives carriers and ISPs the flexibility to offer customers as integrated and seamless set of optical Internet services.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

PubMed

Tian, Hao; Yan, Zhaoli; Yang, Jun

2018-04-09

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

Distributed network management in the flat structured mobile communities

NASA Astrophysics Data System (ADS)

Balandina, Elena

2005-10-01

Delivering proper management into the flat structured mobile communities is crucial for improving users experience and increase applications diversity in mobile networks. The available P2P applications do application-centric management, but it cannot replace network-wide management, especially when a number of different applications are used simultaneously in the network. The network-wide management is the key element required for a smooth transition from standalone P2P applications to the self-organizing mobile communities that maintain various services with quality and security guaranties. The classical centralized network management solutions are not applicable in the flat structured mobile communities due to the decentralized nature and high mobility of the underlying networks. Also the basic network management tasks have to be revised taking into account specialties of the flat structured mobile communities. The network performance management becomes more dependent on the current nodes' context, which also requires extension of the configuration management functionality. The fault management has to take into account high mobility of the network nodes. The performance and accounting managements are mainly targeted in maintain an efficient and fair access to the resources within the community, however they also allow unbalanced resource use of the nodes that explicitly permit it, e.g. as a voluntary donation to the community or due to the profession (commercial) reasons. The security management must implement the new trust models, which are based on the community feedback, professional authorization, and a mix of both. For fulfilling these and another specialties of the flat structured mobile communities, a new network management solution is demanded. The paper presents a distributed network management solution for flat structured mobile communities. Also the paper points out possible network management roles for the different parties (e.g. operators, service providing hubs/super nodes, etc.) involved in a service providing chain.

Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks.

PubMed

Katiravan, Jeevaa; Sylvia, D; Rao, D Srinivasa

2015-01-01

In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network.

Energy Efficient Link Aware Routing with Power Control in Wireless Ad Hoc Networks

PubMed Central

Katiravan, Jeevaa; Sylvia, D.; Rao, D. Srinivasa

2015-01-01

In wireless ad hoc networks, the traditional routing protocols make the route selection based on minimum distance between the nodes and the minimum number of hop counts. Most of the routing decisions do not consider the condition of the network such as link quality and residual energy of the nodes. Also, when a link failure occurs, a route discovery mechanism is initiated which incurs high routing overhead. If the broadcast nature and the spatial diversity of the wireless communication are utilized efficiently it becomes possible to achieve improvement in the performance of the wireless networks. In contrast to the traditional routing scheme which makes use of a predetermined route for packet transmission, such an opportunistic routing scheme defines a predefined forwarding candidate list formed by using single network metrics. In this paper, a protocol is proposed which uses multiple metrics such as residual energy and link quality for route selection and also includes a monitoring mechanism which initiates a route discovery for a poor link, thereby reducing the overhead involved and improving the throughput of the network while maintaining network connectivity. Power control is also implemented not only to save energy but also to improve the network performance. Using simulations, we show the performance improvement attained in the network in terms of packet delivery ratio, routing overhead, and residual energy of the network. PMID:26167529

The Association between Social Network Betweenness and Coronary Calcium: A Baseline Study of Patients with a High Risk of Cardiovascular Disease

PubMed Central

Joo, Won-tak; Lee, Chan Joo; Oh, Jaewon; Kim, In-Cheol; Lee, Sang-Hak; Kang, Seok-Min; Kim, Hyeon Chang; Park, Sungha; Youm, Yoosik

2018-01-01

Aim: The association of social networks with cardiovascular disease (CVD) has been demonstrated through various studies. This study aimed to examine the association between social network betweenness–a network position of mediating between diverse social groups–and coronary artery calcium. Methods: The data of 1,384 participants from the Cardiovascular and Metabolic Disease Etiology Research Center–High Risk Cohort, a prospective cohort study enrolling patients with a high risk of developing CVD (clinicaltrials.gov: NCT02003781), were analyzed. The deficiency in social network betweenness was measured in two ways: only-family networks, in which a respondent had networks with only family members, and no-cutpoint networks, in which the respondent does not function as a point of bridging between two or more social groups that are not directly connected. Results: Participants who had higher coronary artery calcium scores (CACSs) were likely to have a smaller network size (p < 0.001), only-family networks (p < 0.001), and no-cutpoint networks (p < 0.001). Multiple logistic regression analyses revealed no significant association between network size and CACS. Only no-cutpoint networks had a significant relationship with CACS > 400 (odds ratio, 1.72; 95% confidence interval, 1.07–2.77; p = 0.026). The association was stronger among older (age > 60 years) and female respondents. Conclusion: Deficiency in social network betweenness is closely related to coronary calcium in participants with a high risk of CVD. To generalize these results to a general population, further study should be performed. PMID:28740058

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Physical-layer network coding for passive optical interconnect in datacenter networks.

PubMed

Lin, Rui; Cheng, Yuxin; Guan, Xun; Tang, Ming; Liu, Deming; Chan, Chun-Kit; Chen, Jiajia

2017-07-24

We introduce physical-layer network coding (PLNC) technique in a passive optical interconnect (POI) architecture for datacenter networks. The implementation of the PLNC in the POI at 2.5 Gb/s and 10Gb/s have been experimentally validated while the gains in terms of network layer performances have been investigated by simulation. The results reveal that in order to realize negligible packet drop, the wavelengths usage can be reduced by half while a significant improvement in packet delay especially under high traffic load can be achieved by employing PLNC over POI.

Zinc oxide nanowire networks for macroelectronic devices

NASA Astrophysics Data System (ADS)

Unalan, Husnu Emrah; Zhang, Yan; Hiralal, Pritesh; Dalal, Sharvari; Chu, Daping; Eda, Goki; Teo, K. B. K.; Chhowalla, Manish; Milne, William I.; Amaratunga, Gehan A. J.

2009-04-01

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics.

Wireless Sensor Network for Radiometric Detection and Assessment of Partial Discharge in High-Voltage Equipment

NASA Astrophysics Data System (ADS)

Upton, D. W.; Saeed, B. I.; Mather, P. J.; Lazaridis, P. I.; Vieira, M. F. Q.; Atkinson, R. C.; Tachtatzis, C.; Garcia, M. S.; Judd, M. D.; Glover, I. A.

2018-03-01

Monitoring of partial discharge (PD) activity within high-voltage electrical environments is increasingly used for the assessment of insulation condition. Traditional measurement techniques employ technologies that either require off-line installation or have high power consumption and are hence costly. A wireless sensor network is proposed that utilizes only received signal strength to locate areas of PD activity within a high-voltage electricity substation. The network comprises low-power and low-cost radiometric sensor nodes which receive the radiation propagated from a source of PD. Results are reported from several empirical tests performed within a large indoor environment and a substation environment using a network of nine sensor nodes. A portable PD source emulator was placed at multiple locations within the network. Signal strength measured by the nodes is reported via WirelessHART to a data collection hub where it is processed using a location algorithm. The results obtained place the measured location within 2 m of the actual source location.

Vertically Aligned and Interconnected SiC Nanowire Networks Leading to Significantly Enhanced Thermal Conductivity of Polymer Composites.

PubMed

Yao, Yimin; Zhu, Xiaodong; Zeng, Xiaoliang; Sun, Rong; Xu, Jian-Bin; Wong, Ching-Ping

2018-03-21

Efficient heat removal via thermal management materials has become one of the most critical challenges in the development of modern microelectronic devices. However, previously reported polymer composites exhibit limited enhancement of thermal conductivity, even when highly loaded with thermally conductive fillers, because of the lack of efficient heat transfer pathways. Herein, we report vertically aligned and interconnected SiC nanowire (SiCNW) networks as efficient fillers for polymer composites, achieving significantly enhanced thermal conductivity. The SiCNW networks are produced by freeze-casting nanowire aqueous suspensions followed by thermal sintering to consolidate the nanowire junctions, exhibiting a hierarchical architecture in which honeycomb-like SiCNW layers are aligned. The composite obtained by infiltrating SiCNW networks with epoxy resin, at a relatively low SiCNW loading of 2.17 vol %, represents a high through-plane thermal conductivity (1.67 W m -1 K -1 ) compared to the pure matrix, which is equivalent to a significant enhancement of 406.6% per 1 vol % loading. The orderly SiCNW network which can act as a macroscopic expressway for phonon transport is believed to be the main contributor for the excellent thermal performance. This strategy provides insights for the design of high-performance composites with potential to be used in advanced thermal management materials.

Bacterial-cellulose-derived interconnected meso-microporous carbon nanofiber networks as binder-free electrodes for high-performance supercapacitors

NASA Astrophysics Data System (ADS)

Hao, Xiaodong; Wang, Jie; Ding, Bing; Wang, Ya; Chang, Zhi; Dou, Hui; Zhang, Xiaogang

2017-06-01

Bacterial cellulose (BC), a typical biomass prepared from the microbial fermentation process, has been proved that it can be an ideal platform for design of three-dimensional (3D) multifunctional nanomaterials in energy storage and conversion field. Here we developed a simple and general silica-assisted strategy for fabrication of interconnected 3D meso-microporous carbon nanofiber networks by confine nanospace pyrolysis of sustainable BC, which can be used as binder-free electrodes for high-performance supercapacitors. The synthesized carbon nanofibers exhibited the features of interconnected 3D networks architecture, large surface area (624 m2 g-1), mesopores-dominated hierarchical porosity, and high graphitization degree. The as-prepared electrode (CN-BC) displayed a maximum specific capacitance of 302 F g-1 at a current density of 0.5 A g-1, high-rate capability and good cyclicity in 6 M KOH electrolyte. This work, together with cost-effective preparation strategy to make high-value utilization of cheap biomass, should have significant implications in the green and mass-producible energy storage.

Design and performance evaluation of an OpenFlow-based control plane for software-defined elastic optical networks with direct-detection optical OFDM (DDO-OFDM) transmission.

PubMed

Liu, Lei; Peng, Wei-Ren; Casellas, Ramon; Tsuritani, Takehiro; Morita, Itsuro; Martínez, Ricardo; Muñoz, Raül; Yoo, S J B

2014-01-13

Optical Orthogonal Frequency Division Multiplexing (O-OFDM), which transmits high speed optical signals using multiple spectrally overlapped lower-speed subcarriers, is a promising candidate for supporting future elastic optical networks. In contrast to previous works which focus on Coherent Optical OFDM (CO-OFDM), in this paper, we consider the direct-detection optical OFDM (DDO-OFDM) as the transport technique, which leads to simpler hardware and software realizations, potentially offering a low-cost solution for elastic optical networks, especially in metro networks, and short or medium distance core networks. Based on this network scenario, we design and deploy a software-defined networking (SDN) control plane enabled by extending OpenFlow, detailing the network architecture, the routing and spectrum assignment algorithm, OpenFlow protocol extensions and the experimental validation. To the best of our knowledge, it is the first time that an OpenFlow-based control plane is reported and its performance is quantitatively measured in an elastic optical network with DDO-OFDM transmission.

Global connectivity of prefrontal cortex predicts cognitive control and intelligence

PubMed Central

Cole, Michael W.; Yarkoni, Tal; Repovs, Grega; Anticevic, Alan; Braver, Todd S.

2012-01-01

Control of thought and behavior is fundamental to human intelligence. Evidence suggests a fronto-parietal brain network implements such cognitive control across diverse contexts. We identify a mechanism – global connectivity – by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region’s activity was found to predict performance in a high control demand working memory task, and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the fronto-parietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brain-wide influence that facilitates the ability to implement control processes central to human intelligence. PMID:22745498

A Framework for Dimensioning VDL-2 Air-Ground Networks

NASA Technical Reports Server (NTRS)

Ribeiro, Leila Z.; Monticone, Leone C.; Snow, Richard E.; Box, Frank; Apaza, Rafel; Bretmersky, Steven

2014-01-01

This paper describes a framework developed at MITRE for dimensioning a Very High Frequency (VHF) Digital Link Mode 2 (VDL-2) Air-to-Ground network. This framework was developed to support the FAA's Data Communications (Data Comm) program by providing estimates of expected capacity required for the air-ground network services that will support Controller-Pilot-Data-Link Communications (CPDLC), as well as the spectrum needed to operate the system at required levels of performance. The Data Comm program is part of the FAA's NextGen initiative to implement advanced communication capabilities in the National Airspace System (NAS). The first component of the framework is the radio-frequency (RF) coverage design for the network ground stations. Then we proceed to describe the approach used to assess the aircraft geographical distribution and the data traffic demand expected in the network. The next step is the resource allocation utilizing optimization algorithms developed in MITRE's Spectrum ProspectorTM tool to propose frequency assignment solutions, and a NASA-developed VDL-2 tool to perform simulations and determine whether a proposed plan meets the desired performance requirements. The framework presented is capable of providing quantitative estimates of multiple variables related to the air-ground network, in order to satisfy established coverage, capacity and latency performance requirements. Outputs include: coverage provided at different altitudes; data capacity required in the network, aggregated or on a per ground station basis; spectrum (pool of frequencies) needed for the system to meet a target performance; optimized frequency plan for a given scenario; expected performance given spectrum available; and, estimates of throughput distributions for a given scenario. We conclude with a discussion aimed at providing insight into the tradeoffs and challenges identified with respect to radio resource management for VDL-2 air-ground networks.

Individual differences in sentence comprehension: a functional magnetic resonance imaging investigation of syntactic and lexical processing demands.

PubMed

Prat, Chantel S; Keller, Timothy A; Just, Marcel Adam

2007-12-01

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network's properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network's response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance.

DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

PubMed

Kim, Lok-Won

2018-05-01

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

Multichannel Detection in High-Performance Liquid Chromatography.

ERIC Educational Resources Information Center

Miller, James C.; And Others

1982-01-01

A linear photodiode array is used as the photodetector element in a new ultraviolet-visible detection system for high-performance liquid chromatography (HPLC). Using a computer network, the system processes eight different chromatographic signals simultaneously in real-time and acquires spectra manually/automatically. Applications in fast HPLC…

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

JPARSS: A Java Parallel Network Package for Grid Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Jie; Akers, Walter; Chen, Ying

2002-03-01

The emergence of high speed wide area networks makes grid computinga reality. However grid applications that need reliable data transfer still have difficulties to achieve optimal TCP performance due to network tuning of TCP window size to improve bandwidth and to reduce latency on a high speed wide area network. This paper presents a Java package called JPARSS (Java Parallel Secure Stream (Socket)) that divides data into partitions that are sent over several parallel Java streams simultaneously and allows Java or Web applications to achieve optimal TCP performance in a grid environment without the necessity of tuning TCP window size.more » This package enables single sign-on, certificate delegation and secure or plain-text data transfer using several security components based on X.509 certificate and SSL. Several experiments will be presented to show that using Java parallelstreams is more effective than tuning TCP window size. In addition a simple architecture using Web services« less

Nonuniform traffic spots (NUTS) in multistage interconnection networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lang, T.; Kurisaki, L.

1990-09-01

The performance of multistage interconnection networks for multiprocessors is degraded when the traffic pattern produces nonuniform congestion in the blocking switches, that is, when there exist nonuniform traffic spots. For some specific patterns the authors evaluate this degradation in performance and propose modifications to the network organization and operation to reduce the degradation. Successful modifications are the use of diverting switches and the extension of the network with additional links. The use of these modifications makes the network more effective for a larger variety of traffic patterns. The authors also consider the case in which the network carries the superpositionmore » of two types of traffic. One type is the high throughput data and instruction traffic, while the other consists of control and I/O packets which are of low throughput but have severe real-time constraints. The authors conclude that diverting switches and networks with additional links are also suitable for assuring low latency for the real-time traffic, especially when using the displacing mode.« less

Modeling and simulation of the data communication network at the ASRM Facility

NASA Technical Reports Server (NTRS)

Nirgudkar, R. P.; Moorhead, R. J.; Smith, W. D.

1994-01-01

This paper describes the modeling and simulation of the communication network for the NASA Advanced Solid Rocket Motor (ASRM) facility under construction at Yellow Creek near Luka, Mississippi. Manufacturing, testing, and operations at the ASRM site will be performed in different buildings scattered over an 1800 acre site. These buildings are interconnected through a local area network (LAN), which will contain one logical Fiber Distributed Data Interface (FDDI) ring acting as a backbone for the whole complex. The network contains approximately 700 multi-vendor workstations, 22 multi-vendor workcells, and 3 VAX clusters interconnected via Ethernet and FDDI. The different devices produce appreciably different traffic patterns, each pattern will be highly variable, and some patterns will be very bursty. Most traffic is between the VAX clusters and the other devices. Comdisco's Block Oriented Network Simulator (BONeS) has been used for network simulation. The two primary evaluation parameters used to judge the expected network performance are throughput and delay.

Spontaneous Ad Hoc Mobile Cloud Computing Network

PubMed Central

Lacuesta, Raquel; Sendra, Sandra; Peñalver, Lourdes

2014-01-01

Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes. PMID:25202715

Spontaneous ad hoc mobile cloud computing network.

PubMed

Lacuesta, Raquel; Lloret, Jaime; Sendra, Sandra; Peñalver, Lourdes

2014-01-01

Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes.

A dual-scale metal nanowire network transparent conductor for highly efficient and flexible organic light emitting diodes.

PubMed

Lee, Jinhwan; An, Kunsik; Won, Phillip; Ka, Yoonseok; Hwang, Hyejin; Moon, Hyunjin; Kwon, Yongwon; Hong, Sukjoon; Kim, Changsoon; Lee, Changhee; Ko, Seung Hwan

2017-02-02

Although solution processed metal nanowire (NW) percolation networks are a strong candidate to replace commercial indium tin oxide, their performance is limited in thin film device applications due to reduced effective electrical areas arising from the dimple structure and percolative voids that single size metal NW percolation networks inevitably possess. Here, we present a transparent electrode based on a dual-scale silver nanowire (AgNW) percolation network embedded in a flexible substrate to demonstrate a significant enhancement in the effective electrical area by filling the large percolative voids present in a long/thick AgNW network with short/thin AgNWs. As a proof of concept, the performance enhancement of a flexible phosphorescent OLED is demonstrated with the dual-scale AgNW percolation network compared to the previous mono-scale AgNWs. Moreover, we report that mechanical and oxidative robustness, which are critical for flexible OLEDs, are greatly increased by embedding the dual-scale AgNW network in a resin layer.

Grand Challenges 1993: High Performance Computing and Communications. A Report by the Committee on Physical, Mathematical, and Engineering Sciences. The FY 1993 U.S. Research and Development Program.

ERIC Educational Resources Information Center

Office of Science and Technology Policy, Washington, DC.

This report presents the United States research and development program for 1993 for high performance computing and computer communications (HPCC) networks. The first of four chapters presents the program goals and an overview of the federal government's emphasis on high performance computing as an important factor in the nation's scientific and…

The challenges of archiving networked-based multimedia performances (Performance cryogenics)

NASA Astrophysics Data System (ADS)

Cohen, Elizabeth; Cooperstock, Jeremy; Kyriakakis, Chris

2002-11-01

Music archives and libraries have cultural preservation at the core of their charters. New forms of art often race ahead of the preservation infrastructure. The ability to stream multiple synchronized ultra-low latency streams of audio and video across a continent for a distributed interactive performance such as music and dance with high-definition video and multichannel audio raises a series of challenges for the architects of digital libraries and those responsible for cultural preservation. The archiving of such performances presents numerous challenges that go beyond simply recording each stream. Case studies of storage and subsequent retrieval issues for Internet2 collaborative performances are discussed. The development of shared reality and immersive environments generate issues about, What constitutes an archived performance that occurs across a network (in multiple spaces over time)? What are the families of necessary metadata to reconstruct this virtual world in another venue or era? For example, if the network exhibited changes in latency the performers most likely adapted. In a future recreation, the latency will most likely be completely different. We discuss the parameters of immersive environment acquisition and rendering, network architectures, software architecture, musical/choreographic scores, and environmental acoustics that must be considered to address this problem.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

PubMed

Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

High Performance Implementation of 3D Convolutional Neural Networks on a GPU

PubMed Central

Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE PAGES

Yoo, Wucherl; Sim, Alex

2016-06-24

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Radio Synthesis Imaging - A High Performance Computing and Communications Project

NASA Astrophysics Data System (ADS)

Crutcher, Richard M.

The National Science Foundation has funded a five-year High Performance Computing and Communications project at the National Center for Supercomputing Applications (NCSA) for the direct implementation of several of the computing recommendations of the Astronomy and Astrophysics Survey Committee (the "Bahcall report"). This paper is a summary of the project goals and a progress report. The project will implement a prototype of the next generation of astronomical telescope systems - remotely located telescopes connected by high-speed networks to very high performance, scalable architecture computers and on-line data archives, which are accessed by astronomers over Gbit/sec networks. Specifically, a data link has been installed between the BIMA millimeter-wave synthesis array at Hat Creek, California and NCSA at Urbana, Illinois for real-time transmission of data to NCSA. Data are automatically archived, and may be browsed and retrieved by astronomers using the NCSA Mosaic software. In addition, an on-line digital library of processed images will be established. BIMA data will be processed on a very high performance distributed computing system, with I/O, user interface, and most of the software system running on the NCSA Convex C3880 supercomputer or Silicon Graphics Onyx workstations connected by HiPPI to the high performance, massively parallel Thinking Machines Corporation CM-5. The very computationally intensive algorithms for calibration and imaging of radio synthesis array observations will be optimized for the CM-5 and new algorithms which utilize the massively parallel architecture will be developed. Code running simultaneously on the distributed computers will communicate using the Data Transport Mechanism developed by NCSA. The project will also use the BLANCA Gbit/s testbed network between Urbana and Madison, Wisconsin to connect an Onyx workstation in the University of Wisconsin Astronomy Department to the NCSA CM-5, for development of long-distance distributed computing. Finally, the project is developing 2D and 3D visualization software as part of the international AIPS++ project. This research and development project is being carried out by a team of experts in radio astronomy, algorithm development for massively parallel architectures, high-speed networking, database management, and Thinking Machines Corporation personnel. The development of this complete software, distributed computing, and data archive and library solution to the radio astronomy computing problem will advance our expertise in high performance computing and communications technology and the application of these techniques to astronomical data processing.

An Efficient Framework Model for Optimizing Routing Performance in VANETs.

PubMed

Al-Kharasani, Nori M; Zulkarnain, Zuriati Ahmad; Subramaniam, Shamala; Hanapi, Zurina Mohd

2018-02-15

Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED).

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

PubMed Central

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-01-01

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance. PMID:28471387

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

PubMed

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-05-04

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA.

PubMed

Carvlin, Graeme N; Lugo, Humberto; Olmedo, Luis; Bejarano, Ester; Wilkie, Alexa; Meltzer, Dan; Wong, Michelle; King, Galatea; Northcross, Amanda; Jerrett, Michael; English, Paul B; Hammond, Donald; Seto, Edmund

2017-12-01

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM 2.5 and PM 10 (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R 2 for converted hourly averaged Dylos mass measurements versus a PM 2.5 BAM was 0.79 and that versus a PM 10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM 2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R 2 = 0.35-0.81). The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

MANEMO Routing in Practice: Protocol Selection, Expected Performance, and Experimental Evaluation

NASA Astrophysics Data System (ADS)

Tazaki, Hajime; van Meter, Rodney; Wakikawa, Ryuji; Wongsaardsakul, Thirapon; Kanchanasut, Kanchana; Dias de Amorim, Marcelo; Murai, Jun

Motivated by the deployment of post-disaster MANEMO (MANET for NEMO) composed of mobile routers and stations, we evaluate two candidate routing protocols through network simulation, theoretical performance analysis, and field experiments. The first protocol is the widely adopted Optimized Link State Routing protocol (OLSR) and the second is the combination of the Tree Discovery Protocol (TDP) with Network In Node Advertisement (NINA). To the best of our knowledge, this is the first time that these two protocols are compared in both theoretical and practical terms. We focus on the control overhead generated when mobile routers perform a handover. Our results confirm the correctness and operational robustness of both protocols. More interestingly, although in the general case OLSR leads to better results, TDP/NINA outperforms OLSR both in the case of sparse networks and in highly mobile networks, which correspond to the operation point of a large set of post-disaster scenarios.

Schemes for efficient transmission of encoded video streams on high-speed networks

NASA Astrophysics Data System (ADS)

Ramanathan, Srinivas; Vin, Harrick M.; Rangan, P. Venkat

1994-04-01

In this paper, we argue that significant performance benefits can accrue if integrated networks implement application-specific mechanisms that account for the diversities in media compression schemes. Towards this end, we propose a simple, yet effective, strategy called Frame Induced Packet Discarding (FIPD), in which, upon detection of loss of a threshold number (determined by an application's video encoding scheme) of packets belonging to a video frame, the network attempts to discard all the remaining packets of that frame. In order to analytically quantify the performance of FIPD so as to obtain fractional frame losses that can be guaranteed to video channels, we develop a finite state, discrete time markov chain model of the FIPD strategy. The fractional frame loss thus computed can serve as the criterion for admission control at the network. Performance evaluations demonstrate the utility of the FIPD strategy.

An efficient group multicast routing for multimedia communication

NASA Astrophysics Data System (ADS)

Wang, Yanlin; Sun, Yugen; Yan, Xinfang

2004-04-01

Group multicasting is a kind of communication mechanism whereby each member of a group sends messages to all the other members of the same group. Group multicast routing algorithms capable of satisfying quality of service (QoS) requirements of multimedia applications are essential for high-speed networks. We present a heuristic algorithm for group multicast routing with end to end delay constraint. Source-specific routing trees for each member are generated in our algorithm, which satisfy member"s bandwidth and end to end delay requirements. Simulations over random network were carried out to compare proposed algorithm performance with Low and Song"s. The experimental results show that our proposed algorithm performs better in terms of network cost and ability in constructing feasible multicast trees for group members. Moreover, our algorithm achieves good performance in balancing traffic, which can avoid link blocking and enhance the network behavior efficiently.

Interconnect Performance Evaluation of SGI Altix 3700 BX2, Cray X1, Cray Opteron Cluster, and Dell PowerEdge

NASA Technical Reports Server (NTRS)

Fatoohi, Rod; Saini, Subbash; Ciotti, Robert

2006-01-01

We study the performance of inter-process communication on four high-speed multiprocessor systems using a set of communication benchmarks. The goal is to identify certain limiting factors and bottlenecks with the interconnect of these systems as well as to compare these interconnects. We measured network bandwidth using different number of communicating processors and communication patterns, such as point-to-point communication, collective communication, and dense communication patterns. The four platforms are: a 512-processor SGI Altix 3700 BX2 shared-memory machine with 3.2 GB/s links; a 64-processor (single-streaming) Cray XI shared-memory machine with 32 1.6 GB/s links; a 128-processor Cray Opteron cluster using a Myrinet network; and a 1280-node Dell PowerEdge cluster with an InfiniBand network. Our, results show the impact of the network bandwidth and topology on the overall performance of each interconnect.

Outstanding Li-storage performance of LiFePO4@MWCNTs cathode material with 3D network structure for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Sun, Xiaodong; Zhang, Le

2018-05-01

In this work, the MWCNTs-decorated LiFePO4 microspheres (LiFePO4@MWCNTs) with a 3D network structure have been synthesized by a facile and efficient spray-drying approach followed by solid-state reaction in a reduction atmosphere. In the as-prepared composite, the MWCNTs around LiFePO4 nanoparticles can provide 3D conductive networks which greatly facilitate the transport of Li+-ion and electron during the electrochemical reaction. Compared to the pure LiFePO4 material, the LiFePO4@MWCNTs composite as cathode for lithium-ion batteries exhibits significantly improved Li-storage performance in terms of rate capability and cyclic stability. Therefore, we can speculate that the spray-drying approach is a promising route to prepare the high-performance electrode materials with 3D network structure for electrochemical energy storage.

Co-Percolating Graphene-Wrapped Silver Nanowire Network for High Performance, Highly Stable, Transparent Conducting Electrodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Ruiyi; Das, Suprem R; Jeong, Changwook

Transparent conducting electrodes (TCEs) require high transparency and low sheet resistance for applications in photovoltaics, photodetectors, flat panel displays, touch screen devices, and imagers. Indium tin oxide (ITO), or other transparent conductive oxides, have been used, and provide a baseline sheet resistance (RS) vs. transparency (T) relationship. Several alternative material systems have been investigated. The development of high-performance hybrid structures provides a route towards robust, scalable and low-cost approaches for realizing high-performance TCE.

From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution

PubMed Central

Hortelano, Diego; Olivares, Teresa; Ruiz, M. Carmen; Garrido-Hidalgo, Celia; López, Vicente

2017-01-01

Current sensor networks need to be improved and updated to satisfy new essential requirements of the Internet of Things, where cutting-edge applications will appear. These requirements are: total coverage, zero fails (high performance), scalability and sustainability (hardware and software). We are going to evaluate Bluetooth Low Energy as wireless transmission technology and as the ideal candidate for these improvements, due to its low power consumption, its low cost radio chips and its ability to communicate with users directly, using their smartphones or smartbands. However, this technology is relatively recent, and standard network topologies are not able to fulfil its new requirements. To address these shortcomings, the implementation of other more flexible topologies (as the mesh topology) will be very interesting. After studying it in depth, we have identified certain weaknesses, for example, specific devices are needed to provide network scalability, and the need to choose between high performance or sustainability. In this paper, after presenting the studies carried out on these new technologies, we propose a new packet format and a new BLE mesh topology, with two different configurations: Individual Mesh and Collaborative Mesh. Our results show how this topology improves the scalability, sustainability, coverage and performance. PMID:28216560

From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution.

PubMed

Hortelano, Diego; Olivares, Teresa; Ruiz, M Carmen; Garrido-Hidalgo, Celia; López, Vicente

2017-02-14

Current sensor networks need to be improved and updated to satisfy new essential requirements of the Internet of Things, where cutting-edge applications will appear. These requirements are: total coverage, zero fails (high performance), scalability and sustainability (hardware and software). We are going to evaluate Bluetooth Low Energy as wireless transmission technology and as the ideal candidate for these improvements, due to its low power consumption, its low cost radio chips and its ability to communicate with users directly, using their smartphones or smartbands. However, this technology is relatively recent, and standard network topologies are not able to fulfil its new requirements. To address these shortcomings, the implementation of other more flexible topologies (as the mesh topology) will be very interesting. After studying it in depth, we have identified certain weaknesses, for example, specific devices are needed to provide network scalability, and the need to choose between high performance or sustainability. In this paper, after presenting the studies carried out on these new technologies, we propose a new packet format and a new BLE mesh topology, with two different configurations: Individual Mesh and Collaborative Mesh . Our results show how this topology improves the scalability, sustainability, coverage and performance.

Modeling a Wireless Network for International Space Station

NASA Technical Reports Server (NTRS)

Alena, Richard; Yaprak, Ece; Lamouri, Saad

2000-01-01

This paper describes the application of wireless local area network (LAN) simulation modeling methods to the hybrid LAN architecture designed for supporting crew-computing tools aboard the International Space Station (ISS). These crew-computing tools, such as wearable computers and portable advisory systems, will provide crew members with real-time vehicle and payload status information and access to digital technical and scientific libraries, significantly enhancing human capabilities in space. A wireless network, therefore, will provide wearable computer and remote instruments with the high performance computational power needed by next-generation 'intelligent' software applications. Wireless network performance in such simulated environments is characterized by the sustainable throughput of data under different traffic conditions. This data will be used to help plan the addition of more access points supporting new modules and more nodes for increased network capacity as the ISS grows.

Planning Multitechnology Access Networks with Performance Constraints

NASA Astrophysics Data System (ADS)

Chamberland, Steven

Considering the number of access network technologies and the investment needed for the “last mile” of a solution, in today’s highly competitive markets, planning tools are crucial for the service providers to optimize the network costs and accelerate the planning process. In this paper, we propose to tackle the problem of planning access networks composed of four technologies/architectures: the digital subscriber line (xDSL) technologies deployed directly from the central office (CO), the fiber-to-the-node (FTTN), the fiber-to-the-micro-node (FTTn) and the fiber-to-the-premises (FTTP). A mathematical programming model is proposed for this planning problem that is solved using a commercial implementation of the branch-and-bound algorithm. Next, a detailed access network planning example is presented followed by a systematic set of experiments designed to assess the performance of the proposed approach.

High Performance Data Transfer for Distributed Data Intensive Sciences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Chin; Cottrell, R 'Les' A.; Hanushevsky, Andrew B.

We report on the development of ZX software providing high performance data transfer and encryption. The design scales in: computation power, network interfaces, and IOPS while carefully balancing the available resources. Two U.S. patent-pending algorithms help tackle data sets containing lots of small files and very large files, and provide insensitivity to network latency. It has a cluster-oriented architecture, using peer-to-peer technologies to ease deployment, operation, usage, and resource discovery. Its unique optimizations enable effective use of flash memory. Using a pair of existing data transfer nodes at SLAC and NERSC, we compared its performance to that of bbcp andmore » GridFTP and determined that they were comparable. With a proof of concept created using two four-node clusters with multiple distributed multi-core CPUs, network interfaces and flash memory, we achieved 155Gbps memory-to-memory over a 2x100Gbps link aggregated channel and 70Gbps file-to-file with encryption over a 5000 mile 100Gbps link.« less

Biological network motif detection and evaluation

PubMed Central

2011-01-01

Background Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. Results We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. Conclusion We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks. PMID:22784624

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bobyshev, A.; DeMar, P.; Grigaliunas, V.

The LHC is entering its fourth year of production operation. Most Tier1 facilities have been in operation for almost a decade, when development and ramp-up efforts are included. LHC's distributed computing model is based on the availability of high capacity, high performance network facilities for both the WAN and LAN data movement, particularly within the Tier1 centers. As a result, the Tier1 centers tend to be on the leading edge of data center networking technology. In this paper, we analyze past and current developments in Tier1 LAN networking, as well as extrapolating where we anticipate networking technology is heading. Ourmore » analysis will include examination into the following areas: Evolution of Tier1 centers to their current state Evolving data center networking models and how they apply to Tier1 centers Impact of emerging network technologies (e.g. 10GE-connected hosts, 40GE/100GE links, IPv6) on Tier1 centers Trends in WAN data movement and emergence of software-defined WAN network capabilities Network virtualization« less

Wavelets and Elman Neural Networks for monitoring environmental variables

NASA Astrophysics Data System (ADS)

Ciarlini, Patrizia; Maniscalco, Umberto

2008-11-01

An application in cultural heritage is introduced. Wavelet decomposition and Neural Networks like virtual sensors are jointly used to simulate physical and chemical measurements in specific locations of a monument. Virtual sensors, suitably trained and tested, can substitute real sensors in monitoring the monument surface quality, while the real ones should be installed for a long time and at high costs. The application of the wavelet decomposition to the environmental data series allows getting the treatment of underlying temporal structure at low frequencies. Consequently a separate training of suitable Elman Neural Networks for high/low components can be performed, thus improving the networks convergence in learning time and measurement accuracy in working time.

Core networks and their reconfiguration patterns across cognitive loads.

PubMed

Zuo, Nianming; Yang, Zhengyi; Liu, Yong; Li, Jin; Jiang, Tianzi

2018-04-20

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks. © 2018 Wiley Periodicals, Inc.

Age, sex, and performance influence the visuospatial working memory network in childhood.

PubMed

Spencer-Smith, Megan; Ritter, Barbara Catherine; Mürner-Lavanchy, Ines; El-Koussy, Marwan; Steinlin, Maja; Everts, Regula

2013-01-01

This study describes the influence of age, sex, and working memory (WM) performance on the visuospatial WM network. Thirty-nine healthy children (7-12 years) completed a dot location functional magnetic resonance imaging (fMRI) task. Percent signal change measured the intensity and laterality indices measured the asymmetry of activation in frontal and parietal brain regions. Old children showed greater intensity of activation in parietal regions than young children but no differences in lateralization were observed. Intensity of activation was similar across sex and WM performance groups. Girls and high WM performers showed more right-sided lateralization of parietal regions than boys and low WM performers.

Scalable Optical-Fiber Communication Networks

NASA Technical Reports Server (NTRS)

Chow, Edward T.; Peterson, John C.

1993-01-01

Scalable arbitrary fiber extension network (SAFEnet) is conceptual fiber-optic communication network passing digital signals among variety of computers and input/output devices at rates from 200 Mb/s to more than 100 Gb/s. Intended for use with very-high-speed computers and other data-processing and communication systems in which message-passing delays must be kept short. Inherent flexibility makes it possible to match performance of network to computers by optimizing configuration of interconnections. In addition, interconnections made redundant to provide tolerance to faults.

Research on TCP/IP network communication based on Node.js

NASA Astrophysics Data System (ADS)

Huang, Jing; Cai, Lixiong

2018-04-01

In the face of big data, long connection and high synchronization, TCP/IP network communication will cause performance bottlenecks due to its blocking multi-threading service model. This paper presents a method of TCP/IP network communication protocol based on Node.js. On the basis of analyzing the characteristics of Node.js architecture and asynchronous non-blocking I/O model, the principle of its efficiency is discussed, and then compare and analyze the network communication model of TCP/IP protocol to expound the reasons why TCP/IP protocol stack is widely used in network communication. Finally, according to the large data and high concurrency in the large-scale grape growing environment monitoring process, a TCP server design based on Node.js is completed. The results show that the example runs stably and efficiently.

A neural networks-based hybrid routing protocol for wireless mesh networks.

PubMed

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

PubMed Central

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

birgHPC: creating instant computing clusters for bioinformatics and molecular dynamics.

PubMed

Chew, Teong Han; Joyce-Tan, Kwee Hong; Akma, Farizuwana; Shamsir, Mohd Shahir

2011-05-01

birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware. The birgHPC Live CD and relevant user guide are available for free at http://birg1.fbb.utm.my/birghpc.

Reliable and Fault-Tolerant Software-Defined Network Operations Scheme for Remote 3D Printing

NASA Astrophysics Data System (ADS)

Kim, Dongkyun; Gil, Joon-Min

2015-03-01

The recent wide expansion of applicable three-dimensional (3D) printing and software-defined networking (SDN) technologies has led to a great deal of attention being focused on efficient remote control of manufacturing processes. SDN is a renowned paradigm for network softwarization, which has helped facilitate remote manufacturing in association with high network performance, since SDN is designed to control network paths and traffic flows, guaranteeing improved quality of services by obtaining network requests from end-applications on demand through the separated SDN controller or control plane. However, current SDN approaches are generally focused on the controls and automation of the networks, which indicates that there is a lack of management plane development designed for a reliable and fault-tolerant SDN environment. Therefore, in addition to the inherent advantage of SDN, this paper proposes a new software-defined network operations center (SD-NOC) architecture to strengthen the reliability and fault-tolerance of SDN in terms of network operations and management in particular. The cooperation and orchestration between SDN and SD-NOC are also introduced for the SDN failover processes based on four principal SDN breakdown scenarios derived from the failures of the controller, SDN nodes, and connected links. The abovementioned SDN troubles significantly reduce the network reachability to remote devices (e.g., 3D printers, super high-definition cameras, etc.) and the reliability of relevant control processes. Our performance consideration and analysis results show that the proposed scheme can shrink operations and management overheads of SDN, which leads to the enhancement of responsiveness and reliability of SDN for remote 3D printing and control processes.

Advanced digital signal processing for short-haul and access network

NASA Astrophysics Data System (ADS)

Zhang, Junwen; Yu, Jianjun; Chi, Nan

2016-02-01

Digital signal processing (DSP) has been proved to be a successful technology recently in high speed and high spectrum-efficiency optical short-haul and access network, which enables high performances based on digital equalizations and compensations. In this paper, we investigate advanced DSP at the transmitter and receiver side for signal pre-equalization and post-equalization in an optical access network. A novel DSP-based digital and optical pre-equalization scheme has been proposed for bandwidth-limited high speed short-distance communication system, which is based on the feedback of receiver-side adaptive equalizers, such as least-mean-squares (LMS) algorithm and constant or multi-modulus algorithms (CMA, MMA). Based on this scheme, we experimentally demonstrate 400GE on a single optical carrier based on the highest ETDM 120-GBaud PDM-PAM-4 signal, using one external modulator and coherent detection. A line rate of 480-Gb/s is achieved, which enables 20% forward-error correction (FEC) overhead to keep the 400-Gb/s net information rate. The performance after fiber transmission shows large margin for both short range and metro/regional networks. We also extend the advanced DSP for short haul optical access networks by using high order QAMs. We propose and demonstrate a high speed multi-band CAP-WDM-PON system on intensity modulation, direct detection and digital equalizations. A hybrid modified cascaded MMA post-equalization schemes are used to equalize the multi-band CAP-mQAM signals. Using this scheme, we successfully demonstrates 550Gb/s high capacity WDMPON system with 11 WDM channels, 55 sub-bands, and 10-Gb/s per user in the downstream over 40-km SMF.

RAID-2: Design and implementation of a large scale disk array controller

NASA Technical Reports Server (NTRS)

Katz, R. H.; Chen, P. M.; Drapeau, A. L.; Lee, E. K.; Lutz, K.; Miller, E. L.; Seshan, S.; Patterson, D. A.

1992-01-01

We describe the implementation of a large scale disk array controller and subsystem incorporating over 100 high performance 3.5 inch disk drives. It is designed to provide 40 MB/s sustained performance and 40 GB capacity in three 19 inch racks. The array controller forms an integral part of a file server that attaches to a Gb/s local area network. The controller implements a high bandwidth interconnect between an interleaved memory, an XOR calculation engine, the network interface (HIPPI), and the disk interfaces (SCSI). The system is now functionally operational, and we are tuning its performance. We review the design decisions, history, and lessons learned from this three year university implementation effort to construct a truly large scale system assembly.

Dynamic Social Networks in High Performance Football Coaching

ERIC Educational Resources Information Center

Occhino, Joseph; Mallett, Cliff; Rynne, Steven

2013-01-01

Background: Sports coaching is largely a social activity where engagement with athletes and support staff can enhance the experiences for all involved. This paper examines how high performance football coaches develop knowledge through their interactions with others within a social learning theory framework. Purpose: The key purpose of this study…

High-performance, scalable optical network-on-chip architectures

NASA Astrophysics Data System (ADS)

Tan, Xianfang

The rapid advance of technology enables a large number of processing cores to be integrated into a single chip which is called a Chip Multiprocessor (CMP) or a Multiprocessor System-on-Chip (MPSoC) design. The on-chip interconnection network, which is the communication infrastructure for these processing cores, plays a central role in a many-core system. With the continuously increasing complexity of many-core systems, traditional metallic wired electronic networks-on-chip (NoC) became a bottleneck because of the unbearable latency in data transmission and extremely high energy consumption on chip. Optical networks-on-chip (ONoC) has been proposed as a promising alternative paradigm for electronic NoC with the benefits of optical signaling communication such as extremely high bandwidth, negligible latency, and low power consumption. This dissertation focus on the design of high-performance and scalable ONoC architectures and the contributions are highlighted as follow: 1. A micro-ring resonator (MRR)-based Generic Wavelength-routed Optical Router (GWOR) is proposed. A method for developing any sized GWOR is introduced. GWOR is a scalable non-blocking ONoC architecture with simple structure, low cost and high power efficiency compared to existing ONoC designs. 2. To expand the bandwidth and improve the fault tolerance of the GWOR, a redundant GWOR architecture is designed by cascading different type of GWORs into one network. 3. The redundant GWOR built with MRR-based comb switches is proposed. Comb switches can expand the bandwidth while keep the topology of GWOR unchanged by replacing the general MRRs with comb switches. 4. A butterfly fat tree (BFT)-based hybrid optoelectronic NoC (HONoC) architecture is developed in which GWORs are used for global communication and electronic routers are used for local communication. The proposed HONoC uses less numbers of electronic routers and links than its counterpart of electronic BFT-based NoC. It takes the advantages of GWOR in optical communication and BFT in non-uniform traffic communication and three-dimension (3D) implementation. 5. A cycle-accurate NoC simulator is developed to evaluate the performance of proposed HONoC architectures. It is a comprehensive platform that can simulate both electronic and optical NoCs. Different size HONoC architectures are evaluated in terms of throughput, latency and energy dissipation. Simulation results confirm that HONoC achieves good network performance with lower power consumption.

Measurement-Driven Characterization of the Mobile Environment

ERIC Educational Resources Information Center

Soroush, Hamed

2013-01-01

The concurrent deployment of high-quality wireless networks and large-scale cloud services offers the promise of secure ubiquitous access to seemingly limitless amount of content. However, as users' expectations have grown more demanding, the performance and connectivity failures endemic to the existing networking infrastructure have become more…

Modeling and performance analysis using extended fuzzy-timing Petri nets for networked virtual environments.

PubMed

Zhou, Y; Murata, T; Defanti, T A

2000-01-01

Despite their attractive properties, networked virtual environments (net-VEs) are notoriously difficult to design, implement, and test due to the concurrency, real-time and networking features in these systems. Net-VEs demand high quality-of-service (QoS) requirements on the network to maintain natural and real-time interactions among users. The current practice for net-VE design is basically trial and error, empirical, and totally lacks formal methods. This paper proposes to apply a Petri net formal modeling technique to a net-VE-NICE (narrative immersive constructionist/collaborative environment), predict the net-VE performance based on simulation, and improve the net-VE performance. NICE is essentially a network of collaborative virtual reality systems called the CAVE-(CAVE automatic virtual environment). First, we introduce extended fuzzy-timing Petri net (EFTN) modeling and analysis techniques. Then, we present EFTN models of the CAVE, NICE, and transport layer protocol used in NICE: transmission control protocol (TCP). We show the possibility analysis based on the EFTN model for the CAVE. Then, by using these models and design/CPN as the simulation tool, we conducted various simulations to study real-time behavior, network effects and performance (latencies and jitters) of NICE. Our simulation results are consistent with experimental data.

Real-time visual communication to aid disaster recovery in a multi-segment hybrid wireless networking system

NASA Astrophysics Data System (ADS)

Al Hadhrami, Tawfik; Wang, Qi; Grecos, Christos

2012-06-01

When natural disasters or other large-scale incidents occur, obtaining accurate and timely information on the developing situation is vital to effective disaster recovery operations. High-quality video streams and high-resolution images, if available in real time, would provide an invaluable source of current situation reports to the incident management team. Meanwhile, a disaster often causes significant damage to the communications infrastructure. Therefore, another essential requirement for disaster management is the ability to rapidly deploy a flexible incident area communication network. Such a network would facilitate the transmission of real-time video streams and still images from the disrupted area to remote command and control locations. In this paper, a comprehensive end-to-end video/image transmission system between an incident area and a remote control centre is proposed and implemented, and its performance is experimentally investigated. In this study a hybrid multi-segment communication network is designed that seamlessly integrates terrestrial wireless mesh networks (WMNs), distributed wireless visual sensor networks, an airborne platform with video camera balloons, and a Digital Video Broadcasting- Satellite (DVB-S) system. By carefully integrating all of these rapidly deployable, interworking and collaborative networking technologies, we can fully exploit the joint benefits provided by WMNs, WSNs, balloon camera networks and DVB-S for real-time video streaming and image delivery in emergency situations among the disaster hit area, the remote control centre and the rescue teams in the field. The whole proposed system is implemented in a proven simulator. Through extensive simulations, the real-time visual communication performance of this integrated system has been numerically evaluated, towards a more in-depth understanding in supporting high-quality visual communications in such a demanding context.

A comparison study between MLP and convolutional neural network models for character recognition

NASA Astrophysics Data System (ADS)

Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

2017-05-01

Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-07-01

With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

Unified pipe network method for simulation of water flow in fractured porous rock

NASA Astrophysics Data System (ADS)

Ren, Feng; Ma, Guowei; Wang, Yang; Li, Tuo; Zhu, Hehua

2017-04-01

Rock masses are often conceptualized as dual-permeability media containing fractures or fracture networks with high permeability and porous matrix that is less permeable. In order to overcome the difficulties in simulating fluid flow in a highly discontinuous dual-permeability medium, an effective unified pipe network method is developed, which discretizes the dual-permeability rock mass into a virtual pipe network system. It includes fracture pipe networks and matrix pipe networks. They are constructed separately based on equivalent flow models in a representative area or volume by taking the advantage of the orthogonality of the mesh partition. Numerical examples of fluid flow in 2-D and 3-D domain including porous media and fractured porous media are presented to demonstrate the accuracy, robustness, and effectiveness of the proposed unified pipe network method. Results show that the developed method has good performance even with highly distorted mesh. Water recharge into the fractured rock mass with complex fracture network is studied. It has been found in this case that the effect of aperture change on the water recharge rate is more significant in the early stage compared to the fracture density change.

Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems.

PubMed

Kim, Jason Z; Soffer, Jonathan M; Kahn, Ari E; Vettel, Jean M; Pasqualetti, Fabio; Bassett, Danielle S

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

Role of graph architecture in controlling dynamical networks with applications to neural systems

NASA Astrophysics Data System (ADS)

Kim, Jason Z.; Soffer, Jonathan M.; Kahn, Ari E.; Vettel, Jean M.; Pasqualetti, Fabio; Bassett, Danielle S.

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

The medical science DMZ: a network design pattern for data-intensive medical science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peisert, Sean; Dart, Eli; Barnett, William

We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.High-end networking, packet-filter firewalls, network intrusion-detection systems.We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs.The exponentially increasing amounts of "omics" data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research "Big Data." The storage, analysis, and networkmore » resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows.By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.« less

The Medical Science DMZ.

PubMed

Peisert, Sean; Barnett, William; Dart, Eli; Cuff, James; Grossman, Robert L; Balas, Edward; Berman, Ari; Shankar, Anurag; Tierney, Brian

2016-11-01

We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. High-end networking, packet filter firewalls, network intrusion detection systems. We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. The exponentially increasing amounts of "omics" data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research "big data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a "Science DMZ"-a framework that is used in physical sciences and engineering research to manage high-capacity data flows. By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

The Medical Science DMZ

PubMed Central

Barnett, William; Dart, Eli; Cuff, James; Grossman, Robert L; Balas, Edward; Berman, Ari; Shankar, Anurag; Tierney, Brian

2016-01-01

Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements. PMID:27136944

Aberrant within- and between-network connectivity of the mirror neuron system network and the mentalizing network in first episode psychosis.

PubMed

Choe, Eugenie; Lee, Tae Young; Kim, Minah; Hur, Ji-Won; Yoon, Youngwoo Bryan; Cho, Kang-Ik K; Kwon, Jun Soo

2018-03-26

It has been suggested that the mentalizing network and the mirror neuron system network support important social cognitive processes that are impaired in schizophrenia. However, the integrity and interaction of these two networks have not been sufficiently studied, and their effects on social cognition in schizophrenia remain unclear. Our study included 26 first-episode psychosis (FEP) patients and 26 healthy controls. We utilized resting-state functional connectivity to examine the a priori-defined mirror neuron system network and the mentalizing network and to assess the within- and between-network connectivities of the networks in FEP patients. We also assessed the correlation between resting-state functional connectivity measures and theory of mind performance. FEP patients showed altered within-network connectivity of the mirror neuron system network, and aberrant between-network connectivity between the mirror neuron system network and the mentalizing network. The within-network connectivity of the mirror neuron system network was noticeably correlated with theory of mind task performance in FEP patients. The integrity and interaction of the mirror neuron system network and the mentalizing network may be altered during the early stages of psychosis. Additionally, this study suggests that alterations in the integrity of the mirror neuron system network are highly related to deficient theory of mind in schizophrenia, and this problem would be present from the early stage of psychosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Meso-decorated self-healing gels: network structure and properties

NASA Astrophysics Data System (ADS)

Gong, Jin; Sawamura, Kensuke; Igarashi, Susumu; Furukawa, Hidemitsu

2013-04-01

Gels are a new material having three-dimensional network structures of macromolecules. They possess excellent properties as swellability, high permeability and biocompatibility, and have been applied in various fields of daily life, food, medicine, architecture, and chemistry. In this study, we tried to prepare new multi-functional and high-strength gels by using Meso-Decoration (Meso-Deco), one new method of structure design at intermediate mesoscale. High-performance rigid-rod aromatic polymorphic crystals, and the functional groups of thermoreversible Diels-Alder reaction were introduced into soft gels as crosslinkable pendent chains. The functionalization and strengthening of gels can be realized by meso-decorating the gels' structure using high-performance polymorphic crystals and thermoreversible pendent chains. New gels with good mechanical properties, novel optical properties and thermal properties are expected to be developed.

Bio-inspired patterned networks (BIPS) for development of wearable/disposable biosensors

NASA Astrophysics Data System (ADS)

McLamore, E. S.; Convertino, M.; Hondred, John; Das, Suprem; Claussen, J. C.; Vanegas, D. C.; Gomes, C.

2016-05-01

Here we demonstrate a novel approach for fabricating point of care (POC) wearable electrochemical biosensors based on 3D patterning of bionanocomposite networks. To create Bio-Inspired Patterned network (BIPS) electrodes, we first generate fractal network in silico models that optimize transport of network fluxes according to an energy function. Network patterns are then inkjet printed onto flexible substrate using conductive graphene ink. We then deposit fractal nanometal structures onto the graphene to create a 3D nanocomposite network. Finally, we biofunctionalize the surface with biorecognition agents using covalent bonding. In this paper, BIPS are used to develop high efficiency, low cost biosensors for measuring glucose as a proof of concept. Our results on the fundamental performance of BIPS sensors show that the biomimetic nanostructures significantly enhance biosensor sensitivity, accuracy, response time, limit of detection, and hysteresis compared to conventional POC non fractal electrodes (serpentine, interdigitated, and screen printed electrodes). BIPs, in particular Apollonian patterned BIPS, represent a new generation of POC biosensors based on nanoscale and microscale fractal networks that significantly improve electrical connectivity, leading to enhanced sensor performance.

Aligning Solution-Derived Carbon Nanotube Film with Full Surface Coverage for High-Performance Electronics Applications.

PubMed

Zhu, Ma-Guang; Si, Jia; Zhang, Zhiyong; Peng, Lian-Mao

2018-06-01

The main challenge for application of solution-derived carbon nanotubes (CNTs) in high performance field-effect transistor (FET) is how to align CNTs into an array with high density and full surface coverage. A directional shrinking transfer method is developed to realize high density aligned array based on randomly orientated CNT network film. Through transferring a solution-derived CNT network film onto a stretched retractable film followed by a shrinking process, alignment degree and density of CNT film increase with the shrinking multiple. The quadruply shrunk CNT films present well alignment, which is identified by the polarized Raman spectroscopy and electrical transport measurements. Based on the high quality and high density aligned CNT array, the fabricated FETs with channel length of 300 nm present ultrahigh performance including on-state current I on of 290 µA µm -1 (V ds = -1.5 V and V gs = -2 V) and peak transconductance g m of 150 µS µm -1 , which are, respectively, among the highest corresponding values in the reported CNT array FETs. High quality and high semiconducting purity CNT arrays with high density and full coverage obtained through this method promote the development of high performance CNT-based electronics. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learning and robustness to catch-and-release fishing in a shark social network

PubMed Central

Brown, Culum; Planes, Serge

2017-01-01

Individuals can play different roles in maintaining connectivity and social cohesion in animal populations and thereby influence population robustness to perturbations. We performed a social network analysis in a reef shark population to assess the vulnerability of the global network to node removal under different scenarios. We found that the network was generally robust to the removal of nodes with high centrality. The network appeared also highly robust to experimental fishing. Individual shark catchability decreased as a function of experience, as revealed by comparing capture frequency and site presence. Altogether, these features suggest that individuals learnt to avoid capture, which ultimately increased network robustness to experimental catch-and-release. Our results also suggest that some caution must be taken when using capture–recapture models often used to assess population size as assumptions (such as equal probabilities of capture and recapture) may be violated by individual learning to escape recapture. PMID:28298593

Information Systems at Enterprise. Design of Secure Network of Enterprise

NASA Astrophysics Data System (ADS)

Saigushev, N. Y.; Mikhailova, U. V.; Vedeneeva, O. A.; Tsaran, A. A.

2018-05-01

No enterprise and company can do without designing its own corporate network in today's information society. It accelerates and facilitates the work of employees at any level, but contains a big threat to confidential information of the company. In addition to the data theft attackers, there are plenty of information threats posed by modern malware effects. In this regard, the computational security of corporate networks is an important component of modern information technologies of computer security for any enterprise. This article says about the design of the protected corporate network of the enterprise that provides the computers on the network access to the Internet, as well interoperability with the branch. The access speed to the Internet at a high level is provided through the use of high-speed access channels and load balancing between devices. The security of the designed network is performed through the use of VLAN technology as well as access lists and AAA server.

Formation of homophily in academic performance: Students change their friends rather than performance

PubMed Central

Smirnov, Ivan

2017-01-01

Homophily, the tendency of individuals to associate with others who share similar traits, has been identified as a major driving force in the formation and evolution of social ties. In many cases, it is not clear if homophily is the result of a socialization process, where individuals change their traits according to the dominance of that trait in their local social networks, or if it results from a selection process, in which individuals reshape their social networks so that their traits match those in the new environment. Here we demonstrate the detailed temporal formation of strong homophily in academic achievements of high school and university students. We analyze a unique dataset that contains information about the detailed time evolution of a friendship network of 6,000 students across 42 months. Combining the evolving social network data with the time series of the academic performance (GPA) of individual students, we show that academic homophily is a result of selection: students prefer to gradually reorganize their social networks according to their performance levels, rather than adapting their performance to the level of their local group. We find no signs for a pull effect, where a social environment of good performers motivates bad students to improve their performance. We are able to understand the underlying dynamics of grades and networks with a simple model. The lack of a social pull effect in classical educational settings could have important implications for the understanding of the observed persistence of segregation, inequality and social immobility in societies. PMID:28854202

Light scattering optimization of chitin random network in ultrawhite beetle scales

NASA Astrophysics Data System (ADS)

Utel, Francesco; Cortese, Lorenzo; Pattelli, Lorenzo; Burresi, Matteo; Vignolini, Silvia; Wiersma, Diederik

2017-09-01

Among the natural white colored photonics structures, a bio-system has become of great interest in the field of disordered optical media: the scale of the white beetle Chyphochilus. Despite its low thickness, on average 7 μm, and low refractive index, this beetle exhibits extreme high brightness and unique whiteness. These properties arise from the interaction of light with a complex network of chitin nano filaments embedded in the interior of the scales. As it's been recently claimed, this could be a consequence of the peculiar morphology of the filaments network that, by means of high filling fraction (0.61) and structural anisotropy, optimizes the multiple scattering of light. We therefore performed a numerical analysis on the structural properties of the chitin network in order to understand their role in the enhancement of the scale scattering intensity. Modeling the filaments as interconnected rod shaped scattering centers, we numerically generated the spatial coordinates of the network components. Controlling the quantities that are claimed to play a fundamental role in the brightness and whiteness properties of the investigated system (filling fraction and average rods orientation, i.e. the anisotropy of the ensemble of scattering centers), we obtained a set of customized random networks. FDTD simulations of light transport have been performed on these systems, observing high reflectance for all the visible frequencies and proving the implemented algorithm to numerically generate the structures is suitable to investigate the dependence of reflectance by anisotropy.

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

High Performance All-solid Supercapacitors Based on the Network of Ultralong Manganese dioxide/Polyaniline Coaxial Nanowires

NASA Astrophysics Data System (ADS)

Zhou, Junli; Yu, Lin; Liu, Wei; Zhang, Xiaodan; Mu, Wei; Du, Xu; Zhang, Zhe; Deng, Yulin

2015-12-01

In recent years, thin, lightweight and flexible solid supercapacitors are of considerable interest as energy storage devices. Here we demonstrated all-solid supercapacitors (SSCs) with high electrochemical properties, low self-discharge characteristics based on manganese dioxide/polyaniline (MNW/PANI) coaxial nanowire networks. The synergistic effect of MnO2/PANI plus the unique coaxial nanostructure of the ultralong nanowires with a highly interconnected network effectively enhance the conductivity and capacitive performance of the SSCs device. The MNW/PANI composite with 62.5% MnO2 exhibits an outstanding areal specific capacitance reaching 346 mF/cm2 at 5 mV s-1 which is significant higher than most previously reported solid supercapacitors (15.3 mF/cm2-109 mF/cm2) and is close to the that of the best graphene films solid state supercapacitors (372 mF/cm2). In contrast, only 190 mF/cm2 of areal specific capacitance was obtained for the pure MnO2 NW network. The supercapacitors also exhibited low leakage current as small as 20.1 μA, which demonstrated that the MNW/PANI SSCs have great potential for practical applications.

High Performance All-solid Supercapacitors Based on the Network of Ultralong Manganese dioxide/Polyaniline Coaxial Nanowires.

PubMed

Zhou, Junli; Yu, Lin; Liu, Wei; Zhang, Xiaodan; Mu, Wei; Du, Xu; Zhang, Zhe; Deng, Yulin

2015-12-08

In recent years, thin, lightweight and flexible solid supercapacitors are of considerable interest as energy storage devices. Here we demonstrated all-solid supercapacitors (SSCs) with high electrochemical properties, low self-discharge characteristics based on manganese dioxide/polyaniline (MNW/PANI) coaxial nanowire networks. The synergistic effect of MnO2/PANI plus the unique coaxial nanostructure of the ultralong nanowires with a highly interconnected network effectively enhance the conductivity and capacitive performance of the SSCs device. The MNW/PANI composite with 62.5% MnO2 exhibits an outstanding areal specific capacitance reaching 346 mF/cm(2) at 5 mV s(-1) which is significant higher than most previously reported solid supercapacitors (15.3 mF/cm(2)-109 mF/cm(2)) and is close to the that of the best graphene films solid state supercapacitors (372 mF/cm(2)). In contrast, only 190 mF/cm(2) of areal specific capacitance was obtained for the pure MnO2 NW network. The supercapacitors also exhibited low leakage current as small as 20.1 μA, which demonstrated that the MNW/PANI SSCs have great potential for practical applications.

Wet Chemistry Synthesis of Multidimensional Nanocarbon-Sulfur Hybrid Materials with Ultrahigh Sulfur Loading for Lithium-Sulfur Batteries.

PubMed

Du, Wen-Cheng; Yin, Ya-Xia; Zeng, Xian-Xiang; Shi, Ji-Lei; Zhang, Shuai-Feng; Wan, Li-Jun; Guo, Yu-Guo

2016-02-17

An optimized nanocarbon-sulfur cathode material with ultrahigh sulfur loading of up to 90 wt % is realized in the form of sulfur nanolayer-coated three-dimensional (3D) conducting network. This 3D nanocarbon-sulfur network combines three different nanocarbons, as follows: zero-dimensional carbon nanoparticle, one-dimensional carbon nanotube, and two-dimensional graphene. This 3D nanocarbon-sulfur network is synthesized by using a method based on soluble chemistry of elemental sulfur and three types of nanocarbons in well-chosen solvents. The resultant sulfur-carbon material shows a high specific capacity of 1115 mA h g(-1) at 0.02C and good rate performance of 551 mA h g(-1) at 1C based on the mass of sulfur-carbon composite. Good battery performance can be attributed to the homogeneous compositing of sulfur with the 3D hierarchical hybrid nanocarbon networks at nanometer scale, which provides efficient multidimensional transport pathways for electrons and ions. Wet chemical method developed here provides an easy and cost-effective way to prepare sulfur-carbon cathode materials with high sulfur loading for application in high-energy Li-S batteries.

Low-power, transparent optical network interface for high bandwidth off-chip interconnects.

PubMed

Liboiron-Ladouceur, Odile; Wang, Howard; Garg, Ajay S; Bergman, Keren

2009-04-13

The recent emergence of multicore architectures and chip multiprocessors (CMPs) has accelerated the bandwidth requirements in high-performance processors for both on-chip and off-chip interconnects. For next generation computing clusters, the delivery of scalable power efficient off-chip communications to each compute node has emerged as a key bottleneck to realizing the full computational performance of these systems. The power dissipation is dominated by the off-chip interface and the necessity to drive high-speed signals over long distances. We present a scalable photonic network interface approach that fully exploits the bandwidth capacity offered by optical interconnects while offering significant power savings over traditional E/O and O/E approaches. The power-efficient interface optically aggregates electronic serial data streams into a multiple WDM channel packet structure at time-of-flight latencies. We demonstrate a scalable optical network interface with 70% improvement in power efficiency for a complete end-to-end PCI Express data transfer.

Optimal Signal Processing in Small Stochastic Biochemical Networks

PubMed Central

Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

2007-01-01

We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

PubMed

Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

2017-08-26

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

PubMed Central

Carro, Belen; Sanchez-Esguevillas, Antonio

2017-01-01

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

Improving the Unsteady Aerodynamic Performance of Transonic Turbines using Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan; Madavan, Nateri K.; Huber, Frank W.

1999-01-01

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The procedure yielded a modified design that improves the aerodynamic performance through small changes to the reference design geometry. These results demonstrate the capabilities of the neural net-based design procedure, and also show the advantages of including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Latency of TCP applications over the ATM-WAN using the GFR service category

NASA Astrophysics Data System (ADS)

Chen, Kuo-Hsien; Siliquini, John F.; Budrikis, Zigmantas

1998-10-01

The GFR service category has been proposed for data services in ATM networks. Since users are ultimately interested in data service that provide high efficiency and low latency, it is important to study the latency performance for data traffic of the GFR service category in an ATM network. Today much of the data traffic utilizes the TCP/IP protocol suite and in this paper we study through simulation the latency of TCP applications running over a wide-area ATM network utilizing the GFR service category using a realistic TCP traffic model. From this study, we find that during congestion periods the reserved bandwidth in GFR can improve the latency performance for TCP applications. However, due to TCP 'Slow Start' data segment generation dynamics, we show that a large proportion of TCP segments are discarded under network congestion even when the reserved bandwidth is equal to the average generated rate of user data. Therefore, a user experiences worse than expected latency performance when the network is congested. In this study we also examine the effects of segment size on the latency performance of TCP applications using the GFR service category.

Image processing system design for microcantilever-based optical readout infrared arrays

NASA Astrophysics Data System (ADS)

Tong, Qiang; Dong, Liquan; Zhao, Yuejin; Gong, Cheng; Liu, Xiaohua; Yu, Xiaomei; Yang, Lei; Liu, Weiyu

2012-12-01

Compared with the traditional infrared imaging technology, the new type of optical-readout uncooled infrared imaging technology based on MEMS has many advantages, such as low cost, small size, producing simple. In addition, the theory proves that the technology's high thermal detection sensitivity. So it has a very broad application prospects in the field of high performance infrared detection. The paper mainly focuses on an image capturing and processing system in the new type of optical-readout uncooled infrared imaging technology based on MEMS. The image capturing and processing system consists of software and hardware. We build our image processing core hardware platform based on TI's high performance DSP chip which is the TMS320DM642, and then design our image capturing board based on the MT9P031. MT9P031 is Micron's company high frame rate, low power consumption CMOS chip. Last we use Intel's company network transceiver devices-LXT971A to design the network output board. The software system is built on the real-time operating system DSP/BIOS. We design our video capture driver program based on TI's class-mini driver and network output program based on the NDK kit for image capturing and processing and transmitting. The experiment shows that the system has the advantages of high capturing resolution and fast processing speed. The speed of the network transmission is up to 100Mbps.

Cross-linked carbon networks constructed from N-doped nanosheets with enhanced performance for supercapacitors

NASA Astrophysics Data System (ADS)

Liu, Qingqing; Zhong, Jialiang; Sun, Zhipeng; Mi, Hongyu

2017-02-01

Hierarchically porous carbons offer great benefits for constructing advanced electrodes for energy-related applications. Herein, we reported facile synthesis of cross-linked carbon networks (HPCNs) made from N-doped nanosheets. By using MgO as self-sacrificial templates, the polyethylene glycol and melamine precursors were first uniformly coated on the template, and then annealed at the elevated temperature in inert atmosphere before removing the templates by mild acid etching. Interestingly, the capacitance performance of HPCNs could be easily modulated by adjusting the mass ratio of the precursors and templates, as well as the carbonization temperature. The optimized HPCNs showed specific capacitances of 192.6 F g-1 at 1.0 A g-1 and 156.2 F g-1 even at 20 A g-1 in 6.0 M KOH solution, and long-term cyclability with 85.5% capacitance retention at high current load of 10 A g-1 after 8000 successive cycles, which were attributed to structural merits of these continuous networks including high surface area of 370.8 m2 g-1, high pore volume of 1.65 cm3 g-1, as well as high nitrogen content of 9.920 wt.%. Owing to simplicity of the synthesis method and superior performance, such HPCNs may promise great potential in energy storage fields.

Cross-layer model design in wireless ad hoc networks for the Internet of Things.

PubMed

Yang, Xin; Wang, Ling; Xie, Jian; Zhang, Zhaolin

2018-01-01

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods.

Cross-layer model design in wireless ad hoc networks for the Internet of Things

PubMed Central

Wang, Ling; Xie, Jian; Zhang, Zhaolin

2018-01-01

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods. PMID:29734355

High solar activity predictions through an artificial neural network

NASA Astrophysics Data System (ADS)

Orozco-Del-Castillo, M. G.; Ortiz-Alemán, J. C.; Couder-Castañeda, C.; Hernández-Gómez, J. J.; Solís-Santomé, A.

The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24-26, which we found to occur before 2040.

The Telecommunications Review

DOT National Transportation Integrated Search

1991-01-01

TELECOMMUNICATION NETWORKS ARE BECOMING INCREASINGLY COMPLEX. THEY MUST MEET THE NEEDS OF SOPHISTICATED USERS WHO DEMAND A HIGH-LEVEL OF FUNCTIONALITY, HIGH PERFORMANCE, AND LOW COST. : TELECOMMUNICATIONS TECHNOLOGY IS CHANGING RAPIDLY TO MEET THA...

On the Inference of Functional Circadian Networks Using Granger Causality

PubMed Central

Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

2015-01-01

Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

Integrated Social and Quality of Service Trust Management of Mobile Groups in Ad Hoc Networks

DTIC Science & Technology

2013-01-01

high resiliency to malicious attacks and misbehaving nodes. Keywords—trust management; mobile ad hoc networks; QoS trust; social trust; trust...paper we address an importance issue of trust management protocol design for MANETs: trust bias minimization despite misbehaving nodes performing

Arra: Tas::89 0227::Tas Recovery Act 100g Ftp: An Ultra-High Speed Data Transfer Service Over Next Generation 100 Gigabit Per Second Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

YU, DANTONG; Jin, Shudong

2014-03-01

Data-intensive applications, including high energy and nuclear physics, astrophysics, climate modeling, nano-scale materials science, genomics, and financing, are expected to generate exabytes of data over the coming years, which must be transferred, visualized, and analyzed by geographically distributed teams of users. High-performance network capabilities must be available to these users at the application level in a transparent, virtualized manner. Moreover, the application users must have the capability to move large datasets from local and remote locations across network environments to their home institutions. To solve these challenges, the main goal of our project is to design and evaluate high-performance datamore » transfer software to support various data-intensive applications. First, we have designed a middleware software that provides access to Remote Direct Memory Access (RDMA) functionalities. This middleware integrates network access, memory management and multitasking in its core design. We address a number of issues related to its efficient implementation, for instance, explicit buffer management and memory registration, and parallelization of RDMA operations, which are vital to delivering the benefit of RDMA to the applications. Built on top of this middleware, an implementation and experimental evaluation of the RDMA-based FTP software, RFTP, is described and evaluated. This application has been implemented by our team to exploit the full capabilities of advanced RDMA mechanisms for ultra-high speed bulk data transfer applications on Energy Sciences Network (ESnet). Second, we designed our data transfer software to optimize TCP/IP based data transfer performance such that RFTP can be fully compatible with today’s Internet. Our kernel optimization techniques with Linux system calls sendfile and splice, can reduce data copy cost. In this report, we summarize the technical challenges of our project, the primary software design methods, the major project milestones achieved, as well as the testbed evaluation work and demonstrations during our project life time.« less

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase Qishi; Zhu, Michelle Mengxia

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models featuremore » diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.« less

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

NASA Technical Reports Server (NTRS)

Shameldin, Ramez Ahmed; Bowling, Shannon R.

2010-01-01

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Polymer nanofiber-carbon nanotube network generating circuits

NASA Astrophysics Data System (ADS)

Mutlu, Mustafa Umut; Akın, Osman; Yildiz, Ümit Hakan

2018-02-01

The polymer nanofiber carbon nanotube (CNT) based devices attracts attention since they promise high performance for next generation devices such as wearable electronics, ultra-light weighted appliances and foldable devices. This abstract describes the utilization of polymer nanofibers and CNT as major component of low cost foldable photo-resistor. We use polymer nanofiber as template guiding CNTs to generate nanocircuits and conductive sensing network. The controlled combination of CNTs and polymer nanofibers provide opportunities for device miniaturization without loss of performance. The nanofiber-CNT network based photo-resistor exhibits broad band response 400 to 1600 nm that holding promises for ultra-thin devices and new sensing platforms.

HRLSim: a high performance spiking neural network simulator for GPGPU clusters.

PubMed

Minkovich, Kirill; Thibeault, Corey M; O'Brien, Michael John; Nogin, Aleksey; Cho, Youngkwan; Srinivasa, Narayan

2014-02-01

Modeling of large-scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRL Spiking Simulator (HRLSim). This simulator is suitable for implementation on a cluster of general purpose graphical processing units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its performance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power, HRLSim offers an affordable and scalable tool for design, real-time simulation, and analysis of large-scale spiking neural networks.

Broadband and tunable high-performance microwave absorption of an ultralight and highly compressible graphene foam.

PubMed

Zhang, Yi; Huang, Yi; Zhang, Tengfei; Chang, Huicong; Xiao, Peishuang; Chen, Honghui; Huang, Zhiyu; Chen, Yongsheng

2015-03-25

The broadband and tunable high-performance microwave absorption properties of an ultralight and highly compressible graphene foam (GF) are investigated. Simply via physical compression, the microwave absorption performance can be tuned. The qualified bandwidth coverage of 93.8% (60.5 GHz/64.5 GHz) is achieved for the GF under 90% compressive strain (1.0 mm thickness). This mainly because of the 3D conductive network. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Status and Trends in Networking at LHC Tier1 Facilities

NASA Astrophysics Data System (ADS)

Bobyshev, A.; DeMar, P.; Grigaliunas, V.; Bigrow, J.; Hoeft, B.; Reymund, A.

2012-12-01

The LHC is entering its fourth year of production operation. Most Tier1 facilities have been in operation for almost a decade, when development and ramp-up efforts are included. LHC's distributed computing model is based on the availability of high capacity, high performance network facilities for both the WAN and LAN data movement, particularly within the Tier1 centers. As a result, the Tier1 centers tend to be on the leading edge of data center networking technology. In this paper, we analyze past and current developments in Tier1 LAN networking, as well as extrapolating where we anticipate networking technology is heading. Our analysis will include examination into the following areas: • Evolution of Tier1 centers to their current state • Evolving data center networking models and how they apply to Tier1 centers • Impact of emerging network technologies (e.g. 10GE-connected hosts, 40GE/100GE links, IPv6) on Tier1 centers • Trends in WAN data movement and emergence of software-defined WAN network capabilities • Network virtualization

Nanowire mesh solar fuels generator

DOEpatents

Yang, Peidong; Chan, Candace; Sun, Jianwei; Liu, Bin

2016-05-24

This disclosure provides systems, methods, and apparatus related to a nanowire mesh solar fuels generator. In one aspect, a nanowire mesh solar fuels generator includes (1) a photoanode configured to perform water oxidation and (2) a photocathode configured to perform water reduction. The photocathode is in electrical contact with the photoanode. The photoanode may include a high surface area network of photoanode nanowires. The photocathode may include a high surface area network of photocathode nanowires. In some embodiments, the nanowire mesh solar fuels generator may include an ion conductive polymer infiltrating the photoanode and the photocathode in the region where the photocathode is in electrical contact with the photoanode.

Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.

PubMed

Xue, Hongqi; Wu, Shuang; Wu, Yichao; Ramirez Idarraga, Juan C; Wu, Hulin

2018-05-02

Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae. Copyright © 2018 John Wiley & Sons, Ltd.

Thermochemical Stability and Friction Properties of Soft Organosilica Networks for Solid Lubrication.

PubMed

Gonzalez Rodriguez, Pablo; Dral, A Petra; van den Nieuwenhuijzen, Karin J H; Lette, Walter; Schipper, Dik J; Ten Elshof, Johan E

2018-01-24

In view of their possible application as high temperature solid lubricants, the tribological and thermochemical properties of several organosilica networks were investigated over a range of temperatures between 25 and 580 °C. Organosilica networks, obtained from monomers with terminal and bridging organic groups, were synthesized by a sol-gel process. The influence of carbon content, crosslink density, rotational freedom of incorporated hydrocarbon groups, and network connectivity on the high temperature friction properties of the polymer was studied for condensed materials from silicon alkoxide precursors with terminating organic groups, i.e., methyltrimethoxysilane, propyltrimethoxysilane, diisopropyldimethoxysilane, cyclohexyltrimethoxysilane, phenyltrimethoxysilane and 4-biphenylyltriethoxysilane networks, as well as precursors with organic bridging groups between Si centers, i.e., 1,4-bis(triethoxysilyl)benzene and 4,4'-bis(triethoxysilyl)-1,1'-biphenyl. Pin-on-disc measurements were performed using all selected solid lubricants. It was found that materials obtained from phenyltrimethoxysilane and cyclohexyltrimethoxysilane precursors showed softening above 120 °C and performed best in terms of friction reduction, reaching friction coefficients as low as 0.01. This value is lower than that of graphite films (0.050 ± 0.005), a common bench mark for solid lubricants.

Performance evaluation of data center service localization based on virtual resource migration in software defined elastic optical network.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; Tan, Yuanlong; Lin, Yi; Han, Jianrui; Lee, Young

2015-09-07

Data center interconnection with elastic optical network is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate data center services. In view of this, this study extends the data center resources to user side to enhance the end-to-end quality of service. We propose a novel data center service localization (DCSL) architecture based on virtual resource migration in software defined elastic data center optical network. A migration evaluation scheme (MES) is introduced for DCSL based on the proposed architecture. The DCSL can enhance the responsiveness to the dynamic end-to-end data center demands, and effectively reduce the blocking probability to globally optimize optical network and application resources. The overall feasibility and efficiency of the proposed architecture are experimentally verified on the control plane of our OpenFlow-based enhanced SDN testbed. The performance of MES scheme under heavy traffic load scenario is also quantitatively evaluated based on DCSL architecture in terms of path blocking probability, provisioning latency and resource utilization, compared with other provisioning scheme.

Thermochemical Stability and Friction Properties of Soft Organosilica Networks for Solid Lubrication

PubMed Central

Gonzalez Rodriguez, Pablo; van den Nieuwenhuijzen, Karin J. H.; Lette, Walter; Schipper, Dik J.

2018-01-01

In view of their possible application as high temperature solid lubricants, the tribological and thermochemical properties of several organosilica networks were investigated over a range of temperatures between 25 and 580 °C. Organosilica networks, obtained from monomers with terminal and bridging organic groups, were synthesized by a sol-gel process. The influence of carbon content, crosslink density, rotational freedom of incorporated hydrocarbon groups, and network connectivity on the high temperature friction properties of the polymer was studied for condensed materials from silicon alkoxide precursors with terminating organic groups, i.e., methyltrimethoxysilane, propyltrimethoxysilane, diisopropyldimethoxysilane, cyclohexyltrimethoxysilane, phenyltrimethoxysilane and 4-biphenylyltriethoxysilane networks, as well as precursors with organic bridging groups between Si centers, i.e., 1,4-bis(triethoxysilyl)benzene and 4,4′-bis(triethoxysilyl)-1,1′-biphenyl. Pin-on-disc measurements were performed using all selected solid lubricants. It was found that materials obtained from phenyltrimethoxysilane and cyclohexyltrimethoxysilane precursors showed softening above 120 °C and performed best in terms of friction reduction, reaching friction coefficients as low as 0.01. This value is lower than that of graphite films (0.050 ± 0.005), a common bench mark for solid lubricants. PMID:29364164

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

PubMed

Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

2014-01-01

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection -A First Study

PubMed Central

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Orthaus, Sandra; Achilefu, Samuel; Nussinov, Zohar

2014-01-01

Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution. PMID:24251410

CATO: a CAD tool for intelligent design of optical networks and interconnects

NASA Astrophysics Data System (ADS)

Chlamtac, Imrich; Ciesielski, Maciej; Fumagalli, Andrea F.; Ruszczyk, Chester; Wedzinga, Gosse

1997-10-01

Increasing communication speed requirements have created a great interest in very high speed optical and all-optical networks and interconnects. The design of these optical systems is a highly complex task, requiring the simultaneous optimization of various parts of the system, ranging from optical components' characteristics to access protocol techniques. Currently there are no computer aided design (CAD) tools on the market to support the interrelated design of all parts of optical communication systems, thus the designer has to rely on costly and time consuming testbed evaluations. The objective of the CATO (CAD tool for optical networks and interconnects) project is to develop a prototype of an intelligent CAD tool for the specification, design, simulation and optimization of optical communication networks. CATO allows the user to build an abstract, possible incomplete, model of the system, and determine its expected performance. Based on design constraints provided by the user, CATO will automatically complete an optimum design, using mathematical programming techniques, intelligent search methods and artificial intelligence (AI). Initial design and testing of a CATO prototype (CATO-1) has been completed recently. The objective was to prove the feasibility of combining AI techniques, simulation techniques, an optical device library and a graphical user interface into a flexible CAD tool for obtaining optimal communication network designs in terms of system cost and performance. CATO-1 is an experimental tool for designing packet-switching wavelength division multiplexing all-optical communication systems using a LAN/MAN ring topology as the underlying network. The two specific AI algorithms incorporated are simulated annealing and a genetic algorithm. CATO-1 finds the optimal number of transceivers for each network node, using an objective function that includes the cost of the devices and the overall system performance.

High Energy Physics and Nuclear Physics Network Requirements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dart, Eli; Bauerdick, Lothar; Bell, Greg

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements needed by instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In August 2013, ESnet and the DOE SC Offices of High Energy Physics (HEP) and Nuclear Physicsmore » (NP) organized a review to characterize the networking requirements of the programs funded by the HEP and NP program offices. Several key findings resulted from the review. Among them: 1. The Large Hadron Collider?s ATLAS (A Toroidal LHC Apparatus) and CMS (Compact Muon Solenoid) experiments are adopting remote input/output (I/O) as a core component of their data analysis infrastructure. This will significantly increase their demands on the network from both a reliability perspective and a performance perspective. 2. The Large Hadron Collider (LHC) experiments (particularly ATLAS and CMS) are working to integrate network awareness into the workflow systems that manage the large number of daily analysis jobs (1 million analysis jobs per day for ATLAS), which are an integral part of the experiments. Collaboration with networking organizations such as ESnet, and the consumption of performance data (e.g., from perfSONAR [PERformance Service Oriented Network monitoring Architecture]) are critical to the success of these efforts. 3. The international aspects of HEP and NP collaborations continue to expand. This includes the LHC experiments, the Relativistic Heavy Ion Collider (RHIC) experiments, the Belle II Collaboration, the Large Synoptic Survey Telescope (LSST), and others. The international nature of these collaborations makes them heavily reliant on transoceanic connectivity, which is subject to longer term service disruptions than terrestrial connectivity. The network engineering aspects of undersea connectivity will continue to be a significant part of the planning, deployment, and operation of the data analysis infrastructure for HEP and NP experiments for the foreseeable future. Given their critical dependency on networking services, the experiments have expressed the need for tight integration (both technically and operationally) of the domestic and the transoceanic parts of the network infrastructure that supports the experiments. 4. The datasets associated with simulations continue to increase in size, and the need to move these datasets between analysis centers is placing ever-increasing demands on networks and on data management systems at the supercomputing centers. In addition, there is a need to harmonize cybersecurity practice with the data transfer performance requirements of the science. This report expands on these points, and addresses others as well. The report contains a findings section in addition to the text of the case studies discussed during the review.« less

A scalable silicon photonic chip-scale optical switch for high performance computing systems.

PubMed

Yu, Runxiang; Cheung, Stanley; Li, Yuliang; Okamoto, Katsunari; Proietti, Roberto; Yin, Yawei; Yoo, S J B

2013-12-30

This paper discusses the architecture and provides performance studies of a silicon photonic chip-scale optical switch for scalable interconnect network in high performance computing systems. The proposed switch exploits optical wavelength parallelism and wavelength routing characteristics of an Arrayed Waveguide Grating Router (AWGR) to allow contention resolution in the wavelength domain. Simulation results from a cycle-accurate network simulator indicate that, even with only two transmitter/receiver pairs per node, the switch exhibits lower end-to-end latency and higher throughput at high (>90%) input loads compared with electronic switches. On the device integration level, we propose to integrate all the components (ring modulators, photodetectors and AWGR) on a CMOS-compatible silicon photonic platform to ensure a compact, energy efficient and cost-effective device. We successfully demonstrate proof-of-concept routing functions on an 8 × 8 prototype fabricated using foundry services provided by OpSIS-IME.

Discovering functional interdependence relationship in PPI networks for protein complex identification.

PubMed

Lam, Winnie W M; Chan, Keith C C

2012-04-01

Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation, PCIFI was used to identify protein complexes in real PPI network data and the protein complexes it found were matched against those that were previously known in MIPS. The results show that PCIFI can be an effective technique for the identification of protein complexes. The protein complexes it found can match more known protein complexes with a smaller false-alarm rate and can provide useful insights into the understanding of the functional interdependence relationships between proteins in protein complexes.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

PubMed

Gratton, Caterina; Laumann, Timothy O; Nielsen, Ashley N; Greene, Deanna J; Gordon, Evan M; Gilmore, Adrian W; Nelson, Steven M; Coalson, Rebecca S; Snyder, Abraham Z; Schlaggar, Bradley L; Dosenbach, Nico U F; Petersen, Steven E

2018-04-18

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. Copyright © 2018 Elsevier Inc. All rights reserved.

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

PubMed

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

Video transmission on ATM networks. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chen, Yun-Chung

1993-01-01

The broadband integrated services digital network (B-ISDN) is expected to provide high-speed and flexible multimedia applications. Multimedia includes data, graphics, image, voice, and video. Asynchronous transfer mode (ATM) is the adopted transport techniques for B-ISDN and has the potential for providing a more efficient and integrated environment for multimedia. It is believed that most broadband applications will make heavy use of visual information. The prospect of wide spread use of image and video communication has led to interest in coding algorithms for reducing bandwidth requirements and improving image quality. The major results of a study on the bridging of network transmission performance and video coding are: Using two representative video sequences, several video source models are developed. The fitness of these models are validated through the use of statistical tests and network queuing performance. A dual leaky bucket algorithm is proposed as an effective network policing function. The concept of the dual leaky bucket algorithm can be applied to a prioritized coding approach to achieve transmission efficiency. A mapping of the performance/control parameters at the network level into equivalent parameters at the video coding level is developed. Based on that, a complete set of principles for the design of video codecs for network transmission is proposed.

Portable emergency telemedicine system over wireless broadband and 3G networks.

PubMed

Hong, SungHye; Kim, SangYong; Kim, JungChae; Lim, DongKyu; Jung, SeokMyung; Kim, DongKeun; Yoo, Sun K

2009-01-01

The telemedicine system aims at monitoring patients remotely without limit in time and space. However the existing telemedicine systems exchange medical information simply in a specified location. Due to increasing speed in processing data and expanding bandwidth of wireless networks, it is possible to perform telemedicine services on personal digital assistants (PDA). In this paper, a telemedicine system on PDA was developed using wideband mobile networks such as Wi-Fi, HSDPA, and WiBro for high speed bandwidths. This system enables to utilize and exchange variety and reliable patient information of video, biosignals, chatting messages, and triage data. By measuring bandwidths of individual data of the system over wireless networks, and evaluating the performance of this system using PDA, we demonstrated the feasibility of the designed portable emergency telemedicine system.

TinyOS-based quality of service management in wireless sensor networks

USGS Publications Warehouse

Peterson, N.; Anusuya-Rangappa, L.; Shirazi, B.A.; Huang, R.; Song, W.-Z.; Miceli, M.; McBride, D.; Hurson, A.; LaHusen, R.

2009-01-01

Previously the cost and extremely limited capabilities of sensors prohibited Quality of Service (QoS) implementations in wireless sensor networks. With advances in technology, sensors are becoming significantly less expensive and the increases in computational and storage capabilities are opening the door for new, sophisticated algorithms to be implemented. Newer sensor network applications require higher data rates with more stringent priority requirements. We introduce a dynamic scheduling algorithm to improve bandwidth for high priority data in sensor networks, called Tiny-DWFQ. Our Tiny-Dynamic Weighted Fair Queuing scheduling algorithm allows for dynamic QoS for prioritized communications by continually adjusting the treatment of communication packages according to their priorities and the current level of network congestion. For performance evaluation, we tested Tiny-DWFQ, Tiny-WFQ (traditional WFQ algorithm implemented in TinyOS), and FIFO queues on an Imote2-based wireless sensor network and report their throughput and packet loss. Our results show that Tiny-DWFQ performs better in all test cases. ?? 2009 IEEE.

Prediction of competitive diffusion on complex networks

NASA Astrophysics Data System (ADS)

Zhao, Jiuhua; Liu, Qipeng; Wang, Lin; Wang, Xiaofan

2018-10-01

In this paper, we study the prediction problem of diffusion process on complex networks in competitive circumstances. With this problem solved, the competitors could timely intervene the diffusion process if needed such that an expected outcome might be obtained. We consider a model with two groups of competitors spreading opposite opinions on a network. A prediction method based on the mutual influences among the agents is proposed, called Influence Matrix (IM for short), and simulations on real-world networks show that the proposed IM method has quite high accuracy on predicting both the preference of any normal agent and the final competition result. For comparison purpose, classic centrality measures are also used to predict the competition result. It is shown that PageRank, Degree, Katz Centrality, and the IM method are suitable for predicting the competition result. More precisely, in undirected networks, the IM method performs better than these centrality measures when the competing group contains more than one agent; in directed networks, the IM method performs only second to PageRank.

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson's disease prediction.

PubMed

Khan, Maryam Mahsal; Mendes, Alexandre; Chalup, Stephan K

2018-01-01

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson's disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results.

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

PubMed Central

Mendes, Alexandre; Chalup, Stephan K.

2018-01-01

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson’s disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results. PMID:29420578

Multipath Routing in Wireless Sensor Networks: Survey and Research Challenges

PubMed Central

Radi, Marjan; Dezfouli, Behnam; Bakar, Kamalrulnizam Abu; Lee, Malrey

2012-01-01

A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks. PMID:22368490

Multipath routing in wireless sensor networks: survey and research challenges.

PubMed

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Lee, Malrey

2012-01-01

A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks.

Totally opportunistic routing algorithm (TORA) for underwater wireless sensor network

PubMed Central

Hashim, Fazirulhisyam; Rasid, Mohd Fadlee A.; Othman, Mohamed

2018-01-01

Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26% of HH-VBF, 15% of VAPR, and 25% of H2DAB. Moreover, the average end-to-end delay has been reduced by 70% of VBF, 69% of HH-VBF, 46% of VAPR, and 73% of H2DAB. Furthermore, average hope-count has been improved by 57%, 53%, 16% and 31% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively. Also, propagation delay has been reduced by 34%, 30%, 15% and 23% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively. PMID:29874237

Totally opportunistic routing algorithm (TORA) for underwater wireless sensor network.

PubMed

Rahman, Ziaur; Hashim, Fazirulhisyam; Rasid, Mohd Fadlee A; Othman, Mohamed

2018-01-01

Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26% of HH-VBF, 15% of VAPR, and 25% of H2DAB. Moreover, the average end-to-end delay has been reduced by 70% of VBF, 69% of HH-VBF, 46% of VAPR, and 73% of H2DAB. Furthermore, average hope-count has been improved by 57%, 53%, 16% and 31% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively. Also, propagation delay has been reduced by 34%, 30%, 15% and 23% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively.

Issues in ATM Support of High-Performance, Geographically Distributed Computing

NASA Technical Reports Server (NTRS)

Claus, Russell W.; Dowd, Patrick W.; Srinidhi, Saragur M.; Blade, Eric D.G

1995-01-01

This report experimentally assesses the effect of the underlying network in a cluster-based computing environment. The assessment is quantified by application-level benchmarking, process-level communication, and network file input/output. Two testbeds were considered, one small cluster of Sun workstations and another large cluster composed of 32 high-end IBM RS/6000 platforms. The clusters had Ethernet, fiber distributed data interface (FDDI), Fibre Channel, and asynchronous transfer mode (ATM) network interface cards installed, providing the same processors and operating system for the entire suite of experiments. The primary goal of this report is to assess the suitability of an ATM-based, local-area network to support interprocess communication and remote file input/output systems for distributed computing.

The IRIS DMC: Perspectives on Real-Time Data Management and Open Access From a Large Seismological Archive: Challenges, Tools, and Quality Assurance

NASA Astrophysics Data System (ADS)

Benson, R. B.

2007-05-01

The IRIS Data Management Center, located in Seattle, WA, is the largest openly accessible geophysical archive in the world, and has a unique perspective on data management and operational practices that gets the most out of your network. Networks scale broad domains in time and space, from finite needs to monitor bridges and dams to national and international networks like the GSN and the FDSN that establish a baseline for global monitoring and research, the requirements that go into creating a well-tuned DMC archive treat these the same, building a collaborative network of networks that generations of users rely on and adds value to the data. Funded by the National Science Foundation through the Division of Earth Sciences, IRIS is operated through member universities and in cooperation with the USGS, and the DMS facility is a bridge between a globally distributed collaboration of seismic networks and an equally distributed network of users that demand a high standard for data quality, completeness, and ease of access. I will describe the role that a perpetual archive has in the life cycle of data, and how hosting real-time data performs a dual role of being a hub for continuous data from approximately 59 real-time networks, and distributing these (along with other data from the 40-year library of available time-series data) to researchers, while simultaneously providing shared data back to networks in real- time that benefits monitoring activities. I will describe aspects of our quality-assurance framework that are both passively and actively performed on 1100 seismic stations, generating over 6,000 channels of regularly sampled data arriving daily, that data providers can use as aids in operating their network, and users can likewise use when requesting suitable data for research purposes. The goal of the DMC is to eliminate bottlenecks in data discovery and shortening the steps leading to analysis. This includes many challenges, including keeping metadata current, tools for evaluating and viewing them, along with measuring and creating databases of other performance metrics and how monitoring them closer to real- time helps reduce operation costs, creates a richer repository, and eliminates problems over generations of duty cycles of data usage. I will describe a new resource, called the Nominal Response Library, which hopes to provide accurate and representative examples of sensor and data logger configurations that are hosted at the DMC and constitute a high-graded subset for crafting your own metadata. Finally, I want to encourage all network operators who do not currently submit SEED format data to an archive to consider these benefits, and briefly discuss how robust transfer mechanisms that include Earthworm, LISS, Antelope, NRTS and SeisComp, to name a few, can assist you in contributing your network data and help create this enabling virtual network of networks. In this era of high performance Internet capacity, the process that enables others to share your data and allows you to utilize external sources of data is nearly seamless with your current mission of network operation.

Acid-doped polymer nanofiber framework: Three-dimensional proton conductive network for high-performance fuel cells

NASA Astrophysics Data System (ADS)

Tanaka, Manabu; Takeda, Yasushi; Wakiya, Takeru; Wakamoto, Yuta; Harigaya, Kaori; Ito, Tatsunori; Tarao, Takashi; Kawakami, Hiroyoshi

2017-02-01

High-performance polymer electrolyte membranes (PEMs) with excellent proton conductivity, gas barrier property, and membrane stability are desired for future fuel cells. Here we report the development of PEMs based on our proposed new concept "Nanofiber Framework (NfF)." The NfF composite membranes composed of phytic acid-doped polybenzimidazole nanofibers (PBINf) and Nafion matrix show higher proton conductivity than the recast-Nafion membrane without nanofibers. A series of analyses reveal the formation of three-dimensional network nanostructures to conduct protons and water effectively through acid-condensed layers at the interface of PBINf and Nafion matrix. In addition, the NfF composite membrane achieves high gas barrier property and distinguished membrane stability. The fuel cell performance by the NfF composite membrane, which enables ultra-thin membranes with their thickness less than 5 μm, is superior to that by the recast-Nafion membrane, especially at low relative humidity. Such NfF-based high-performance PEM will be accomplished not only by the Nafion matrix used in this study but also by other polymer electrolyte matrices for future PEFCs.

A Novel Dual Separate Paths (DSP) Algorithm Providing Fault-Tolerant Communication for Wireless Sensor Networks.

PubMed

Tien, Nguyen Xuan; Kim, Semog; Rhee, Jong Myung; Park, Sang Yoon

2017-07-25

Fault tolerance has long been a major concern for sensor communications in fault-tolerant cyber physical systems (CPSs). Network failure problems often occur in wireless sensor networks (WSNs) due to various factors such as the insufficient power of sensor nodes, the dislocation of sensor nodes, the unstable state of wireless links, and unpredictable environmental interference. Fault tolerance is thus one of the key requirements for data communications in WSN applications. This paper proposes a novel path redundancy-based algorithm, called dual separate paths (DSP), that provides fault-tolerant communication with the improvement of the network traffic performance for WSN applications, such as fault-tolerant CPSs. The proposed DSP algorithm establishes two separate paths between a source and a destination in a network based on the network topology information. These paths are node-disjoint paths and have optimal path distances. Unicast frames are delivered from the source to the destination in the network through the dual paths, providing fault-tolerant communication and reducing redundant unicast traffic for the network. The DSP algorithm can be applied to wired and wireless networks, such as WSNs, to provide seamless fault-tolerant communication for mission-critical and life-critical applications such as fault-tolerant CPSs. The analyzed and simulated results show that the DSP-based approach not only provides fault-tolerant communication, but also improves network traffic performance. For the case study in this paper, when the DSP algorithm was applied to high-availability seamless redundancy (HSR) networks, the proposed DSP-based approach reduced the network traffic by 80% to 88% compared with the standard HSR protocol, thus improving network traffic performance.

Influence of network topology on cooperative problem-solving systems.

PubMed

Fontanari, José F; Rodrigues, Francisco A

2016-09-01

The idea of a collective intelligence behind the complex natural structures built by organisms suggests that the organization of social networks is selected so as to optimize problem-solving competence at the group level. Here we study the influence of the social network topology on the performance of a group of agents whose task is to locate the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence networks. In the case those messages convey accurate information on the proximity of the solution (i.e., for smooth fitness landscapes), we find that high connectivity as well as centralization boosts the group performance. For rugged landscapes, however, these characteristics are beneficial for small groups only. For large groups, it is advantageous to slow down the information transmission through the network to avoid local maximum traps. Long-range links and modularity have marginal effects on the performance of the group, except for a very narrow region of the model parameters.

The Command and Control of the Grand Armee: Napoleon as Organizational Designer

DTIC Science & Technology

2009-06-01

AUTHOR(S) Norman L. Durham 5. FUNDING NUMBERS 7 . PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000...served as the framework for a highly effective command and control system. This command and control network allowed Napoleon to dominate a war with...within his organizational design was a vast information network that served as the framework for a highly effective command and control system. This

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.

Transition of the functional brain network related to increasing cognitive demands.

PubMed

Finc, Karolina; Bonna, Kamil; Lewandowska, Monika; Wolak, Tomasz; Nikadon, Jan; Dreszer, Joanna; Duch, Włodzisław; Kühn, Simone

2017-04-22

Network neuroscience provides tools that can easily be used to verify main assumptions of the global workspace theory (GWT), such as the existence of highly segregated information processing during effortless tasks performance, engagement of multiple distributed networks during effortful tasks and the critical role of long-range connections in workspace formation. A number of studies support the assumptions of GWT by showing the reorganization of the whole-brain functional network during cognitive task performance; however, the involvement of specific large scale networks in the formation of workspace is still not well-understood. (1) to examine changes in the whole-brain functional network under increased cognitive demands of working memory during an n-back task, and their relationship with behavioral outcomes; and (2) to provide a comprehensive description of local changes that may be involved in the formation of the global workspace, using hub detection and network-based statistic. Our results show that network modularity decreased with increasing cognitive demands, and this change allowed us to predict behavioral performance. The number of connector hubs increased, whereas the number of provincial hubs decreased when the task became more demanding. We also found that the default mode network (DMN) increased its connectivity to other networks while decreasing connectivity between its own regions. These results, apart from replicating previous findings, provide a valuable insight into the mechanisms of the formation of the global workspace, highlighting the role of the DMN in the processes of network integration. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Miao; Wang, Wei; Liu, Yang

Three-dimensional (3D) network structure has been envisioned as a superior architecture for lithium ion battery (LIB) electrodes, which enhances both ion and electron transport to significantly improve battery performance. Herein, a 3D carbon nano-network is fabricated through chemical vapor deposition of carbon on a scalably manufactured 3D porous anodic alumina (PAA) template. As a demonstration on the applicability of 3D carbon nano-network for LIB electrodes, the low conductivity active material, TiO 2, is then uniformly coated on the 3D carbon nano-network using atomic layer deposition. High power performance is demonstrated in the 3D C/TiO 2 electrodes, where the parallel tubesmore » and gaps in the 3D carbon nano-network facilitates fast Li ion transport. A large areal capacity of ~0.37 mAh·cm –2 is achieved due to the large TiO 2 mass loading in the 60 µm-thick 3D C/TiO 2 electrodes. At a test rate of C/5, the 3D C/TiO 2 electrode with 18 nm-thick TiO 2 delivers a high gravimetric capacity of ~240 mAh g –1, calculated with the mass of the whole electrode. A long cycle life of over 1000 cycles with a capacity retention of 91% is demonstrated at 1C. In this study, the effects of the electrical conductivity of carbon nano-network, ion diffusion, and the electrolyte permeability on the rate performance of these 3D C/TiO 2 electrodes are systematically studied.« less

An Efficient Framework Model for Optimizing Routing Performance in VANETs

PubMed Central

Zulkarnain, Zuriati Ahmad; Subramaniam, Shamala

2018-01-01

Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED). PMID:29462884

Optimal Quantum Spatial Search on Random Temporal Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Interfacing a high performance disk array file server to a Gigabit LAN

NASA Technical Reports Server (NTRS)

Seshan, Srinivasan; Katz, Randy H.

1993-01-01

Our previous prototype, RAID-1, identified several bottlenecks in typical file server architectures. The most important bottleneck was the lack of a high-bandwidth path between disk, memory, and the network. Workstation servers, such as the Sun-4/280, have very slow access to peripherals on busses far from the CPU. For the RAID-2 system, we addressed this problem by designing a crossbar interconnect, Xbus board, that provides a 40MB/s path between disk, memory, and the network interfaces. However, this interconnect does not provide the system CPU with low latency access to control the various interfaces. To provide a high data rate to clients on the network, we were forced to carefully and efficiently design the network software. A block diagram of the system hardware architecture is given. In the following subsections, we describe pieces of the RAID-2 file server hardware that had a significant impact on the design of the network interface.

Object-oriented Approach to High-level Network Monitoring and Management

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi

2000-01-01

An absolute prerequisite for the management of large investigating methods to build high-level monitoring computer networks is the ability to measure their systems that are built on top of existing monitoring performance. Unless we monitor a system, we cannot tools. Due to the heterogeneous nature of the hope to manage and control its performance. In this underlying systems at NASA Langley Research Center, paper, we describe a network monitoring system that we use an object-oriented approach for the design, we are currently designing and implementing. Keeping, first, we use UML (Unified Modeling Language) to in mind the complexity of the task and the required model users' requirements. Second, we identify the flexibility for future changes, we use an object-oriented existing capabilities of the underlying monitoring design methodology. The system is built using the system. Third, we try to map the former with the latter. APIs offered by the HP OpenView system.

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

2016-09-01

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

NASA Astrophysics Data System (ADS)

Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

2017-03-01

Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

Fibrous hybrid of graphene and sulfur nanocrystals for high-performance lithium-sulfur batteries.

PubMed

Zhou, Guangmin; Yin, Li-Chang; Wang, Da-Wei; Li, Lu; Pei, Songfeng; Gentle, Ian Ross; Li, Feng; Cheng, Hui-Ming

2013-06-25

Graphene-sulfur (G-S) hybrid materials with sulfur nanocrystals anchored on interconnected fibrous graphene are obtained by a facile one-pot strategy using a sulfur/carbon disulfide/alcohol mixed solution. The reduction of graphene oxide and the formation/binding of sulfur nanocrystals were integrated. The G-S hybrids exhibit a highly porous network structure constructed by fibrous graphene, many electrically conducting pathways, and easily tunable sulfur content, which can be cut and pressed into pellets to be directly used as lithium-sulfur battery cathodes without using a metal current-collector, binder, and conductive additive. The porous network and sulfur nanocrystals enable rapid ion transport and short Li(+) diffusion distance, the interconnected fibrous graphene provides highly conductive electron transport pathways, and the oxygen-containing (mainly hydroxyl/epoxide) groups show strong binding with polysulfides, preventing their dissolution into the electrolyte based on first-principles calculations. As a result, the G-S hybrids show a high capacity, an excellent high-rate performance, and a long life over 100 cycles. These results demonstrate the great potential of this unique hybrid structure as cathodes for high-performance lithium-sulfur batteries.

SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

PubMed

Jeong, Hyundoo; Yoon, Byung-Jun

2017-03-14

Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .

3D hierarchical MnO2 nanorod/welded Ag-nanowire-network composites for high-performance supercapacitor electrodes.

PubMed

Qiao, Zhensong; Yang, Xiaopeng; Yang, Shuhua; Zhang, Liqiang; Cao, Bingqiang

2016-06-28

3D MnO2 nanorod/welded Ag-nanowire-network supercapacitor electrodes were prepared. Welding treatment of the Ag nanowire-network leads to low resistance and long lifetime. Galvanostatic charge/discharge (GCD) induces an ever-lasting morphology changing from flower-like to honeycomb-like for MnO2, which manifests as increasing specific capacitance to 663.4 F g(-1) after 7000 GCD cycles.

High Performance Computing and Network Program. Hearing before the Subcommittee on Science of the Committee on Science, Space, and Technology, House of Representatives, One Hundred Third Congress, First Session.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. House Committee on Science, Space and Technology.

The purpose of the hearing transcribed in this document was to obtain the views of representatives of network user and provider communities regarding the path the National Science Foundation (NSF) is taking for recompetition of the NSFNET computer network. In particular the committee was interested in the consistency of the evolution of NSFNET…

Data Transfer Advisor with Transport Profiling Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, Nageswara S.; Liu, Qiang; Yun, Daqing

The network infrastructures have been rapidly upgraded in many high-performance networks (HPNs). However, such infrastructure investment has not led to corresponding performance improvement in big data transfer, especially at the application layer, largely due to the complexity of optimizing transport control on end hosts. We design and implement ProbData, a PRofiling Optimization Based DAta Transfer Advisor, to help users determine the most effective data transfer method with the most appropriate control parameter values to achieve the best data transfer performance. ProbData employs a profiling optimization based approach to exploit the optimal operational zone of various data transfer methods in supportmore » of big data transfer in extreme scale scientific applications. We present a theoretical framework of the optimized profiling approach employed in ProbData as wellas its detailed design and implementation. The advising procedure and performance benefits of ProbData are illustrated and evaluated by proof-of-concept experiments in real-life networks.« less

Advanced Graphene-Based Binder-Free Electrodes for High-Performance Energy Storage.

PubMed

Ji, Junyi; Li, Yang; Peng, Wenchao; Zhang, Guoliang; Zhang, Fengbao; Fan, Xiaobin

2015-09-23

The increasing demand for energy has triggered tremendous research effort for the development of high-performance and durable energy-storage devices. Advanced graphene-based electrodes with high electrical conductivity and ion accessibility can exhibit superior electrochemical performance in energy-storage devices. Among them, binder-free configurations can enhance the electron conductivity of the electrode, which leads to a higher capacity by avoiding the addition of non-conductive and inactive binders. Graphene, a 2D material, can be fabricated into a porous and flexible structure with an interconnected conductive network. Such a conductive structure is favorable for both electron and ion transport to the entire electrode surface. In this review, the main processes used to prepare binder-free graphene-based hybrids with high porosity and well-designed electron conductive networks are summarized. Then, the applications of free-standing binder-free graphene-based electrodes in energy-storage devices are discussed. Future research aspects with regard to overcoming the technological bottlenecks are also proposed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Datum maintenance of the main Egyptian geodetic control networks by utilizing Precise Point Positioning "PPP" technique

NASA Astrophysics Data System (ADS)

Rabah, Mostafa; Elmewafey, Mahmoud; Farahan, Magda H.

2016-06-01

A geodetic control network is the wire-frame or the skeleton on which continuous and consistent mapping, Geographic Information Systems (GIS), and surveys are based. Traditionally, geodetic control points are established as permanent physical monuments placed in the ground and precisely marked, located, and documented. With the development of satellite surveying methods and their availability and high degree of accuracy, a geodetic control network could be established by using GNSS and referred to an international terrestrial reference frame used as a three-dimensional geocentric reference system for a country. Based on this concept, in 1992, the Egypt Survey Authority (ESA) established two networks, namely High Accuracy Reference Network (HARN) and the National Agricultural Cadastral Network (NACN). To transfer the International Terrestrial Reference Frame to the HARN, the HARN was connected with four IGS stations. The processing results were 1:10,000,000 (Order A) for HARN and 1:1,000,000 (Order B) for NACN relative network accuracy standard between stations defined in ITRF1994 Epoch1996. Since 1996, ESA did not perform any updating or maintaining works for these networks. To see how non-performing maintenance degrading the values of the HARN and NACN, the available HARN and NACN stations in the Nile Delta were observed. The Processing of the tested part was done by CSRS-PPP Service based on utilizing Precise Point Positioning "PPP" and Trimble Business Center "TBC". The study shows the feasibility of Precise Point Positioning in updating the absolute positioning of the HARN network and its role in updating the reference frame (ITRF). The study also confirmed the necessity of the absent role of datum maintenance of Egypt networks.

A High-Performance Recycling Solution for Polystyrene Achieved by the Synthesis of Renewable Poly(thioether) Networks Derived from d-Limonene

PubMed Central

Nash, Landon D.; Rodriguez, Jennifer N.; Lonnecker, Alexander T.; Raymond, Jeffery E.; Wilson, Thomas S.; Wooley, Karen L.; Maitland, Duncan J.

2014-01-01

Nanocomposite polymers have been prepared using a new sustainable materials synthesis process in which d-Limonene functions simultaneously both as a solvent for recycling polystyrene (PS) waste and as a monomer that undergoes UV-catalyzed thiol-ene polymerization reactions with polythiol co-monomers to afford polymeric products comprised of precipitated PS phases dispersed throughout elastomeric poly(thioether) networks. These blended networks exhibit mechanical properties that greatly exceed those of either polystyrene or the poly(thioether) network homopolymers alone. PMID:24249666

Relations between mental health team characteristics and work role performance.

PubMed

Fleury, Marie-Josée; Grenier, Guy; Bamvita, Jean-Marie; Farand, Lambert

2017-01-01

Effective mental health care requires a high performing, interprofessional team. Among 79 mental health teams in Quebec (Canada), this exploratory study aims to 1) determine the association between work role performance and a wide range of variables related to team effectiveness according to the literature, and to 2) using structural equation modelling, assess the covariance between each of these variables as well as the correlation with other exogenous variables. Work role performance was measured with an adapted version of a work role questionnaire. Various independent variables including team manager characteristics, user characteristics, team profiles, clinical activities, organizational culture, network integration strategies and frequency/satisfaction of interactions with other teams or services were analyzed under the structural equation model. The later provided a good fit with the data. Frequent use of standardized procedures and evaluation tools (e.g. screening and assessment tools for mental health disorders) and team manager seniority exerted the most direct effect on work role performance. While network integration strategies had little effect on work role performance, there was a high covariance between this variable and those directly affecting work role performance among mental health teams. The results suggest that the mental healthcare system should apply standardized procedures and evaluation tools and, to a lesser extent, clinical approaches to improve work role performance in mental health teams. Overall, a more systematic implementation of network integration strategies may contribute to improved work role performance in mental health care.

Relations between mental health team characteristics and work role performance

PubMed Central

Grenier, Guy; Bamvita, Jean-Marie; Farand, Lambert

2017-01-01

Effective mental health care requires a high performing, interprofessional team. Among 79 mental health teams in Quebec (Canada), this exploratory study aims to 1) determine the association between work role performance and a wide range of variables related to team effectiveness according to the literature, and to 2) using structural equation modelling, assess the covariance between each of these variables as well as the correlation with other exogenous variables. Work role performance was measured with an adapted version of a work role questionnaire. Various independent variables including team manager characteristics, user characteristics, team profiles, clinical activities, organizational culture, network integration strategies and frequency/satisfaction of interactions with other teams or services were analyzed under the structural equation model. The later provided a good fit with the data. Frequent use of standardized procedures and evaluation tools (e.g. screening and assessment tools for mental health disorders) and team manager seniority exerted the most direct effect on work role performance. While network integration strategies had little effect on work role performance, there was a high covariance between this variable and those directly affecting work role performance among mental health teams. The results suggest that the mental healthcare system should apply standardized procedures and evaluation tools and, to a lesser extent, clinical approaches to improve work role performance in mental health teams. Overall, a more systematic implementation of network integration strategies may contribute to improved work role performance in mental health care. PMID:28991923

Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke.

PubMed

Zhao, Zhiyong; Wu, Jie; Fan, Mingxia; Yin, Dazhi; Tang, Chaozheng; Gong, Jiayu; Xu, Guojun; Gao, Xinjie; Yu, Qiurong; Yang, Hao; Sun, Limin; Jia, Jie

2018-04-24

Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra- and inter-network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty-three left subcortical chronic stroke patients and 34 healthy controls underwent resting-state functional magnetic resonance imaging. Eleven resting-state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl-Meyer Assessment (FMA) scores (hand + wrist). With respect to inter-network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra- and inter-network alterations associated with motor-visual attention and high-order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke. © 2018 Wiley Periodicals, Inc.

GLOBECOM '87 - Global Telecommunications Conference, Tokyo, Japan, Nov. 15-18, 1987, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

The present conference on global telecommunications discusses topics in the fields of Integrated Services Digital Network (ISDN) technology field trial planning and results to date, motion video coding, ISDN networking, future network communications security, flexible and intelligent voice/data networks, Asian and Pacific lightwave and radio systems, subscriber radio systems, the performance of distributed systems, signal processing theory, satellite communications modulation and coding, and terminals for the handicapped. Also discussed are knowledge-based technologies for communications systems, future satellite transmissions, high quality image services, novel digital signal processors, broadband network access interface, traffic engineering for ISDN design and planning, telecommunications software, coherent optical communications, multimedia terminal systems, advanced speed coding, portable and mobile radio communications, multi-Gbit/second lightwave transmission systems, enhanced capability digital terminals, communications network reliability, advanced antimultipath fading techniques, undersea lightwave transmission, image coding, modulation and synchronization, adaptive signal processing, integrated optical devices, VLSI technologies for ISDN, field performance of packet switching, CSMA protocols, optical transport system architectures for broadband ISDN, mobile satellite communications, indoor wireless communication, echo cancellation in communications, and distributed network algorithms.

Optical and mechanical behaviors of glassy silicone networks derived from linear siloxane precursors

NASA Astrophysics Data System (ADS)

Jang, Heejun; Seo, Wooram; Kim, Hyungsun; Lee, Yoonjoo; Kim, Younghee

2016-01-01

Silicon-based inorganic polymers are promising materials as matrix materials for glass fiber composites because of their good process ability, transparency, and thermal property. In this study, for utilization as a matrix precursor for a glass-fiber-reinforced composite, glassy silicone networks were prepared via hydrosilylation of linear/pendant Si-H polysiloxanes and the C=C bonds of viny-lterminated linear/cyclic polysiloxanes. 13C nuclear magnetic resonance spectroscopy was used to determine the structure of the cross-linked states, and a thermal analysis was performed. To assess the mechanical properties of the glassy silicone networks, we performed nanoindentation and 4-point bending tests. Cross-linked networks derived from siloxane polymers are thermally and optically more stable at high temperatures. Different cross-linking agents led to final networks with different properties due to differences in the molecular weights and structures. After stepped postcuring, the Young's modulus and the hardness of the glassy silicone networks increased; however, the brittleness also increased. The characteristics of the cross-linking agent played an important role in the functional glassy silicone networks.

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

PubMed Central

Abbott, L. F.; Sompolinsky, Haim

2017-01-01

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it. A plausible synaptic plasticity rule for learning that balances weight configurations is presented. Our theory predicts an optimal ratio of the number of excitatory and inhibitory synapses for maximizing the encoding capacity of balanced networks for given statistics of afferent activations. Previous work has shown that balanced networks amplify spatiotemporal variability and account for observed asynchronous irregular states. Here we present a distinct type of balanced network that amplifies small changes in the impinging signals and emerges automatically from learning to perform neuronal and network functions robustly. PMID:29042519

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Using an Extended Kalman Filter Learning Algorithm for Feed-Forward Neural Networks to Describe Tracer Correlations

NASA Technical Reports Server (NTRS)

Lary, David J.; Mussa, Yussuf

2004-01-01

In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

A smarter way to network.

PubMed

Cross, Rob; Thomas, Robert

2011-01-01

The adage "It's not what you know, it's who you know" is true. The right social network can have a huge impact on your success. But many people have misguided ideas about what makes a network strong: They believe the key is having a large circle filled with high-powered contacts. That's not the right approach, say Cross, of UVA's McIntire School of Commerce, and Thomas, of the Accenture Institute for High Performance. The authors, who have spent years researching how organizations can capitalize on employees' social networks, have seen that the happiest, highest-performing executives have a different kind of network: select but diverse, made up of high-quality relationships with people who come from varying spheres and from up and down the corporate ladder. Effective networks typically range in size from 12 to 18 people. They help managers learn, make decisions with less bias, and grow personally. Cross and Thomas have found that they include six critical kinds of connections: people who provide information, ideas, or expertise; formally and informally powerful people, who offer mentoring and political support; people who give developmental feedback; people who lend personal support; people who increase your sense of purpose or worth; and people who promote work/life balance. Moreover, the best kind of connections are "energizers"--positive, trustworthy individuals who enjoy other people and always see opportunities, even in challenging situations. If your network doesn't look like this, you can follow a four-step process to improve it. You'll need to identify who your connections are and what they offer you, back away from redundant and energy-draining connections, fill holes in your network with the right kind of people, and work to make the most of your contacts. Do this, and in due course, you'll have a network that steers the best opportunities, ideas, and talent your way.

Fusion solution for soldier wearable gunfire detection systems

NASA Astrophysics Data System (ADS)

Cakiades, George; Desai, Sachi; Deligeorges, Socrates; Buckland, Bruce E.; George, Jemin

2012-06-01

Currently existing acoustic based Gunfire Detection Systems (GDS) such as soldier wearable, vehicle mounted, and fixed site devices provide enemy detection and localization capabilities to the user. However, the solution to the problem of portability versus performance tradeoff remains elusive. The Data Fusion Module (DFM), described herein, is a sensor/platform agnostic software supplemental tool that addresses this tradeoff problem by leveraging existing soldier networks to enhance GDS performance across a Tactical Combat Unit (TCU). The DFM software enhances performance by leveraging all available acoustic GDS information across the TCU synergistically to calculate highly accurate solutions more consistently than any individual GDS in the TCU. The networked sensor architecture provides additional capabilities addressing the multiple shooter/fire-fight problems in addition to sniper detection/localization. The addition of the fusion solution to the overall Size, Weight and Power & Cost (SWaP&C) is zero to negligible. At the end of the first-year effort, the DFM integrated sensor network's performance was impressive showing improvements upwards of 50% in comparison to a single sensor solution. Further improvements are expected when the networked sensor architecture created in this effort is fully exploited.

Stretchable Electronic Sensors of Nanocomposite Network Films for Ultrasensitive Chemical Vapor Sensing.

PubMed

Yan, Hong; Zhong, Mengjuan; Lv, Ze; Wan, Pengbo

2017-11-01

A stretchable, transparent, and body-attachable chemical sensor is assembled from the stretchable nanocomposite network film for ultrasensitive chemical vapor sensing. The stretchable nanocomposite network film is fabricated by in situ preparation of polyaniline/MoS 2 (PANI/MoS 2 ) nanocomposite in MoS 2 suspension and simultaneously nanocomposite deposition onto prestrain elastomeric polydimethylsiloxane substrate. The assembled stretchable electronic sensor demonstrates ultrasensitive sensing performance as low as 50 ppb, robust sensing stability, and reliable stretchability for high-performance chemical vapor sensing. The ultrasensitive sensing performance of the stretchable electronic sensors could be ascribed to the synergistic sensing advantages of MoS 2 and PANI, higher specific surface area, the reliable sensing channels of interconnected network, and the effectively exposed sensing materials. It is expected to hold great promise for assembling various flexible stretchable chemical vapor sensors with ultrasensitive sensing performance, superior sensing stability, reliable stretchability, and robust portability to be potentially integrated into wearable electronics for real-time monitoring of environment safety and human healthcare. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tunable Core-Shell Single-Walled Carbon Nanotube-Cu2S Networked Nanocomposites as High-Performance Cathodes for Lithium-ion Batteries

DOE PAGES

Meng, Xiangbo; Riha, Shannon C.; Libera, Joseph A.; ...

2015-01-24

In this study, nanoscale copper(I) sulfide (n-Cu2S) was deposited over networks of single-walled carbon nanotubes (SWCNTs) by atomic layer deposition (ALD). This synthetic route provides a high degree of control for tuning the materials properties. The resulting core shell SWCNT-n-Cu2S composite structure ensures an intimate contact between the two components while maintaining a high porosity for efficient transport of charges. Indeed, electrochemical testing demonstrates that these nanocomposites are promising as cathodes in lithium-ion batteries (LIBs), exhibiting excellent stability over 200 discharge-charge cycles with a sustainable, high capacity of 260 mAh g(-1) (92% of the theoretical value in terms of Cu2S)more » and >99% Coulombic efficiency. This work establishes a general strategy for developing high-performance nanoscale electrode materials.« less

Piezoresistive Sensor with High Elasticity Based on 3D Hybrid Network of Sponge@CNTs@Ag NPs.

PubMed

Zhang, Hui; Liu, Nishuang; Shi, Yuling; Liu, Weijie; Yue, Yang; Wang, Siliang; Ma, Yanan; Wen, Li; Li, Luying; Long, Fei; Zou, Zhengguang; Gao, Yihua

2016-08-31

Pressure sensors with high elasticity are in great demand for the realization of intelligent sensing, but there is a need to develope a simple, inexpensive, and scalable method for the manufacture of the sensors. Here, we reported an efficient, simple, facile, and repeatable "dipping and coating" process to manufacture a piezoresistive sensor with high elasticity, based on homogeneous 3D hybrid network of carbon nanotubes@silver nanoparticles (CNTs@Ag NPs) anchored on a skeleton sponge. Highly elastic, sensitive, and wearable sensors are obtained using the porous structure of sponge and the synergy effect of CNTs/Ag NPs. Our sensor was also tested for over 2000 compression-release cycles, exhibiting excellent elasticity and cycling stability. Sensors with high performance and a simple fabrication process are promising devices for commercial production in various electronic devices, for example, sport performance monitoring and man-machine interfaces.

Team performance in networked supervisory control of unmanned air vehicles: effects of automation, working memory, and communication content.

PubMed

McKendrick, Ryan; Shaw, Tyler; de Visser, Ewart; Saqer, Haneen; Kidwell, Brian; Parasuraman, Raja

2014-05-01

Assess team performance within a net-worked supervisory control setting while manipulating automated decision aids and monitoring team communication and working memory ability. Networked systems such as multi-unmanned air vehicle (UAV) supervision have complex properties that make prediction of human-system performance difficult. Automated decision aid can provide valuable information to operators, individual abilities can limit or facilitate team performance, and team communication patterns can alter how effectively individuals work together. We hypothesized that reliable automation, higher working memory capacity, and increased communication rates of task-relevant information would offset performance decrements attributed to high task load. Two-person teams performed a simulated air defense task with two levels of task load and three levels of automated aid reliability. Teams communicated and received decision aid messages via chat window text messages. Task Load x Automation effects were significant across all performance measures. Reliable automation limited the decline in team performance with increasing task load. Average team spatial working memory was a stronger predictor than other measures of team working memory. Frequency of team rapport and enemy location communications positively related to team performance, and word count was negatively related to team performance. Reliable decision aiding mitigated team performance decline during increased task load during multi-UAV supervisory control. Team spatial working memory, communication of spatial information, and team rapport predicted team success. An automated decision aid can improve team performance under high task load. Assessment of spatial working memory and the communication of task-relevant information can help in operator and team selection in supervisory control systems.

High-performance reconfigurable hardware architecture for restricted Boltzmann machines.

PubMed

Ly, Daniel Le; Chow, Paul

2010-11-01

Despite the popularity and success of neural networks in research, the number of resulting commercial or industrial applications has been limited. A primary cause for this lack of adoption is that neural networks are usually implemented as software running on general-purpose processors. Hence, a hardware implementation that can exploit the inherent parallelism in neural networks is desired. This paper investigates how the restricted Boltzmann machine (RBM), which is a popular type of neural network, can be mapped to a high-performance hardware architecture on field-programmable gate array (FPGA) platforms. The proposed modular framework is designed to reduce the time complexity of the computations through heavily customized hardware engines. A method to partition large RBMs into smaller congruent components is also presented, allowing the distribution of one RBM across multiple FPGA resources. The framework is tested on a platform of four Xilinx Virtex II-Pro XC2VP70 FPGAs running at 100 MHz through a variety of different configurations. The maximum performance was obtained by instantiating an RBM of 256 × 256 nodes distributed across four FPGAs, which resulted in a computational speed of 3.13 billion connection-updates-per-second and a speedup of 145-fold over an optimized C program running on a 2.8-GHz Intel processor.

Traffic signal synchronization in the saturated high-density grid road network.

PubMed

Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye

2015-01-01

Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN.