Distributed weighted least-squares estimation with fast convergence for large-scale systems☆

PubMed Central

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods. PMID:25641976

Problems of Global Networks of Gravitational Detectors

NASA Astrophysics Data System (ADS)

Kuchik, E. K.; Rudenko, V. N.

We describe the network of gravitational wave detectors which now exist in the world: Stanford-Louisiana-Pert-Geneva-Moscow. A computer simulation of a gravitational wave detection is performed. Proposals for the creation of a global observational gravitational wave service are made.

Configuring compute nodes of a parallel computer in an operational group into a plurality of independent non-overlapping collective networks

DOEpatents

Archer, Charles J.; Inglett, Todd A.; Ratterman, Joseph D.; Smith, Brian E.

2010-03-02

Methods, apparatus, and products are disclosed for configuring compute nodes of a parallel computer in an operational group into a plurality of independent non-overlapping collective networks, the compute nodes in the operational group connected together for data communications through a global combining network, that include: partitioning the compute nodes in the operational group into a plurality of non-overlapping subgroups; designating one compute node from each of the non-overlapping subgroups as a master node; and assigning, to the compute nodes in each of the non-overlapping subgroups, class routing instructions that organize the compute nodes in that non-overlapping subgroup as a collective network such that the master node is a physical root.

DMA engine for repeating communication patterns

DOEpatents

Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard; Vranas, Pavlos

2010-09-21

A parallel computer system is constructed as a network of interconnected compute nodes to operate a global message-passing application for performing communications across the network. Each of the compute nodes includes one or more individual processors with memories which run local instances of the global message-passing application operating at each compute node to carry out local processing operations independent of processing operations carried out at other compute nodes. Each compute node also includes a DMA engine constructed to interact with the application via Injection FIFO Metadata describing multiple Injection FIFOs where each Injection FIFO may containing an arbitrary number of message descriptors in order to process messages with a fixed processing overhead irrespective of the number of message descriptors included in the Injection FIFO.

Estimating multi-period global cost efficiency and productivity change of systems with network structures

NASA Astrophysics Data System (ADS)

Tohidnia, S.; Tohidi, G.

2018-02-01

The current paper develops three different ways to measure the multi-period global cost efficiency for homogeneous networks of processes when the prices of exogenous inputs are known at all time periods. A multi-period network data envelopment analysis model is presented to measure the minimum cost of the network system based on the global production possibility set. We show that there is a relationship between the multi-period global cost efficiency of network system and its subsystems, and also its processes. The proposed model is applied to compute the global cost Malmquist productivity index for measuring the productivity changes of network system and each of its process between two time periods. This index is circular. Furthermore, we show that the productivity changes of network system can be defined as a weighted average of the process productivity changes. Finally, a numerical example will be presented to illustrate the proposed approach.

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

Global Value Trees

PubMed Central

Zhu, Zhen; Puliga, Michelangelo; Cerina, Federica; Chessa, Alessandro; Riccaboni, Massimo

2015-01-01

The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term “global value chains” (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the evolution of the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs. PMID:25978067

Locally excitatory, globally inhibitory oscillator networks: theory and application to scene segmentation

NASA Astrophysics Data System (ADS)

Wang, DeLiang; Terman, David

1995-01-01

A novel class of locally excitatory, globally inhibitory oscillator networks (LEGION) is proposed and investigated analytically and by computer simulation. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing other oscillators from jumping up. We show analytically that with the selective gating mechanism the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate LEGION's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding, and may provide an effective computational framework for scene segmentation and figure/ground segregation.

Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin.

PubMed

Guzzi, Pietro Hiram; Milenkovic, Tijana

2018-05-01

Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.

Dispatching packets on a global combining network of a parallel computer

DOEpatents

Almasi, Gheorghe [Ardsley, NY; Archer, Charles J [Rochester, MN

2011-07-19

Methods, apparatus, and products are disclosed for dispatching packets on a global combining network of a parallel computer comprising a plurality of nodes connected for data communications using the network capable of performing collective operations and point to point operations that include: receiving, by an origin system messaging module on an origin node from an origin application messaging module on the origin node, a storage identifier and an operation identifier, the storage identifier specifying storage containing an application message for transmission to a target node, and the operation identifier specifying a message passing operation; packetizing, by the origin system messaging module, the application message into network packets for transmission to the target node, each network packet specifying the operation identifier and an operation type for the message passing operation specified by the operation identifier; and transmitting, by the origin system messaging module, the network packets to the target node.

Electronic neural networks for global optimization

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.

1990-01-01

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

The Network of Global Corporate Control

PubMed Central

Vitali, Stefania; Glattfelder, James B.; Battiston, Stefano

2011-01-01

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy makers. PMID:22046252

Networking: the view from HEP

NASA Astrophysics Data System (ADS)

McKee, Shawn

2017-10-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. National and global-scale collaborations that characterize HEP would not be feasible without ubiquitous capable networks. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. This paper will briefly discuss the history of networking in HEP, the current activities and challenges we are facing, and try to provide some understanding of where networking may be going in the next 5 to 10 years.

Networks in ATLAS

NASA Astrophysics Data System (ADS)

McKee, Shawn; ATLAS Collaboration

2017-10-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks. We will report on a number of networking initiatives in ATLAS including participation in the global perfSONAR network monitoring and measuring efforts of WLCG and OSG, the collaboration with the LHCOPN/LHCONE effort, the integration of network awareness into PanDA, the use of the evolving ATLAS analytics framework to better understand our networks and the changes in our DDM system to allow remote access to data. We will also discuss new efforts underway that are exploring the inclusion and use of software defined networks (SDN) and how ATLAS might benefit from: • Orchestration and optimization of distributed data access and data movement. • Better control of workflows, end to end. • Enabling prioritization of time-critical vs normal tasks • Improvements in the efficiency of resource usage

Cultural and Pedagogical Implications of a Global E-Learning Programme

ERIC Educational Resources Information Center

Selinger, Michelle

2004-01-01

An eleven country internal evaluation of the Cisco Networking Academy program across Europe, the Middle East and Africa, revealed a number of issues related to the globalization of e-learning. The Academy program is a 280-hour web-based course that teaches students to install, maintain and troubleshoot computer networks. It was developed in the US…

Recognition of partially occluded threat objects using the annealed Hopefield network

NASA Technical Reports Server (NTRS)

Kim, Jung H.; Yoon, Sung H.; Park, Eui H.; Ntuen, Celestine A.

1992-01-01

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

Data Handling and Communication

NASA Astrophysics Data System (ADS)

Hemmer, FréDéRic Giorgio Innocenti, Pier

The following sections are included: * Introduction * Computing Clusters and Data Storage: The New Factory and Warehouse * Local Area Networks: Organizing Interconnection * High-Speed Worldwide Networking: Accelerating Protocols * Detector Simulation: Events Before the Event * Data Analysis and Programming Environment: Distilling Information * World Wide Web: Global Networking * References

Synchronizing compute node time bases in a parallel computer

DOEpatents

Chen, Dong; Faraj, Daniel A; Gooding, Thomas M; Heidelberger, Philip

2015-01-27

Synchronizing time bases in a parallel computer that includes compute nodes organized for data communications in a tree network, where one compute node is designated as a root, and, for each compute node: calculating data transmission latency from the root to the compute node; configuring a thread as a pulse waiter; initializing a wakeup unit; and performing a local barrier operation; upon each node completing the local barrier operation, entering, by all compute nodes, a global barrier operation; upon all nodes entering the global barrier operation, sending, to all the compute nodes, a pulse signal; and for each compute node upon receiving the pulse signal: waking, by the wakeup unit, the pulse waiter; setting a time base for the compute node equal to the data transmission latency between the root node and the compute node; and exiting the global barrier operation.

Synchronizing compute node time bases in a parallel computer

DOEpatents

Chen, Dong; Faraj, Daniel A; Gooding, Thomas M; Heidelberger, Philip

2014-12-30

Synchronizing time bases in a parallel computer that includes compute nodes organized for data communications in a tree network, where one compute node is designated as a root, and, for each compute node: calculating data transmission latency from the root to the compute node; configuring a thread as a pulse waiter; initializing a wakeup unit; and performing a local barrier operation; upon each node completing the local barrier operation, entering, by all compute nodes, a global barrier operation; upon all nodes entering the global barrier operation, sending, to all the compute nodes, a pulse signal; and for each compute node upon receiving the pulse signal: waking, by the wakeup unit, the pulse waiter; setting a time base for the compute node equal to the data transmission latency between the root node and the compute node; and exiting the global barrier operation.

Using SPEEDES to simulate the blue gene interconnect network

NASA Technical Reports Server (NTRS)

Springer, P.; Upchurch, E.

2003-01-01

JPL and the Center for Advanced Computer Architecture (CACR) is conducting application and simulation analyses of BG/L in order to establish a range of effectiveness for the Blue Gene/L MPP architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution.

Modeling Physiological Systems in the Human Body as Networks of Quasi-1D Fluid Flows

NASA Astrophysics Data System (ADS)

Staples, Anne

2008-11-01

Extensive research has been done on modeling human physiology. Most of this work has been aimed at developing detailed, three-dimensional models of specific components of physiological systems, such as a cell, a vein, a molecule, or a heart valve. While efforts such as these are invaluable to our understanding of human biology, if we were to construct a global model of human physiology with this level of detail, computing even a nanosecond in this computational being's life would certainly be prohibitively expensive. With this in mind, we derive the Pulsed Flow Equations, a set of coupled one-dimensional partial differential equations, specifically designed to capture two-dimensional viscous, transport, and other effects, and aimed at providing accurate and fast-to-compute global models for physiological systems represented as networks of quasi one-dimensional fluid flows. Our goal is to be able to perform faster-than-real time simulations of global processes in the human body on desktop computers.

Pheromone Static Routing Strategy for Complex Networks

NASA Astrophysics Data System (ADS)

Hu, Mao-Bin; Henry, Y. K. Lau; Ling, Xiang; Jiang, Rui

2012-12-01

We adopt the concept of using pheromones to generate a set of static paths that can reach the performance of global dynamic routing strategy [Phys. Rev. E 81 (2010) 016113]. The path generation method consists of two stages. In the first stage, a pheromone is dropped to the nodes by packets forwarded according to the global dynamic routing strategy. In the second stage, pheromone static paths are generated according to the pheromone density. The output paths can greatly improve traffic systems' overall capacity on different network structures, including scale-free networks, small-world networks and random graphs. Because the paths are static, the system needs much less computational resources than the global dynamic routing strategy.

A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease.

PubMed

Zanzoni, Andreas

2016-01-01

Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies.

Engineering Amorphous Systems, Using Global-to-Local Compilation

NASA Astrophysics Data System (ADS)

Nagpal, Radhika

Emerging technologies are making it possible to assemble systems that incorporate myriad of information-processing units at almost no cost: smart materials, selfassembling structures, vast sensor networks, pervasive computing. How does one engineer robust and prespecified global behavior from the local interactions of immense numbers of unreliable parts? We discuss organizing principles and programming methodologies that have emerged from Amorphous Computing research, that allow us to compile a specification of global behavior into a robust program for local behavior.

Introduction

NASA Astrophysics Data System (ADS)

Dum, Ralph

Various types of diverse networks — communication networks, transport networks, global business networks, networks of friends, or the Internet — shape our daily life and the way we think and act. We depend on various social, economic, and technological networks that weave a tissue of businesses, governments, technologies and that contain us as citizens, users, or customers. We only become aware of our dependence if failures occur in these networks: when cities are plunged into darkness because of a breakdown of the power grid like happened recently in New York, when national economies collapse because of a failure of global financial systems like happened in the South-Asian banking crisis, or when computer viruses spread with mind-boggling speed over information networks destroying or, even worse, exposing sensitive data.

Efficient Memory Access with NumPy Global Arrays using Local Memory Access

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

Daily, Jeffrey A.; Berghofer, Dan C.

This paper discusses the work completed working with Global Arrays of data on distributed multi-computer systems and improving their performance. The tasks completed were done at Pacific Northwest National Laboratory in the Science Undergrad Laboratory Internship program in the summer of 2013 for the Data Intensive Computing Group in the Fundamental and Computational Sciences DIrectorate. This work was done on the Global Arrays Toolkit developed by this group. This toolkit is an interface for programmers to more easily create arrays of data on networks of computers. This is useful because scientific computation is often done on large amounts of datamore » sometimes so large that individual computers cannot hold all of it. This data is held in array form and can best be processed on supercomputers which often consist of a network of individual computers doing their computation in parallel. One major challenge for this sort of programming is that operations on arrays on multiple computers is very complex and an interface is needed so that these arrays seem like they are on a single computer. This is what global arrays does. The work done here is to use more efficient operations on that data that requires less copying of data to be completed. This saves a lot of time because copying data on many different computers is time intensive. The way this challenge was solved is when data to be operated on with binary operations are on the same computer, they are not copied when they are accessed. When they are on separate computers, only one set is copied when accessed. This saves time because of less copying done although more data access operations were done.« less

Viable Global Networked Learning. JSRI Occasional Paper No. 23. Latino Studies Series.

ERIC Educational Resources Information Center

Arias, Armando A., Jr.

This paper discusses an innovative paradigm for looking at computer mediated/networked teaching, learning, and research known as BESTNET (Binational English and Spanish Telecommunications Network). BESTNET is functionally defined as an international community of universities and institutions linked by common educational goals and processes,…

Node fingerprinting: an efficient heuristic for aligning biological networks.

PubMed

Radu, Alex; Charleston, Michael

2014-10-01

With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

Advanced Algorithms for Local Routing Strategy on Complex Networks

PubMed Central

Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K.; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

2016-01-01

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. PMID:27434502

Advanced Algorithms for Local Routing Strategy on Complex Networks.

PubMed

Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

2016-01-01

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70-90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

Global competition and local cooperation in a network of neural oscillators

NASA Astrophysics Data System (ADS)

Terman, David; Wang, DeLiang

An architecture of locally excitatory, globally inhibitory oscillator networks is proposed and investigated both analytically and by computer simulation. The model for each oscillator corresponds to a standard relaxation oscillator with two time scales. Oscillators are locally coupled by a scheme that resembles excitatory synaptic coupling, and each oscillator also inhibits other oscillators through a common inhibitor. Oscillators are driven to be oscillatory by external stimulation. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing the other oscillators from jumping up. We show analytically that with the selective gating mechanism, the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate the model's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding and may provide an effective computational framework for scene segmentation and figure/ ground segregation.

Results of an Internet-Based Dual-Frequency Global Differential GPS System

NASA Technical Reports Server (NTRS)

Muellerschoen, R.; Bertiger, W.; Lough, M.

2000-01-01

Observables from a global network of 18 GPS receivers are returned in real-time to JPL over the open Internet. 30 - 40 cm RSS global GPS orbits and precise dual-frequency GPS clocks are computed in real-time with JPL's Real Time Gipsy (RTG) software.

Efficiently modeling neural networks on massively parallel computers

NASA Technical Reports Server (NTRS)

Farber, Robert M.

1993-01-01

Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.

Seven Deadliest Network Attacks

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

Prowell, Stacy J; Borkin, Michael; Kraus, Robert

2010-05-01

Do you need to keep up with the latest hacks, attacks, and exploits effecting networks? Then you need "Seven Deadliest Network Attacks". This book pinpoints the most dangerous hacks and exploits specific to networks, laying out the anatomy of these attacks including how to make your system more secure. You will discover the best ways to defend against these vicious hacks with step-by-step instruction and learn techniques to make your computer and network impenetrable. Attacks detailed in this book include: Denial of Service; War Dialing; Penetration 'Testing'; Protocol Tunneling; Spanning Tree Attacks; Man-in-the-Middle; and, Password Replay. Knowledge is power, findmore » out about the most dominant attacks currently waging war on computers and networks globally. Discover the best ways to defend against these vicious attacks; step-by-step instruction shows you how. Institute countermeasures, don't be caught defenseless again, learn techniques to make your computer and network impenetrable.« less

The global transmission network of HIV-1.

PubMed

Wertheim, Joel O; Leigh Brown, Andrew J; Hepler, N Lance; Mehta, Sanjay R; Richman, Douglas D; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-01-15

Human immunodeficiency virus type 1 (HIV-1) is pandemic, but its contemporary global transmission network has not been characterized. A better understanding of the properties and dynamics of this network is essential for surveillance, prevention, and eventual eradication of HIV. Here, we apply a simple and computationally efficient network-based approach to all publicly available HIV polymerase sequences in the global database, revealing a contemporary picture of the spread of HIV-1 within and between countries. This approach automatically recovered well-characterized transmission clusters and extended other clusters thought to be contained within a single country across international borders. In addition, previously undescribed transmission clusters were discovered. Together, these clusters represent all known modes of HIV transmission. The extent of international linkage revealed by our comprehensive approach demonstrates the need to consider the global diversity of HIV, even when describing local epidemics. Finally, the speed of this method allows for near-real-time surveillance of the pandemic's progression.

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.

Discussion about the Pros and Cons and Recommendations for Multimedia Teaching in Local Vocational Schools

NASA Astrophysics Data System (ADS)

Dai, Wenhui; Fan, Ling

Globalization is an inevitable developing trend of multimedia network teaching. In our contemporary society, the world has connected by internet; it is incredible that people can not use the boundless information through campus network, multimedia classroom or single multimedia computer with out connecting the WAN. The new internet based teaching method breaking the constrains of the limited resources, distance and size of the LAN, bringing multimedia network teaching method to the world. "Open University", "Virtual Schools", "Global Classroom" and a number of new teaching systems merged rapidly.

A Small World of Neuronal Synchrony

PubMed Central

Yu, Shan; Huang, Debin; Singer, Wolf

2008-01-01

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding. PMID:18400792

Quantum computation over the butterfly network

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

Soeda, Akihito; Kinjo, Yoshiyuki; Turner, Peter S.

2011-07-15

In order to investigate distributed quantum computation under restricted network resources, we introduce a quantum computation task over the butterfly network where both quantum and classical communications are limited. We consider deterministically performing a two-qubit global unitary operation on two unknown inputs given at different nodes, with outputs at two distinct nodes. By using a particular resource setting introduced by M. Hayashi [Phys. Rev. A 76, 040301(R) (2007)], which is capable of performing a swap operation by adding two maximally entangled qubits (ebits) between the two input nodes, we show that unitary operations can be performed without adding any entanglementmore » resource, if and only if the unitary operations are locally unitary equivalent to controlled unitary operations. Our protocol is optimal in the sense that the unitary operations cannot be implemented if we relax the specifications of any of the channels. We also construct protocols for performing controlled traceless unitary operations with a 1-ebit resource and for performing global Clifford operations with a 2-ebit resource.« less

Corporations' Resistance to Innovation: The Adoption of the Internet Protocol Version 6

ERIC Educational Resources Information Center

Pazdrowski, Tomasz

2013-01-01

Computer networks that brought unprecedented growth in global communication have been using Internet Protocol version 4 (IPv4) as a standard for routing. The exponential increase in the use of the networks caused an acute shortage of available identification numbers (IP addresses). The shortage and other network communication issues are…

On Learning Cluster Coefficient of Private Networks

PubMed Central

Wang, Yue; Wu, Xintao; Zhu, Jun; Xiang, Yang

2013-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we treat a graph statistics as a function f and develop a divide and conquer approach to enforce differential privacy. The basic procedure of this approach is to first decompose the target computation f into several less complex unit computations f1, …, fm connected by basic mathematical operations (e.g., addition, subtraction, multiplication, division), then perturb the output of each fi with Laplace noise derived from its own sensitivity value and the distributed privacy threshold εi, and finally combine those perturbed fi as the perturbed output of computation f. We examine how various operations affect the accuracy of complex computations. When unit computations have large global sensitivity values, we enforce the differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We illustrate our approach by using clustering coefficient, which is a popular statistics used in social network analysis. Empirical evaluations on five real social networks and various synthetic graphs generated from three random graph models show the developed divide and conquer approach outperforms the direct approach. PMID:24429843

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Integrating Computer-Assisted Language Learning in Saudi Schools: A Change Model

ERIC Educational Resources Information Center

Alresheed, Saleh; Leask, Marilyn; Raiker, Andrea

2015-01-01

Computer-assisted language learning (CALL) technology and pedagogy have gained recognition globally for their success in supporting second language acquisition (SLA). In Saudi Arabia, the government aims to provide most educational institutions with computers and networking for integrating CALL into classrooms. However, the recognition of CALL's…

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.

PubMed

Antiqueira, Lucas; Janga, Sarath Chandra; Costa, Luciano da Fontoura

2012-11-01

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Epidemics and dimensionality in hierarchical networks

NASA Astrophysics Data System (ADS)

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

2005-07-01

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

Nondynamic Tracking Using The Global Positioning System

NASA Technical Reports Server (NTRS)

Yunck, T. P.; Wu, Sien-Chong

1988-01-01

Report describes technique for using Global Positioning System (GPS) to determine position of low Earth orbiter without need for dynamic models. Differential observing strategy requires GPS receiver on user vehicle and network of six ground receivers. Computationally efficient technique delivers decimeter accuracy on orbits down to lowest altitudes. New technique nondynamic long-arc strategy having potential for accuracy of best dynamic techniques while retaining much of computational simplicity of geometric techniques.

Brain architecture: a design for natural computation.

PubMed

Kaiser, Marcus

2007-12-15

Fifty years ago, John von Neumann compared the architecture of the brain with that of the computers he invented and which are still in use today. In those days, the organization of computers was based on concepts of brain organization. Here, we give an update on current results on the global organization of neural systems. For neural systems, we outline how the spatial and topological architecture of neuronal and cortical networks facilitates robustness against failures, fast processing and balanced network activation. Finally, we discuss mechanisms of self-organization for such architectures. After all, the organization of the brain might again inspire computer architecture.

Convex relaxations for gas expansion planning

DOE PAGES

Borraz-Sanchez, Conrado; Bent, Russell Whitford; Backhaus, Scott N.; ...

2016-01-01

Expansion of natural gas networks is a critical process involving substantial capital expenditures with complex decision-support requirements. Here, given the non-convex nature of gas transmission constraints, global optimality and infeasibility guarantees can only be offered by global optimisation approaches. Unfortunately, state-of-the-art global optimisation solvers are unable to scale up to real-world size instances. In this study, we present a convex mixed-integer second-order cone relaxation for the gas expansion planning problem under steady-state conditions. The underlying model offers tight lower bounds with high computational efficiency. In addition, the optimal solution of the relaxation can often be used to derive high-quality solutionsmore » to the original problem, leading to provably tight optimality gaps and, in some cases, global optimal solutions. The convex relaxation is based on a few key ideas, including the introduction of flux direction variables, exact McCormick relaxations, on/off constraints, and integer cuts. Numerical experiments are conducted on the traditional Belgian gas network, as well as other real larger networks. The results demonstrate both the accuracy and computational speed of the relaxation and its ability to produce high-quality solution« less

Computers at the Albuquerque Seismological Laboratory

USGS Publications Warehouse

Hoffman, J.

1979-01-01

The Worldwide Standardized Seismograph Network (WWSSN) is managed by the U.S Geological Survey in Albuquerque, N. Mex. It consists of a global network of seismographs housed in seismic observatories throughout the world. An important recent addition to this network are the Seismic Research Observatories (SRO) which combine a borehole seismometer with a modern digital data recording system. 

Networking as a Strategic Tool, 1991

NASA Technical Reports Server (NTRS)

1991-01-01

This conference focuses on the technological advances, pitfalls, requirements, and trends involved in planning and implementing an effective computer network system. The basic theme of the conference is networking as a strategic tool. Tutorials and conference presentations explore the technology and methods involved in this rapidly changing field. Future directions are explored from a global, as well as local, perspective.

Analog Processor To Solve Optimization Problems

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Eberhardt, Silvio P.; Thakoor, Anil P.

1993-01-01

Proposed analog processor solves "traveling-salesman" problem, considered paradigm of global-optimization problems involving routing or allocation of resources. Includes electronic neural network and auxiliary circuitry based partly on concepts described in "Neural-Network Processor Would Allocate Resources" (NPO-17781) and "Neural Network Solves 'Traveling-Salesman' Problem" (NPO-17807). Processor based on highly parallel computing solves problem in significantly less time.

A global distributed storage architecture

NASA Technical Reports Server (NTRS)

Lionikis, Nemo M.; Shields, Michael F.

1996-01-01

NSA architects and planners have come to realize that to gain the maximum benefit from, and keep pace with, emerging technologies, we must move to a radically different computing architecture. The compute complex of the future will be a distributed heterogeneous environment, where, to a much greater extent than today, network-based services are invoked to obtain resources. Among the rewards of implementing the services-based view are that it insulates the user from much of the complexity of our multi-platform, networked, computer and storage environment and hides its diverse underlying implementation details. In this paper, we will describe one of the fundamental services being built in our envisioned infrastructure; a global, distributed archive with near-real-time access characteristics. Our approach for adapting mass storage services to this infrastructure will become clear as the service is discussed.

GLOBECOM '86 - Global Telecommunications Conference, Houston, TX, Dec. 1-4, 1986, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

Papers are presented on local area networks; formal methods for communication protocols; computer simulation of communication systems; spread spectrum and coded communications; tropical radio propagation; VLSI for communications; strategies for increasing software productivity; multiple access communications; advanced communication satellite technologies; and spread spectrum systems. Topics discussed include Space Station communication and tracking development and design; transmission networks; modulation; data communications; computer network protocols and performance; and coding and synchronization. Consideration is given to free space optical communications systems; VSAT communication networks; network topology design; advances in adaptive filtering echo cancellation and adaptive equalization; advanced signal processing for satellite communications; the elements, design, and analysis of fiber-optic networks; and advances in digital microwave systems.

Increasing Susceptibility of the Global Network of Food Trade to Climate Disturbances

NASA Astrophysics Data System (ADS)

Puma, M. J.; Bose, S.; Chon, S.; Cook, B.

2013-12-01

Globalization of agriculture through trade liberalization has led to a dramatic transformation of the global network of food trade. The many benefits of this globalization include greater and more efficient global agricultural production, reduced variability of regional and global food supplies, and savings in global water resources. However, a potential hidden cost is an increasingly fragile network that is more susceptible to shocks or disruptions. Recent studies suggest that complex systems, like the global food trade network, may have architectural features typically associated with the existence of tipping points and susceptibility to collapse. Here we present evidence that this global agricultural network is increasingly connected, homogeneous, and in a state where network nodes (here countries) can flip between alternate states. We use production and trade data from 1986 to 2009 to identify shifts in national self sufficiency and to quantify changes in connectivity and homogeneity of the wheat, maize and rice trade. We then simulate the possible impacts of climate and crop-disease disruptions, which could potentially trigger a global food crisis through an export-restriction-induced domino effect. Changes in self-sufficiency ratio (SSR) over time for various country groups. The SSR is computed based on production and trade of cereals and starchy roots. (Top row) Time series of SSR for the Group of Eight + Five (G8+5) countries. The '+ Five' refers to the five leading emerging economies in the world. (Bottom row) Boxplots of average SSR over two periods (1986-1990 and 2005-2009) for countries designated as 'Annex I' and 'Least Developed Countries' (LDC) by the United Nations.

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.

A world-wide databridge supported by a commercial cloud provider

NASA Astrophysics Data System (ADS)

Tat Cheung, Kwong; Field, Laurence; Furano, Fabrizio

2017-10-01

Volunteer computing has the potential to provide significant additional computing capacity for the LHC experiments. One of the challenges with exploiting volunteer computing is to support a global community of volunteers that provides heterogeneous resources. However, high energy physics applications require more data input and output than the CPU intensive applications that are typically used by other volunteer computing projects. While the so-called databridge has already been successfully proposed as a method to span the untrusted and trusted domains of volunteer computing and Grid computing respective, globally transferring data between potentially poor-performing residential networks and CERN could be unreliable, leading to wasted resources usage. The expectation is that by placing a storage endpoint that is part of a wider, flexible geographical databridge deployment closer to the volunteers, the transfer success rate and the overall performance can be improved. This contribution investigates the provision of a globally distributed databridge implemented upon a commercial cloud provider.

SPACEWAY: Providing affordable and versatile communication solutions

NASA Astrophysics Data System (ADS)

Fitzpatrick, E. J.

1995-08-01

By the end of this decade, Hughes' SPACEWAY network will provide the first interactive 'bandwidth on demand' communication services for a variety of applications. High quality digital voice, interactive video, global access to multimedia databases, and transborder workgroup computing will make SPACEWAY an essential component of the computer-based workplace of the 21st century. With relatively few satellites to construct, insure, and launch -- plus extensive use of cost-effective, tightly focused spot beams on the world's most populated areas -- the high capacity SPACEWAY system can pass its significant cost savings onto its customers. The SPACEWAY network is different from other proposed global networks in that its geostationary orbit location makes it a truly market driven system: each satellite will make available extensive telecom services to hundreds of millions of people within the continuous view of that satellite, providing immediate capacity within a specific region of the world.

SPACEWAY: Providing affordable and versatile communication solutions

NASA Technical Reports Server (NTRS)

Fitzpatrick, E. J.

1995-01-01

By the end of this decade, Hughes' SPACEWAY network will provide the first interactive 'bandwidth on demand' communication services for a variety of applications. High quality digital voice, interactive video, global access to multimedia databases, and transborder workgroup computing will make SPACEWAY an essential component of the computer-based workplace of the 21st century. With relatively few satellites to construct, insure, and launch -- plus extensive use of cost-effective, tightly focused spot beams on the world's most populated areas -- the high capacity SPACEWAY system can pass its significant cost savings onto its customers. The SPACEWAY network is different from other proposed global networks in that its geostationary orbit location makes it a truly market driven system: each satellite will make available extensive telecom services to hundreds of millions of people within the continuous view of that satellite, providing immediate capacity within a specific region of the world.

Instruction of Computer Supported Collaborative Learning Environment and Students' Contribution Quality

ERIC Educational Resources Information Center

Akgün, Ergün; Akkoyunlu, Buket

2013-01-01

Along with the integration of network and communication innovations into education, those technology enriched learning environments gained importance both qualitatively and operationally. Using network and communication innovations in the education field, provides diffusion of information and global accessibility, and also allows physically…

GLOBECOM '88 - IEEE Global Telecommunications Conference and Exhibition, Hollywood, FL, Nov. 28-Dec. 1, 1988, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

Various papers on communications for the information age are presented. Among the general topics considered are: telematic services and terminals, satellite communications, telecommunications mangaement network, control of integrated broadband networks, advances in digital radio systems, the intelligent network, broadband networks and services deployment, future switch architectures, performance analysis of computer networks, advances in spread spectrum, optical high-speed LANs, and broadband switching and networks. Also addressed are: multiple access protocols, video coding techniques, modulation and coding, photonic switching, SONET terminals and applications, standards for video coding, digital switching, progress in MANs, mobile and portable radio, software design for improved maintainability, multipath propagation and advanced countermeasure, data communication, network control and management, fiber in the loop, network algorithm and protocols, and advances in computer communications.

Joint Services Electronics Program

DTIC Science & Technology

1992-03-05

Packaging Considerations M. T. Raghunath (Professor Abhiram Ranade) A central issue in massively parallel computation is the design of the interconnection...programs on promising network architectures. Publications: [1] M. T. Raghunath and A. G. Ranade, A Simulation-Based Compari- son of Interconnection Networks...more difficult analog function approximation task. Network Design Issues for Fast Global Communication Professor A. Ranade with M.T. Raghunath A

Neuromorphic device architectures with global connectivity through electrolyte gating

NASA Astrophysics Data System (ADS)

Gkoupidenis, Paschalis; Koutsouras, Dimitrios A.; Malliaras, George G.

2017-05-01

Information processing in the brain takes place in a network of neurons that are connected with each other by an immense number of synapses. At the same time, neurons are immersed in a common electrochemical environment, and global parameters such as concentrations of various hormones regulate the overall network function. This computational paradigm of global regulation, also known as homeoplasticity, has important implications in the overall behaviour of large neural ensembles and is barely addressed in neuromorphic device architectures. Here, we demonstrate the global control of an array of organic devices based on poly(3,4ethylenedioxythiophene):poly(styrene sulf) that are immersed in an electrolyte, a behaviour that resembles homeoplasticity phenomena of the neural environment. We use this effect to produce behaviour that is reminiscent of the coupling between local activity and global oscillations in the biological neural networks. We further show that the electrolyte establishes complex connections between individual devices, and leverage these connections to implement coincidence detection. These results demonstrate that electrolyte gating offers significant advantages for the realization of networks of neuromorphic devices of higher complexity and with minimal hardwired connectivity.

Effective seeding strategy in evolutionary prisoner's dilemma games on online social networks

NASA Astrophysics Data System (ADS)

Xu, Bo; Shi, Huibin; Wang, Jianwei; Huang, Yun

2015-04-01

This paper explores effective seeding strategies in prisoner's dilemma game (PDG) on online social networks, i.e. the optimal strategy to obtain global cooperation with minimum cost. Three distinct seeding strategies are compared by performing computer simulations on real online social network datasets. Our finding suggests that degree centrality seeding outperforms other strategies regardless of the initial payoff setting or network size. Celebrities of online social networks play key roles in preserving cooperation.

GLOBECOM '92 - IEEE Global Telecommunications Conference, Orlando, FL, Dec. 6-9, 1992, Conference Record. Vols. 1-3

NASA Astrophysics Data System (ADS)

Papers are presented on such topics as the wireless data network in PCS, advances in digital mobile networks, ATM switching experiments, broadband applications, network planning, and advances in SONET/SDH implementations. Consideration is also given to gigabit computer networks, techniques for modeling large high-speed networks, coding and modulation, the next-generation lightwave system, signaling systems for broadband ISDN, satellite technologies, and advances in standardization of low-rate signal processing.

Java and its future in biomedical computing.

PubMed Central

Rodgers, R P

1996-01-01

Java, a new object-oriented computing language related to C++, is receiving considerable attention due to its use in creating network-sharable, platform-independent software modules (known as "applets") that can be used with the World Wide Web. The Web has rapidly become the most commonly used information-retrieval tool associated with the global computer network known as the Internet, and Java has the potential to further accelerate the Web's application to medical problems. Java's potentially wide acceptance due to its Web association and its own technical merits also suggests that it may become a popular language for non-Web-based, object-oriented computing. PMID:8880677

Global dynamic optimization approach to predict activation in metabolic pathways.

PubMed

de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

2014-01-06

During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

The Death of Distance and the Rise of the Network Society.

ERIC Educational Resources Information Center

Saba, Farhad

1999-01-01

Discusses the rise of computer networks, telecommuting possibilities, the growing global economy, and possible resulting trends in the population of cities. Implications for higher education are suggested, including the centralization of certain core operations and the decentralization of other services to address the varied needs of students.…

The Rise of China in the International Trade Network: A Community Core Detection Approach

PubMed Central

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995–2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism. PMID:25136895

The rise of China in the International Trade Network: a community core detection approach.

PubMed

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

Laboratory and software applications for clinical trials: the global laboratory environment.

PubMed

Briscoe, Chad

2011-11-01

The Applied Pharmaceutical Software Meeting is held annually. It is sponsored by The Boston Society, a not-for-profit organization that coordinates a series of meetings within the global pharmaceutical industry. The meeting generally focuses on laboratory applications, but in recent years has expanded to include some software applications for clinical trials. The 2011 meeting emphasized the global laboratory environment. Global clinical trials generate massive amounts of data in many locations that must be centralized and processed for efficient analysis. Thus, the meeting had a strong focus on establishing networks and systems for dealing with the computer infrastructure to support such environments. In addition to the globally installed laboratory information management system, electronic laboratory notebook and other traditional laboratory applications, cloud computing is quickly becoming the answer to provide efficient, inexpensive options for managing the large volumes of data and computing power, and thus it served as a central theme for the meeting.

Globalisation and human dignity: the case of the information superhighway.

PubMed

Hamelink, C J

1996-01-01

In 1994, Vice President Al Gore coined the concept of the Information Superhighway during a speech in Buenos Aires in which he proposed the development of a global information infrastructure. The project envisions the incorporation of all existing communication networks into one system, facilitating the globalization of markets. The internet, a decentralized network of computer networks, is one small-scale, existing model of what the superhighway could be, a public space owned by nobody in which communication takes place largely for noncommercial purposes. The internet currently connects 40 million computer users from at least 90 countries. The alternative model for the superhighway is the privately managed global shopping mall, a global interactive marketplace for information, entertainment, and advertising. The author warns, however, that it is not possible to predict the future social impact of such an information superhighway. Decision makers nonetheless push ahead in development, full of confidence in the merit and infallibility of technology. Since one cannot accurately predict the future, it rational to assume that social consequences may both promote and threaten human dignity. The author explains at which level he believes the superhighway threatens the human rights norms of equality, inviolability, and liberty, and discusses what the World Association for Christian Communication can do.

Optimal control strategy for a novel computer virus propagation model on scale-free networks

NASA Astrophysics Data System (ADS)

Zhang, Chunming; Huang, Haitao

2016-06-01

This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.

Peering into the Future of Advertising.

ERIC Educational Resources Information Center

Hsia, H. J.

All areas in mass communications (i.e., newspapers, magazines, television, radio, films, photos, and books) will be transformed because of the increasing sophistication of computer users, the decreasing costs for interactive computer systems, and the global adoption of integrated services digital networks (ISDN). ISDN refer to the digitization of…

Advanced Optical Burst Switched Network Concepts

NASA Astrophysics Data System (ADS)

Nejabati, Reza; Aracil, Javier; Castoldi, Piero; de Leenheer, Marc; Simeonidou, Dimitra; Valcarenghi, Luca; Zervas, Georgios; Wu, Jian

In recent years, as the bandwidth and the speed of networks have increased significantly, a new generation of network-based applications using the concept of distributed computing and collaborative services is emerging (e.g., Grid computing applications). The use of the available fiber and DWDM infrastructure for these applications is a logical choice offering huge amounts of cheap bandwidth and ensuring global reach of computing resources [230]. Currently, there is a great deal of interest in deploying optical circuit (wavelength) switched network infrastructure for distributed computing applications that require long-lived wavelength paths and address the specific needs of a small number of well-known users. Typical users are particle physicists who, due to their international collaborations and experiments, generate enormous amounts of data (Petabytes per year). These users require a network infrastructures that can support processing and analysis of large datasets through globally distributed computing resources [230]. However, providing wavelength granularity bandwidth services is not an efficient and scalable solution for applications and services that address a wider base of user communities with different traffic profiles and connectivity requirements. Examples of such applications may be: scientific collaboration in smaller scale (e.g., bioinformatics, environmental research), distributed virtual laboratories (e.g., remote instrumentation), e-health, national security and defense, personalized learning environments and digital libraries, evolving broadband user services (i.e., high resolution home video editing, real-time rendering, high definition interactive TV). As a specific example, in e-health services and in particular mammography applications due to the size and quantity of images produced by remote mammography, stringent network requirements are necessary. Initial calculations have shown that for 100 patients to be screened remotely, the network would have to securely transport 1.2 GB of data every 30 s [230]. According to the above explanation it is clear that these types of applications need a new network infrastructure and transport technology that makes large amounts of bandwidth at subwavelength granularity, storage, computation, and visualization resources potentially available to a wide user base for specified time durations. As these types of collaborative and network-based applications evolve addressing a wide range and large number of users, it is infeasible to build dedicated networks for each application type or category. Consequently, there should be an adaptive network infrastructure able to support all application types, each with their own access, network, and resource usage patterns. This infrastructure should offer flexible and intelligent network elements and control mechanism able to deploy new applications quickly and efficiently.

Suborbital Telepresence and Over-the-Horizon Networking

NASA Technical Reports Server (NTRS)

Freudinger, Lawrence C.

2007-01-01

A viewgraph presentation describing the suborbital telepresence project utilizing in-flight network computing is shown. The topics include: 1) Motivation; 2) Suborbital Telepresence and Global Test Range; 3) Tropical Composition, Cloud, and Climate Coupling Experiment (TC4); 4) Data Sets for TC4 Real-time Monitoring; 5) TC-4 Notional Architecture; 6) An Application Integration View; 7) Telepresence: Architectural Framework; and 8) Disruption Tolerant Networks.

Topological properties of robust biological and computational networks

PubMed Central

Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

2014-01-01

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

PubMed Central

Stetz, Gabrielle; Verkhivker, Gennady M.

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms. PMID:28095400

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

Education on the Electronic Frontier: Teleapprentices in Globally Distributed Educational Contexts. Interactive Technology Laboratory Report #14.

ERIC Educational Resources Information Center

Levin, James A.; And Others

1987-01-01

The instructional media created by microcomputers interconnected by modems to form long-distance networks present some powerful new opportunities for education. While other uses of computers in education have been built on conventional instructional models of classroom interaction, instructional electronic networks facilitate a wider use of…

Classification of epilepsy types through global network analysis of scalp electroencephalograms

NASA Astrophysics Data System (ADS)

Lee, Uncheol; Kim, Seunghwan; Jung, Ki-Young

2006-04-01

Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.

Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing

NASA Astrophysics Data System (ADS)

Kumar, Suhas; Strachan, John Paul; Williams, R. Stanley

2017-08-01

At present, machine learning systems use simplified neuron models that lack the rich nonlinear phenomena observed in biological systems, which display spatio-temporal cooperative dynamics. There is evidence that neurons operate in a regime called the edge of chaos that may be central to complexity, learning efficiency, adaptability and analogue (non-Boolean) computation in brains. Neural networks have exhibited enhanced computational complexity when operated at the edge of chaos, and networks of chaotic elements have been proposed for solving combinatorial or global optimization problems. Thus, a source of controllable chaotic behaviour that can be incorporated into a neural-inspired circuit may be an essential component of future computational systems. Such chaotic elements have been simulated using elaborate transistor circuits that simulate known equations of chaos, but an experimental realization of chaotic dynamics from a single scalable electronic device has been lacking. Here we describe niobium dioxide (NbO2) Mott memristors each less than 100 nanometres across that exhibit both a nonlinear-transport-driven current-controlled negative differential resistance and a Mott-transition-driven temperature-controlled negative differential resistance. Mott materials have a temperature-dependent metal-insulator transition that acts as an electronic switch, which introduces a history-dependent resistance into the device. We incorporate these memristors into a relaxation oscillator and observe a tunable range of periodic and chaotic self-oscillations. We show that the nonlinear current transport coupled with thermal fluctuations at the nanoscale generates chaotic oscillations. Such memristors could be useful in certain types of neural-inspired computation by introducing a pseudo-random signal that prevents global synchronization and could also assist in finding a global minimum during a constrained search. We specifically demonstrate that incorporating such memristors into the hardware of a Hopfield computing network can greatly improve the efficiency and accuracy of converging to a solution for computationally difficult problems.

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa

PubMed Central

Geisler, Daniel; Borchardt, Viola; Lord, Anton R.; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A.; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Background Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. Methods To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Results Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. Limitations The present results may be limited to the methods applied during preprocessing and network construction. Conclusion We demonstrated anorexia nervosa–related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger. PMID:26252451

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa.

PubMed

Geisler, Daniel; Borchardt, Viola; Lord, Anton R; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. The present results may be limited to the methods applied during preprocessing and network construction. We demonstrated anorexia nervosa-related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger.

South Asia in the Global Electronic Village: Issues and Implications.

ERIC Educational Resources Information Center

Singh, Jagtar

This paper discusses issues related to developments in computer and communication technologies in south Asia. The first section considers the Internet and its impact. Paradigm shifts and globalization are addressed in the second section, including the shifts away from stand alone libraries to library and information networks, ownership to access,…

Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration.

PubMed

Xia, Youshen; Sun, Changyin; Zheng, Wei Xing

2012-05-01

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

PubMed

Schmidt, Helmut; Petkov, George; Richardson, Mark P; Terry, John R

2014-11-01

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.

GLOBECOM '85 - Global Telecommunications Conference, New Orleans, LA, December 2-5, 1985, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

Various papers on global telecommunications are presented. The general topics addressed include: multiservice integration with optical fibers, multicompany owned telecommunication networks, softworks quality and reliability, advanced on-board processing, impact of new services and systems on operations and maintenance, analytical studies of protocols for data communication networks, topics in packet radio networking, CCITT No. 7 to support new services, document processing and communication, antenna technology and system aspects in satellite communications. Also considered are: communication systems modelling methodology, experimental integrated local area voice/data nets, spread spectrum communications, motion video at the DS-0 rate, optical and data communications, intelligent work stations, switch performance analysis, novel radio communication systems, wireless local networks, ISDN services, LAN communication protocols, user-system interface, radio propagation and performance, mobile satellite system, software for computer networks, VLSI for ISDN terminals, quality management, man-machine interfaces in switching, and local area network performance.

The Quake-Catcher Network: An Innovative Community-Based Seismic Network

NASA Astrophysics Data System (ADS)

Saltzman, J.; Cochran, E. S.; Lawrence, J. F.; Christensen, C. M.

2009-12-01

The Quake-Catcher Network (QCN) is a volunteer computing seismic network that engages citizen scientists, teachers, and museums to participate in the detection of earthquakes. In less than two years, the network has grown to over 1000 participants globally and continues to expand. QCN utilizes Micro-Electro-Mechanical System (MEMS) accelerometers, in laptops and external to desktop computers, to detect moderate to large earthquakes. One goal of the network is to involve K-12 classrooms and museums by providing sensors and software to introduce participants to seismology and community-based scientific data collection. The Quake-Catcher Network provides a unique opportunity to engage participants directly in the scientific process, through hands-on activities that link activities and outcomes to their daily lives. Partnerships with teachers and museum staff are critical to growth of the Quake Catcher Network. Each participating institution receives a MEMS accelerometer to connect, via USB, to a computer that can be used for hands-on activities and to record earthquakes through a distributed computing system. We developed interactive software (QCNLive) that allows participants to view sensor readings in real time. Participants can also record earthquakes and download earthquake data that was collected by their sensor or other QCN sensors. The Quake-Catcher Network combines research and outreach to improve seismic networks and increase awareness and participation in science-based research in K-12 schools.

The role of the airline transportation network in the prediction and predictability of global epidemics.

PubMed

Colizza, Vittoria; Barrat, Alain; Barthélemy, Marc; Vespignani, Alessandro

2006-02-14

The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.

Citizen ’Cyber’ Airmen: Maintaining Ready and Proficient Cyberspace Operators in the Reserve Components

DTIC Science & Technology

2016-02-01

Internet service providers and global supply chains, over which DOD has no direct authority to mitigate risk effectively. The global technology supply...cyberspace. CO are composed of the military, intelligence, and ordinary business operations of DOD in and through cyberspace. Cyberspace, while a global ...infrastructures, including the Internet , telecommunications networks, computer systems, and embedded processors and controllers, and the content that flows across

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

Integrative network alignment reveals large regions of global network similarity in yeast and human.

PubMed

Kuchaiev, Oleksii; Przulj, Natasa

2011-05-15

High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similarity measures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein-protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7% of proteins in the baker's yeast high-confidence PPI network participate in such a subnetwork that is fully contained in the human high-confidence PPI network. This is the first demonstration that species as diverse as yeast and human contain so large, continuous regions of global network similarity. We apply MI-GRAAL's alignments to predict functions of un-annotated proteins in yeast, human and bacteria validating our predictions in the literature. Furthermore, using network alignment scores for PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship. This is the first time that phylogeny is exactly reconstructed from purely topological alignments of PPI networks. Supplementary files and MI-GRAAL executables: http://bio-nets.doc.ic.ac.uk/MI-GRAAL/.

Study on Global GIS architecture and its key technologies

NASA Astrophysics Data System (ADS)

Cheng, Chengqi; Guan, Li; Lv, Xuefeng

2009-09-01

Global GIS (G2IS) is a system, which supports the huge data process and the global direct manipulation on global grid based on spheroid or ellipsoid surface. Based on global subdivision grid (GSG), Global GIS architecture is presented in this paper, taking advantage of computer cluster theory, the space-time integration technology and the virtual reality technology. Global GIS system architecture is composed of five layers, including data storage layer, data representation layer, network and cluster layer, data management layer and data application layer. Thereinto, it is designed that functions of four-level protocol framework and three-layer data management pattern of Global GIS based on organization, management and publication of spatial information in this architecture. Three kinds of core supportive technologies, which are computer cluster theory, the space-time integration technology and the virtual reality technology, and its application pattern in the Global GIS are introduced in detail. The primary ideas of Global GIS in this paper will be an important development tendency of GIS.

Study on Global GIS architecture and its key technologies

NASA Astrophysics Data System (ADS)

Cheng, Chengqi; Guan, Li; Lv, Xuefeng

2010-11-01

Global GIS (G2IS) is a system, which supports the huge data process and the global direct manipulation on global grid based on spheroid or ellipsoid surface. Based on global subdivision grid (GSG), Global GIS architecture is presented in this paper, taking advantage of computer cluster theory, the space-time integration technology and the virtual reality technology. Global GIS system architecture is composed of five layers, including data storage layer, data representation layer, network and cluster layer, data management layer and data application layer. Thereinto, it is designed that functions of four-level protocol framework and three-layer data management pattern of Global GIS based on organization, management and publication of spatial information in this architecture. Three kinds of core supportive technologies, which are computer cluster theory, the space-time integration technology and the virtual reality technology, and its application pattern in the Global GIS are introduced in detail. The primary ideas of Global GIS in this paper will be an important development tendency of GIS.

A Living Library: New Model for Global Electronic Interactivity and Networking in the Garden.

ERIC Educational Resources Information Center

Sherk, Bonnie

1995-01-01

Describes the Living Library, an idea to create a network of international cultural parks in different cities of the world using new communications technologies on-line in a garden setting, bringing the humanities, sciences, and social sciences to life through plants, visual and performed artworks, lectures, and computer and on-line satellite…

The Evolving Roles of Language Teachers: Trained Coders, Local Researchers, Global Citizens

ERIC Educational Resources Information Center

Godwin-Jones, Robert

2015-01-01

Language teachers are working in a world which has changed in the past decades in fundamentally disruptive ways, through profound changes in the role that networked computers play in everyday life and through the social and demographic shifts brought on by an increasingly globalized society, bringing together more than ever before people from…

Condition monitoring of 3G cellular networks through competitive neural models.

PubMed

Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

2005-09-01

We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

Network Computing Infrastructure to Share Tools and Data in Global Nuclear Energy Partnership

NASA Astrophysics Data System (ADS)

Kim, Guehee; Suzuki, Yoshio; Teshima, Naoya

CCSE/JAEA (Center for Computational Science and e-Systems/Japan Atomic Energy Agency) integrated a prototype system of a network computing infrastructure for sharing tools and data to support the U.S. and Japan collaboration in GNEP (Global Nuclear Energy Partnership). We focused on three technical issues to apply our information process infrastructure, which are accessibility, security, and usability. In designing the prototype system, we integrated and improved both network and Web technologies. For the accessibility issue, we adopted SSL-VPN (Security Socket Layer-Virtual Private Network) technology for the access beyond firewalls. For the security issue, we developed an authentication gateway based on the PKI (Public Key Infrastructure) authentication mechanism to strengthen the security. Also, we set fine access control policy to shared tools and data and used shared key based encryption method to protect tools and data against leakage to third parties. For the usability issue, we chose Web browsers as user interface and developed Web application to provide functions to support sharing tools and data. By using WebDAV (Web-based Distributed Authoring and Versioning) function, users can manipulate shared tools and data through the Windows-like folder environment. We implemented the prototype system in Grid infrastructure for atomic energy research: AEGIS (Atomic Energy Grid Infrastructure) developed by CCSE/JAEA. The prototype system was applied for the trial use in the first period of GNEP.

Global Seismic Imaging Based on Adjoint Tomography

NASA Astrophysics Data System (ADS)

Bozdag, E.; Lefebvre, M.; Lei, W.; Peter, D. B.; Smith, J. A.; Zhu, H.; Komatitsch, D.; Tromp, J.

2013-12-01

Our aim is to perform adjoint tomography at the scale of globe to image the entire planet. We have started elastic inversions with a global data set of 253 CMT earthquakes with moment magnitudes in the range 5.8 ≤ Mw ≤ 7 and used GSN stations as well as some local networks such as USArray, European stations, etc. Using an iterative pre-conditioned conjugate gradient scheme, we initially set the aim to obtain a global crustal and mantle model with confined transverse isotropy in the upper mantle. Global adjoint tomography has so far remained a challenge mainly due to computational limitations. Recent improvements in our 3D solvers (e.g., a GPU version) and access to high-performance computational centers (e.g., ORNL's Cray XK7 "Titan" system) now enable us to perform iterations with higher-resolution (T > 9 s) and longer-duration (200 min) simulations to accommodate high-frequency body waves and major-arc surface waves, respectively, which help improve data coverage. The remaining challenge is the heavy I/O traffic caused by the numerous files generated during the forward/adjoint simulations and the pre- and post-processing stages of our workflow. We improve the global adjoint tomography workflow by adopting the ADIOS file format for our seismic data as well as models, kernels, etc., to improve efficiency on high-performance clusters. Our ultimate aim is to use data from all available networks and earthquakes within the magnitude range of our interest (5.5 ≤ Mw ≤ 7) which requires a solid framework to manage big data in our global adjoint tomography workflow. We discuss the current status and future of global adjoint tomography based on our initial results as well as practical issues such as handling big data in inversions and on high-performance computing systems.

On Increasing Network Lifetime in Body Area Networks Using Global Routing with Energy Consumption Balancing

PubMed Central

Tsouri, Gill R.; Prieto, Alvaro; Argade, Nikhil

2012-01-01

Global routing protocols in wireless body area networks are considered. Global routing is augmented with a novel link cost function designed to balance energy consumption across the network. The result is a substantial increase in network lifetime at the expense of a marginal increase in energy per bit. Network maintenance requirements are reduced as well, since balancing energy consumption means all batteries need to be serviced at the same time and less frequently. The proposed routing protocol is evaluated using a hardware experimental setup comprising multiple nodes and an access point. The setup is used to assess network architectures, including an on-body access point and an off-body access point with varying number of antennas. Real-time experiments are conducted in indoor environments to assess performance gains. In addition, the setup is used to record channel attenuation data which are then processed in extensive computer simulations providing insight on the effect of protocol parameters on performance. Results demonstrate efficient balancing of energy consumption across all nodes, an average increase of up to 40% in network lifetime corresponding to a modest average increase of 0.4 dB in energy per bit, and a cutoff effect on required transmission power to achieve reliable connectivity. PMID:23201987

On increasing network lifetime in body area networks using global routing with energy consumption balancing.

PubMed

Tsouri, Gill R; Prieto, Alvaro; Argade, Nikhil

2012-09-26

Global routing protocols in wireless body area networks are considered. Global routing is augmented with a novel link cost function designed to balance energy consumption across the network. The result is a substantial increase in network lifetime at the expense of a marginal increase in energy per bit. Network maintenance requirements are reduced as well, since balancing energy consumption means all batteries need to be serviced at the same time and less frequently. The proposed routing protocol is evaluated using a hardware experimental setup comprising multiple nodes and an access point. The setup is used to assess network architectures, including an on-body access point and an off-body access point with varying number of antennas. Real-time experiments are conducted in indoor environments to assess performance gains. In addition, the setup is used to record channel attenuation data which are then processed in extensive computer simulations providing insight on the effect of protocol parameters on performance. Results demonstrate efficient balancing of energy consumption across all nodes, an average increase of up to 40% in network lifetime corresponding to a modest average increase of 0.4 dB in energy per bit, and a cutoff effect on required transmission power to achieve reliable connectivity.

A high-resolution physically-based global flood hazard map

NASA Astrophysics Data System (ADS)

Kaheil, Y.; Begnudelli, L.; McCollum, J.

2016-12-01

We present the results from a physically-based global flood hazard model. The model uses a physically-based hydrologic model to simulate river discharges, and 2D hydrodynamic model to simulate inundation. The model is set up such that it allows the application of large-scale flood hazard through efficient use of parallel computing. For hydrology, we use the Hillslope River Routing (HRR) model. HRR accounts for surface hydrology using Green-Ampt parameterization. The model is calibrated against observed discharge data from the Global Runoff Data Centre (GRDC) network, among other publicly-available datasets. The parallel-computing framework takes advantage of the river network structure to minimize cross-processor messages, and thus significantly increases computational efficiency. For inundation, we implemented a computationally-efficient 2D finite-volume model with wetting/drying. The approach consists of simulating flood along the river network by forcing the hydraulic model with the streamflow hydrographs simulated by HRR, and scaled up to certain return levels, e.g. 100 years. The model is distributed such that each available processor takes the next simulation. Given an approximate criterion, the simulations are ordered from most-demanding to least-demanding to ensure that all processors finalize almost simultaneously. Upon completing all simulations, the maximum envelope of flood depth is taken to generate the final map. The model is applied globally, with selected results shown from different continents and regions. The maps shown depict flood depth and extent at different return periods. These maps, which are currently available at 3 arc-sec resolution ( 90m) can be made available at higher resolutions where high resolution DEMs are available. The maps can be utilized by flood risk managers at the national, regional, and even local levels to further understand their flood risk exposure, exercise certain measures of mitigation, and/or transfer the residual risk financially through flood insurance programs.

High-speed quantum networking by ship

NASA Astrophysics Data System (ADS)

Devitt, Simon J.; Greentree, Andrew D.; Stephens, Ashley M.; van Meter, Rodney

2016-11-01

Networked entanglement is an essential component for a plethora of quantum computation and communication protocols. Direct transmission of quantum signals over long distances is prevented by fibre attenuation and the no-cloning theorem, motivating the development of quantum repeaters, designed to purify entanglement, extending its range. Quantum repeaters have been demonstrated over short distances, but error-corrected, global repeater networks with high bandwidth require new technology. Here we show that error corrected quantum memories installed in cargo containers and carried by ship can provide a exible connection between local networks, enabling low-latency, high-fidelity quantum communication across global distances at higher bandwidths than previously proposed. With demonstrations of technology with sufficient fidelity to enable topological error-correction, implementation of the quantum memories is within reach, and bandwidth increases with improvements in fabrication. Our approach to quantum networking avoids technological restrictions of repeater deployment, providing an alternate path to a worldwide Quantum Internet.

High-speed quantum networking by ship

PubMed Central

Devitt, Simon J.; Greentree, Andrew D.; Stephens, Ashley M.; Van Meter, Rodney

2016-01-01

Networked entanglement is an essential component for a plethora of quantum computation and communication protocols. Direct transmission of quantum signals over long distances is prevented by fibre attenuation and the no-cloning theorem, motivating the development of quantum repeaters, designed to purify entanglement, extending its range. Quantum repeaters have been demonstrated over short distances, but error-corrected, global repeater networks with high bandwidth require new technology. Here we show that error corrected quantum memories installed in cargo containers and carried by ship can provide a exible connection between local networks, enabling low-latency, high-fidelity quantum communication across global distances at higher bandwidths than previously proposed. With demonstrations of technology with sufficient fidelity to enable topological error-correction, implementation of the quantum memories is within reach, and bandwidth increases with improvements in fabrication. Our approach to quantum networking avoids technological restrictions of repeater deployment, providing an alternate path to a worldwide Quantum Internet. PMID:27805001

High-speed quantum networking by ship.

PubMed

Devitt, Simon J; Greentree, Andrew D; Stephens, Ashley M; Van Meter, Rodney

2016-11-02

Networked entanglement is an essential component for a plethora of quantum computation and communication protocols. Direct transmission of quantum signals over long distances is prevented by fibre attenuation and the no-cloning theorem, motivating the development of quantum repeaters, designed to purify entanglement, extending its range. Quantum repeaters have been demonstrated over short distances, but error-corrected, global repeater networks with high bandwidth require new technology. Here we show that error corrected quantum memories installed in cargo containers and carried by ship can provide a exible connection between local networks, enabling low-latency, high-fidelity quantum communication across global distances at higher bandwidths than previously proposed. With demonstrations of technology with sufficient fidelity to enable topological error-correction, implementation of the quantum memories is within reach, and bandwidth increases with improvements in fabrication. Our approach to quantum networking avoids technological restrictions of repeater deployment, providing an alternate path to a worldwide Quantum Internet.

Rigorous GNSS network solutions of unlimited size

NASA Astrophysics Data System (ADS)

Boomkamp, H.; Iag Working Group 1. 1. 1

2010-12-01

The session description states that rigorous estimation processes for millions of parameters are computationally impossible. A more accurate observation would be that such solutions exceed the capacity of current Analysis Centres by several orders of magnitude, as was already discussed during the IGS Workshop of 2004. We can however make processing elements that are smaller and simpler than conventional Analysis Centres, until we have a “centre” that can be replicated in arbitrary amounts, at zero cost. In practice this means that the processing element is reduced to a single, automated computer application that can run anywhere. These analysis elements are connected via the internet into a scalable grid computing scheme that can handle GNSS networks of any size. The approach is not fundamentally different from current combination solutions among a network of Analysis Centres, but refines the granularity of the network elements in order to reduce system complexity and eliminate cost. The Dancer project of IAG Working Group 1 has developed a JXTA peer-to-peer application to this purpose. Dancer splits a conventional batch least squares process into as many interacting subtasks as there are receivers. Each task can then run on a local PC of a permanent GNSS site, or anywhere else. All Dancer instances find the same global solution for satellite orbits, clocks and Earth rotation parameters via an efficient vector averaging method called square dancing. The hardware requirements for a single Dancer process do not exceed those of e.g. current mobile phone applications, so that future generations of GNSS receivers may be able to run such a task as an embedded process. This leads to the concept of “smart receivers” that no longer require any post-processing infrastructure. Instead they need an internet connection to join thousands of other smart receivers in a global network solution. The key algorithms, project status and further deployment of the Dancer system will be presented. A brief summary is also given of two follow-on projects, called Digger (distributed computing for global geodetic reprocessing) and Dart (Dancer real-time). For more details, see www.GPSdancer.com.

Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.

PubMed

Baek, K; Morris, L S; Kundu, P; Voon, V

2017-03-01

The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

Telecommunications and Information-Systems Standardization--Is America Ready? CRS Report for Congress. Sci-Tech Alert. 87-458 SPR.

ERIC Educational Resources Information Center

Hack, David

This report on telephone networks and computer networks in a global context focuses on the processes and organizations through which the standards that make this possible are set. The first of five major sections presents descriptions of the standardization process, including discussions of the various kinds of standards, advantages and…

Fluidity in the Networked Society--Self-Initiated learning as a Digital Literacy Competence

ERIC Educational Resources Information Center

Levinsen, Karin Tweddell

2011-01-01

In the globalized economies e-permeation has become a basic condition in our everyday lives. ICT can no longer be understood solely as artefacts and tools and computer-related literacy are no longer restricted to the ability to operate digital tools for specific purposes. The network society, and therefore also eLearning are characterized by…

Internet Protocol Over Telemetry Testing for Earth Science Capability Demo Summary

NASA Technical Reports Server (NTRS)

Franz, Russ; Pestana, Mark; Bessent, Shedrick; Hang, Richard; Ng, Howard

2006-01-01

The development and flight tests described here focused on utilizing existing pulse code modulation (PCM) telemetry equipment to enable on-vehicle networks of instruments and computers to be a simple extension of the ground station network. This capability is envisioned as a necessary component of a global range that supports test and development of manned and unmanned airborne vehicles.

Separating figure from ground with a parallel network.

PubMed

Kienker, P K; Sejnowski, T J; Hinton, G E; Schumacher, L E

1986-01-01

The differentiation of figure from ground plays an important role in the perceptual organization of visual stimuli. The rapidity with which we can discriminate the inside from the outside of a figure suggests that at least this step in the process may be performed in visual cortex by a large number of neurons in several different areas working together in parallel. We have attempted to simulate this collective computation by designing a network of simple processing units that receives two types of information: bottom-up input from the image containing the outlines of a figure, which may be incomplete, and a top-down attentional input that biases one part of the image to be the inside of the figure. No presegmentation of the image was assumed. Two methods for performing the computation were explored: gradient descent, which seeks locally optimal states, and simulated annealing, which attempts to find globally optimal states by introducing noise into the computation. For complete outlines, gradient descent was faster, but the range of input parameters leading to successful performance was very narrow. In contrast, simulated annealing was more robust: it worked over a wider range of attention parameters and a wider range of outlines, including incomplete ones. Our network model is too simplified to serve as a model of human performance, but it does demonstrate that one global property of outlines can be computed through local interactions in a parallel network. Some features of the model, such as the role of noise in escaping from nonglobal optima, may generalize to more realistic models.

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.

An efficient annealing in Boltzmann machine in Hopfield neural network

NASA Astrophysics Data System (ADS)

Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

2012-09-01

This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

Social networks and spreading of epidemics

NASA Astrophysics Data System (ADS)

Trimper, Steffen; Zheng, Dafang; Brandau, Marian

2004-05-01

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

A global spacecraft control network for spacecraft autonomy research

NASA Technical Reports Server (NTRS)

Kitts, Christopher A.

1996-01-01

The development and implementation of the Automated Space System Experimental Testbed (ASSET) space operations and control network, is reported on. This network will serve as a command and control architecture for spacecraft operations and will offer a real testbed for the application and validation of advanced autonomous spacecraft operations strategies. The proposed network will initially consist of globally distributed amateur radio ground stations at locations throughout North America and Europe. These stations will be linked via Internet to various control centers. The Stanford (CA) control center will be capable of human and computer based decision making for the coordination of user experiments, resource scheduling and fault management. The project's system architecture is described together with its proposed use as a command and control system, its value as a testbed for spacecraft autonomy research, and its current implementation.

Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays.

PubMed

Hu, Jin; Wang, Jun

2015-06-01

In recent years, complex-valued recurrent neural networks have been developed and analysed in-depth in view of that they have good modelling performance for some applications involving complex-valued elements. In implementing continuous-time dynamical systems for simulation or computational purposes, it is quite necessary to utilize a discrete-time model which is an analogue of the continuous-time system. In this paper, we analyse a discrete-time complex-valued recurrent neural network model and obtain the sufficient conditions on its global exponential periodicity and exponential stability. Simulation results of several numerical examples are delineated to illustrate the theoretical results and an application on associative memory is also given. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults.

PubMed

Ten Kate, Mara; Visser, Pieter Jelle; Bakardjian, Hovagim; Barkhof, Frederik; Sikkes, Sietske A M; van der Flier, Wiesje M; Scheltens, Philip; Hampel, Harald; Habert, Marie-Odile; Dubois, Bruno; Tijms, Betty M

2018-01-01

The accumulation of amyloid plaques is one of the earliest pathological changes in Alzheimer's disease (AD) and may occur 20 years before the onset of symptoms. Examining associations between amyloid pathology and other early brain changes is critical for understanding the pathophysiological underpinnings of AD. Alterations in gray matter networks might already start at early preclinical stages of AD. In this study, we examined the regional relationship between amyloid aggregation measured with positron emission tomography (PET) and gray matter network measures in elderly subjects with subjective memory complaints. Single-subject gray matter networks were extracted from T1-weigthed structural MRI in cognitively normal subjects ( n = 318, mean age 76.1 ± 3.5, 64% female, 28% amyloid positive). Degree, clustering, path length and small world properties were computed. Global and regional amyloid load was determined using [ 18 F]-Florbetapir PET. Associations between standardized uptake value ratio (SUVr) values and network measures were examined using linear regression models. We found that higher global SUVr was associated with lower clustering ( β = -0.12, p < 0.05), and small world values ( β = -0.16, p < 0.01). Associations were most prominent in orbito- and dorsolateral frontal and parieto-occipital regions. Local SUVr values showed less anatomical variability and did not convey additional information beyond global amyloid burden. In conclusion, we found that in cognitively normal elderly subjects, increased global amyloid pathology is associated with alterations in gray matter networks that are indicative of incipient network breakdown towards AD dementia.

Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study.

PubMed

Jiang, Wenyu; Li, Jianping; Chen, Xuemei; Ye, Wei; Zheng, Jinou

2017-01-01

Previous studies have shown that temporal lobe epilepsy (TLE) involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE) patients and 19 healthy controls. Global network properties were computed by graph theoretical analysis, and correlations were assessed between global network properties and alertness. The results from these analyses showed that rTLE patients exhibit abnormal small-world attributes in structural and functional networks. Structural networks shifted toward more regular attributes, but functional networks trended toward more random attributes. After controlling for the influence of the disease duration, negative correlations were found between alertness, small-worldness, and the cluster coefficient. However, alertness did not correlate with either the characteristic path length or global efficiency in rTLE patients. Our findings show that disruptions of the topological construction of brain structural and functional networks as well as small-world property bias are associated with deficits in alertness in rTLE patients. These data suggest that reorganization of brain networks develops as a mechanism to compensate for altered structural and functional brain function during disease progression.

A character network study of two Sci-Fi TV series

NASA Astrophysics Data System (ADS)

Tan, M. S. A.; Ujum, E. A.; Ratnavelu, K.

2014-03-01

This work is an analysis of the character networks in two science fiction television series: Stargate and Star Trek. These networks are constructed on the basis of scene co-occurrence between characters to indicate the presence of a connection. Global network structure measures such as the average path length, graph density, network diameter, average degree, median degree, maximum degree, and average clustering coefficient are computed as well as individual node centrality scores. The two fictional networks constructed are found to be quite similar in structure which is astonishing given that Stargate only ran for 18 years in comparison to the 48 years for Star Trek.

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Networking Cyberinfrastructure Resources to Support Global, Cross-disciplinary Science

NASA Astrophysics Data System (ADS)

Lehnert, K.; Ramamurthy, M. K.

2016-12-01

Geosciences are globally connected by nature and the grand challenge problems like climate change, ocean circulations, seasonal predictions, impact of volcanic eruptions, etc. all transcend both disciplinary and geographic boundaries, requiring cross-disciplinary and international partnerships. Cross-disciplinary and international collaborations are also needed to unleash the power of cyber- (or e-) infrastructure (CI) by networking globally distributed, multi-disciplinary data, software, and computing resources to accelerate new scientific insights and discoveries. While the promises of a global and cross-disciplinary CI are exhilarating and real, a range of technical, organizational, and social challenges needs to be overcome in order to achieve alignment and linking of operational data systems, software tools, and computing facilities. New modes of collaboration require agreement on and governance of technical standards and best practices, and funding for necessary modifications. This presentation will contribute the perspective of domain-specific data facilities to the discussion of cross-disciplinary and international collaboration in CI development and deployment, in particular those of IEDA (Interdisciplinary Earth Data Alliance) serving the solid Earth sciences and Unidata serving atmospheric sciences. Both facilities are closely involved with the US NSF EarthCube program that aims to network and augment existing Geoscience CI capabilities "to make disciplinary boundaries permeable, nurture and facilitate knowledge sharing, …, and enhance collaborative pursuit of cross-disciplinary research" (EarthCube Strategic Vision), while also collaborating internationally to network domain-specific and cross-disciplinary CI resources. These collaborations are driven by the substantial benefits to the science community, but create challenges, when operational and funding constraints need to be balanced with adjustments to new joint data curation practices and interoperability standards.

Medical image analysis with artificial neural networks.

PubMed

Jiang, J; Trundle, P; Ren, J

2010-12-01

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

PubMed

Hébert-Dufresne, Laurent; Grochow, Joshua A; Allard, Antoine

2016-08-18

We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks

PubMed Central

Chevalier, Michael; Venturelli, Ophelia; El-Samad, Hana

2015-01-01

Stochastic fluctuations in signaling and gene expression limit the ability of cells to sense the state of their environment, transfer this information along cellular pathways, and respond to it with high precision. Mutual information is now often used to quantify the fidelity with which information is transmitted along a cellular pathway. Mutual information calculations from experimental data have mostly generated low values, suggesting that cells might have relatively low signal transmission fidelity. In this work, we demonstrate that mutual information calculations might be artificially lowered by cell-to-cell variability in both initial conditions and slowly fluctuating global factors across the population. We carry out our analysis computationally using a simple signaling pathway and demonstrate that in the presence of slow global fluctuations, every cell might have its own high information transmission capacity but that population averaging underestimates this value. We also construct a simple synthetic transcriptional network and demonstrate using experimental measurements coupled to computational modeling that its operation is dominated by slow global variability, and hence that its mutual information is underestimated by a population averaged calculation. PMID:26484538

An approach for heterogeneous and loosely coupled geospatial data distributed computing

NASA Astrophysics Data System (ADS)

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

2010-07-01

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

Army AL&T, October-December 2008

DTIC Science & Technology

2008-12-01

during the WIN-T technology demonstration Nov. 8, 2007, at Naval Air Engineering Station , Lakehurst, NJ. (U.S. Army photo by Russ Messeroll.) 16 OCTOBER...worldwide communications architecture, enabling connectivity from the global backbone to regional networks to posts/camps/ stations , and, lastly, to...Force Tracker. • Tacticomp™ wireless and Global Positioning System(GPS)-enabled hand-held computer. • One Station Remote Video Terminal. • Counter

Computational Characterization of Type I collagen-based Extra-cellular Matrix

NASA Astrophysics Data System (ADS)

Liang, Long; Jones, Christopher Allen Rucksack; Lin, Daniel; Jiao, Yang; Sun, Bo

2015-03-01

A model of extracellular matrix (ECM) of collagen fibers has been built, in which cells could communicate with distant partners via fiber-mediated long-range-transmitted stress states. The ECM is modeled as a spring-like fiber network derived from skeletonized confocal microscopy data. Different local and global perturbations have been performed on the network, each followed by an optimized global Monte-Carlo (MC) energy minimization leading to the deformed network in response to the perturbations. In the optimization, a highly efficient local energy update procedure is employed and force-directed MC moves are used, which results in a convergence to the energy minimum state 20 times faster than the commonly used random displacement trial moves in MC. Further analysis and visualization of the distribution and correlation of the resulting force network reveal that local perturbations can give rise to global impacts: the force chains formed with a linear extent much further than the characteristic length scale associated with the perturbation sites and average fiber length. This behavior provides a strong evidence for our hypothesis of fiber-mediated long-range force transmission in ECM networks and the resulting long-range cell-cell mechanical signaling. ASU Seed Grant.

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

PubMed Central

Wang, Jin-Hui; Zuo, Xi-Nian; Gohel, Suril; Milham, Michael P.; Biswal, Bharat B.; He, Yong

2011-01-01

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest. PMID:21818285

A Computational Approach to Estimate Interorgan Metabolic Transport in a Mammal

PubMed Central

Cui, Xiao; Geffers, Lars; Eichele, Gregor; Yan, Jun

2014-01-01

In multicellular organisms metabolism is distributed across different organs, each of which has specific requirements to perform its own specialized task. But different organs also have to support the metabolic homeostasis of the organism as a whole by interorgan metabolite transport. Recent studies have successfully reconstructed global metabolic networks in tissues and cell types and attempts have been made to connect organs with interorgan metabolite transport. Instead of these complicated approaches to reconstruct global metabolic networks, we proposed in this study a novel approach to study interorgan metabolite transport focusing on transport processes mediated by solute carrier (Slc) transporters and their couplings to cognate enzymatic reactions. We developed a computational approach to identify and score potential interorgan metabolite transports based on the integration of metabolism and transports in different organs in the adult mouse from quantitative gene expression data. This allowed us to computationally estimate the connectivity between 17 mouse organs via metabolite transport. Finally, by applying our method to circadian metabolism, we showed that our approach can shed new light on the current understanding of interorgan metabolite transport at a whole-body level in mammals. PMID:24971892

From the physics of interacting polymers to optimizing routes on the London Underground

PubMed Central

Yeung, Chi Ho; Saad, David; Wong, K. Y. Michael

2013-01-01

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise. PMID:23898198

From the physics of interacting polymers to optimizing routes on the London Underground.

PubMed

Yeung, Chi Ho; Saad, David; Wong, K Y Michael

2013-08-20

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise.

The interactive use of networking multimedia--innovative education resource for professionals and patients.

PubMed

Matthies, H K; Walter, G F; Brandis, A; Stan, A C; Ammann, A; von Jan, U; Porth, A J

1999-01-01

The combination of new and rapidly developing interactive multimedia computers and applications with electronic networks will require a restructuring of our traditional approach to strategic planning and organizational structure. Worldwide telecommunication networks (using satellites, cable) are now facilitating the global pooling of healthcare information and medical knowledge independent of location. The development of multimedia information and communication systems demands cooperative working teams of authors, who are able to master several areas of medical knowledge as well as the presentation of these in different multimedia forms. The assemblage of telematics and services offers a base for multimedia applications, for example teleteaching, telelearning, telepublishing, teleconsulting, teleconferencing, telemedicine etc. The expansion of the internet will also lead to the formation of interdisciplinary "Global Education Networks". The theory and practice of education are undergoing dramatic changes. Lifelong learning and adaptation of medical practice to new knowledge and new techniques will be even more important in the future.

Stimulation-Based Control of Dynamic Brain Networks

PubMed Central

Pasqualetti, Fabio; Gu, Shi; Cieslak, Matthew

2016-01-01

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement. PMID:27611328

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.

Harmony in the small-world

NASA Astrophysics Data System (ADS)

Marchiori, Massimo; Latora, Vito

2000-10-01

The small-world phenomenon, popularly known as six degrees of separation, has been mathematically formalized by Watts and Strogatz in a study of the topological properties of a network. Small-world networks are defined in terms of two quantities: they have a high clustering coefficient C like regular lattices and a short characteristic path length L typical of random networks. Physical distances are of fundamental importance in applications to real cases; nevertheless, this basic ingredient is missing in the original formulation. Here, we introduce a new concept, the connectivity length D, that gives harmony to the whole theory. D can be evaluated on a global and on a local scale and plays in turn the role of L and 1/ C. Moreover, it can be computed for any metrical network and not only for the topological cases. D has a precise meaning in terms of information propagation and describes in a unified way, both the structural and the dynamical aspects of a network: small-worlds are defined by a small global and local D, i.e., by a high efficiency in propagating information both on a local and global scale. The neural system of the nematode C. elegans, the collaboration graph of film actors, and the oldest US subway system, can now be studied also as metrical networks and are shown to be small-worlds.

Networked Virtual Organizations: A Chance for Small and Medium Sized Enterprises on Global Markets

NASA Astrophysics Data System (ADS)

Cellary, Wojciech

Networked Virtual Organizations (NVOs) are a right answer to challenges of globalized, diversified, and dynamic contemporary economy. NVOs need more than e-trade and outsourcing, namely, they need out-tasking and e-collaboration. To out-task, but retain control on the way a task is performed by an external partner, two integrations are required: (1) integration of computer management systems of enterprises cooperating within an NVO; and (2) integration of cooperating representatives of NVO member enterprises into a virtual team. NVOs provide a particular chance to Small and Medium size Enterprises (SMEs) to find their place on global markets and to play a significant role on them. Requirements for SMEs to be able to successfully join an NVO are analyzed in the paper.

Unified Alignment of Protein-Protein Interaction Networks.

PubMed

Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

2017-04-19

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

NASA Astrophysics Data System (ADS)

Kabbara, A.; Eid, H.; El Falou, W.; Khalil, M.; Wendling, F.; Hassan, M.

2018-04-01

Objective. Emerging evidence shows that cognitive deficits in Alzheimer’s disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients’ functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Reduced integration and improved segregation of functional brain networks in Alzheimer's disease.

PubMed

Kabbara, A; Eid, H; El Falou, W; Khalil, M; Wendling, F; Hassan, M

2018-04-01

Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients' functional brain networks and their cognitive scores. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

PubMed Central

Creixell, Pau; Schoof, Erwin M.; Simpson, Craig D.; Longden, James; Miller, Chad J.; Lou, Hua Jane; Perryman, Lara; Cox, Thomas R.; Zivanovic, Nevena; Palmeri, Antonio; Wesolowska-Andersen, Agata; Helmer-Citterich, Manuela; Ferkinghoff-Borg, Jesper; Itamochi, Hiroaki; Bodenmiller, Bernd; Erler, Janine T.; Turk, Benjamin E.; Linding, Rune

2015-01-01

Summary Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks. PMID:26388441

Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators.

PubMed

Campbell, S; Wang, D

1996-01-01

A network of Wilson-Cowan (WC) oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a 2D matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piecewise linear approximation to the WC oscillator) synchronizes, and we present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections that preserve spatial relationships among components and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.

GIANT API: an application programming interface for functional genomics

PubMed Central

Roberts, Andrew M.; Wong, Aaron K.; Fisk, Ian; Troyanskaya, Olga G.

2016-01-01

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu. PMID:27098035

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.

Distributed computing system with dual independent communications paths between computers and employing split tokens

NASA Technical Reports Server (NTRS)

Rasmussen, Robert D. (Inventor); Manning, Robert M. (Inventor); Lewis, Blair F. (Inventor); Bolotin, Gary S. (Inventor); Ward, Richard S. (Inventor)

1990-01-01

This is a distributed computing system providing flexible fault tolerance; ease of software design and concurrency specification; and dynamic balance of the loads. The system comprises a plurality of computers each having a first input/output interface and a second input/output interface for interfacing to communications networks each second input/output interface including a bypass for bypassing the associated computer. A global communications network interconnects the first input/output interfaces for providing each computer the ability to broadcast messages simultaneously to the remainder of the computers. A meshwork communications network interconnects the second input/output interfaces providing each computer with the ability to establish a communications link with another of the computers bypassing the remainder of computers. Each computer is controlled by a resident copy of a common operating system. Communications between respective ones of computers is by means of split tokens each having a moving first portion which is sent from computer to computer and a resident second portion which is disposed in the memory of at least one of computer and wherein the location of the second portion is part of the first portion. The split tokens represent both functions to be executed by the computers and data to be employed in the execution of the functions. The first input/output interfaces each include logic for detecting a collision between messages and for terminating the broadcasting of a message whereby collisions between messages are detected and avoided.

Communication efficiency and congestion of signal traffic in large-scale brain networks.

PubMed

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

PubMed Central

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R.

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication. PMID:24415931

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

PubMed Central

2014-01-01

Background Network-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers. Results We propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve “locally” the AFP problem, by designing “vertex-centric” implementations of network-based algorithms, but we do not give up thinking “globally” by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins. Conclusions The combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines. PMID:24843788

Virtual target tracking (VTT) as applied to mobile satellite communication networks

NASA Astrophysics Data System (ADS)

Amoozegar, Farid

1999-08-01

Traditionally, target tracking has been used for aerospace applications, such as, tracking highly maneuvering targets in a cluttered environment for missile-to-target intercept scenarios. Although the speed and maneuvering capability of current aerospace targets demand more efficient algorithms, many complex techniques have already been proposed in the literature, which primarily cover the defense applications of tracking methods. On the other hand, the rapid growth of Global Communication Systems, Global Information Systems (GIS), and Global Positioning Systems (GPS) is creating new and more diverse challenges for multi-target tracking applications. Mobile communication and computing can very well appreciate a huge market for Cellular Communication and Tracking Devices (CCTD), which will be tracking networked devices at the cellular level. The objective of this paper is to introduce a new concept, i.e., Virtual Target Tracking (VTT) for commercial applications of multi-target tracking algorithms and techniques as applied to mobile satellite communication networks. It would be discussed how Virtual Target Tracking would bring more diversity to target tracking research.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

PubMed

Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

2015-01-01

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence

PubMed Central

Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Ahmad, Siraj-ul-Islam; Qureshi, Ijaz Mansoor

2012-01-01

A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method. PMID:22919371

A Science Cloud: OneSpaceNet

NASA Astrophysics Data System (ADS)

Morikawa, Y.; Murata, K. T.; Watari, S.; Kato, H.; Yamamoto, K.; Inoue, S.; Tsubouchi, K.; Fukazawa, K.; Kimura, E.; Tatebe, O.; Shimojo, S.

2010-12-01

Main methodologies of Solar-Terrestrial Physics (STP) so far are theoretical, experimental and observational, and computer simulation approaches. Recently "informatics" is expected as a new (fourth) approach to the STP studies. Informatics is a methodology to analyze large-scale data (observation data and computer simulation data) to obtain new findings using a variety of data processing techniques. At NICT (National Institute of Information and Communications Technology, Japan) we are now developing a new research environment named "OneSpaceNet". The OneSpaceNet is a cloud-computing environment specialized for science works, which connects many researchers with high-speed network (JGN: Japan Gigabit Network). The JGN is a wide-area back-born network operated by NICT; it provides 10G network and many access points (AP) over Japan. The OneSpaceNet also provides with rich computer resources for research studies, such as super-computers, large-scale data storage area, licensed applications, visualization devices (like tiled display wall: TDW), database/DBMS, cluster computers (4-8 nodes) for data processing and communication devices. What is amazing in use of the science cloud is that a user simply prepares a terminal (low-cost PC). Once connecting the PC to JGN2plus, the user can make full use of the rich resources of the science cloud. Using communication devices, such as video-conference system, streaming and reflector servers, and media-players, the users on the OneSpaceNet can make research communications as if they belong to a same (one) laboratory: they are members of a virtual laboratory. The specification of the computer resources on the OneSpaceNet is as follows: The size of data storage we have developed so far is almost 1PB. The number of the data files managed on the cloud storage is getting larger and now more than 40,000,000. What is notable is that the disks forming the large-scale storage are distributed to 5 data centers over Japan (but the storage system performs as one disk). There are three supercomputers allocated on the cloud, one from Tokyo, one from Osaka and the other from Nagoya. One's simulation job data on any supercomputers are saved on the cloud data storage (same directory); it is a kind of virtual computing environment. The tiled display wall has 36 panels acting as one display; the pixel (resolution) size of it is as large as 18000x4300. This size is enough to preview or analyze the large-scale computer simulation data. It also allows us to take a look of multiple (e.g., 100 pictures) on one screen together with many researchers. In our talk we also present a brief report of the initial results using the OneSpaceNet for Global MHD simulations as an example of successful use of our science cloud; (i) Ultra-high time resolution visualization of Global MHD simulations on the large-scale storage and parallel processing system on the cloud, (ii) Database of real-time Global MHD simulation and statistic analyses of the data, and (iii) 3D Web service of Global MHD simulations.

Remote consultation and diagnosis in medical imaging using a global PACS backbone network

NASA Astrophysics Data System (ADS)

Martinez, Ralph; Sutaria, Bijal N.; Kim, Jinman; Nam, Jiseung

1993-10-01

A Global PACS is a national network which interconnects several PACS networks at medical and hospital complexes using a national backbone network. A Global PACS environment enables new and beneficial operations between radiologists and physicians, when they are located in different geographical locations. One operation allows the radiologist to view the same image folder at both Local and Remote sites so that a diagnosis can be performed. The paper describes the user interface, database management, and network communication software which has been developed in the Computer Engineering Research Laboratory and Radiology Research Laboratory. Specifically, a design for a file management system in a distributed environment is presented. In the remote consultation and diagnosis operation, a set of images is requested from the database archive system and sent to the Local and Remote workstation sites on the Global PACS network. Viewing the same images, the radiologists use pointing overlay commands, or frames to point out features on the images. Each workstation transfers these frames, to the other workstation, so that an interactive session for diagnosis takes place. In this phase, we use fixed frames and variable size frames, used to outline an object. The data pockets for these frames traverses the national backbone in real-time. We accomplish this feature by using TCP/IP protocol sockets for communications. The remote consultation and diagnosis operation has been tested in real-time between the University Medical Center and the Bowman Gray School of Medicine at Wake Forest University, over the Internet. In this paper, we show the feasibility of the operation in a Global PACS environment. Future improvements to the system will include real-time voice and interactive compressed video scenarios.

Dynamics of global supply chain and electric power networks: Models, pricing analysis, and computations

NASA Astrophysics Data System (ADS)

Matsypura, Dmytro

In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the application of finite-dimensional variational inequality theory, projected dynamical systems theory, game theory, network theory, and other tools that have been recently proposed for the modeling and analysis of supply chain networks (cf. Nagurney (2006)) to electric power markets. This dissertation contributes to the extant literature on the modeling, analysis, and solution of supply chain networks, including global supply chains, in general, and electric power supply chains, in particular, in the following ways. It develops a theoretical framework for modeling, pricing analysis, and computation of electric power flows/transactions in electric power systems using the rationale for supply chain analysis. The models developed include both static and dynamic ones. The dissertation also adds a new dimension to the methodology of the theory of projected dynamical systems by proving that, irrespective of the speeds of adjustment, the equilibrium of the system remains the same. Finally, I include alternative fuel suppliers, along with their behavior into the supply chain modeling and analysis framework. This dissertation has strong practical implications. In an era in which technology and globalization, coupled with increasing risk and uncertainty, complicate electricity demand and supply within and between nations, the successful management of electric power systems and pricing become increasingly pressing topics with relevance not only for economic prosperity but also national security. This dissertation addresses such related topics by providing models, pricing tools, and algorithms for decentralized electric power supply chains. This dissertation is based heavily on the following coauthored papers: Nagurney, Cruz, and Matsypura (2003), Nagurney and Matsypura (2004, 2005, 2006), Matsypura and Nagurney (2005), Matsypura, Nagurney, and Liu (2006).

Complete characterization of the stability of cluster synchronization in complex dynamical networks.

PubMed

Sorrentino, Francesco; Pecora, Louis M; Hagerstrom, Aaron M; Murphy, Thomas E; Roy, Rajarshi

2016-04-01

Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit incomplete synchronization, where two or more clusters of synchronization persist, and computational group theory has recently proved to be valuable in discovering these cluster states based on the topology of the network. In the important case of Laplacian coupling, additional synchronization patterns can exist that would not be predicted from the group theory analysis alone. Understanding how and when clusters form, merge, and persist is essential for understanding collective dynamics, synchronization, and failure mechanisms of complex networks such as electric power grids, distributed control networks, and autonomous swarming vehicles. We describe a method to find and analyze all of the possible cluster synchronization patterns in a Laplacian-coupled network, by applying methods of computational group theory to dynamically equivalent networks. We present a general technique to evaluate the stability of each of the dynamically valid cluster synchronization patterns. Our results are validated in an optoelectronic experiment on a five-node network that confirms the synchronization patterns predicted by the theory.

Analysis of Energy Consumption for Ad Hoc Wireless Sensor Networks Using a Bit-Meter-per-Joule Metric

NASA Astrophysics Data System (ADS)

Gao, J. L.

2002-04-01

In this article, we present a system-level characterization of the energy consumption for sensor network application scenarios. We compute a power efficiency metric -- average watt-per-meter -- for each radio transmission and extend this local metric to find the global energy consumption. This analysis shows how overall energy consumption varies with transceiver characteristics, node density, data traffic distribution, and base-station location.

The topology of metabolic isotope labeling networks.

PubMed

Weitzel, Michael; Wiechert, Wolfgang; Nöh, Katharina

2007-08-29

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

The BlueGene/L supercomputer

NASA Astrophysics Data System (ADS)

Bhanota, Gyan; Chen, Dong; Gara, Alan; Vranas, Pavlos

2003-05-01

The architecture of the BlueGene/L massively parallel supercomputer is described. Each computing node consists of a single compute ASIC plus 256 MB of external memory. The compute ASIC integrates two 700 MHz PowerPC 440 integer CPU cores, two 2.8 Gflops floating point units, 4 MB of embedded DRAM as cache, a memory controller for external memory, six 1.4 Gbit/s bi-directional ports for a 3-dimensional torus network connection, three 2.8 Gbit/s bi-directional ports for connecting to a global tree network and a Gigabit Ethernet for I/O. 65,536 of such nodes are connected into a 3-d torus with a geometry of 32×32×64. The total peak performance of the system is 360 Teraflops and the total amount of memory is 16 TeraBytes.

Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles

NASA Astrophysics Data System (ADS)

Kolsbjerg, E. L.; Peterson, A. A.; Hammer, B.

2018-05-01

We show that approximate structural relaxation with a neural network enables orders of magnitude faster global optimization with an evolutionary algorithm in a density functional theory framework. The increased speed facilitates reliable identification of global minimum energy structures, as exemplified by our finding of a hollow Pt13 nanoparticle on an MgO support. We highlight the importance of knowing the correct structure when studying the catalytic reactivity of the different particle shapes. The computational speedup further enables screening of hundreds of different pathways in the search for optimum kinetic transitions between low-energy conformers and hence pushes the limits of the insight into thermal ensembles that can be obtained from theory.

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

DTIC Science & Technology

2015-03-16

sensitivity value was the maximum uncertainty in that value estimated by the Sobol method. 2.4. Global Sensitivity Analysis of the Reduced Order Coagulation...sensitivity analysis, using the variance-based method of Sobol , to estimate which parameters controlled the performance of the reduced order model [69]. We...Environment. Comput. Sci. Eng. 2007, 9, 90–95. 69. Sobol , I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Comparing Networks from a Data Analysis Perspective

NASA Astrophysics Data System (ADS)

Li, Wei; Yang, Jing-Yu

To probe network characteristics, two predominant ways of network comparison are global property statistics and subgraph enumeration. However, they suffer from limited information and exhaustible computing. Here, we present an approach to compare networks from the perspective of data analysis. Initially, the approach projects each node of original network as a high-dimensional data point, and the network is seen as clouds of data points. Then the dispersion information of the principal component analysis (PCA) projection of the generated data clouds can be used to distinguish networks. We applied this node projection method to the yeast protein-protein interaction networks and the Internet Autonomous System networks, two types of networks with several similar higher properties. The method can efficiently distinguish one from the other. The identical result of different datasets from independent sources also indicated that the method is a robust and universal framework.

Infrastructure for the Global Village.

ERIC Educational Resources Information Center

Gore, Al

1991-01-01

Technologies that enhance the ability to create and understand information have led to changes in the traditional legal concepts of property, ownership, originality, privacy, and intellectual freedom. The importance of government addressing such issues and investing in development of a high-capacity computer network is discussed. (KR)

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

PubMed Central

Usaite, Renata; Jewett, Michael C; Oliveira, Ana Paula; Yates, John R; Olsson, Lisbeth; Nielsen, Jens

2009-01-01

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases. PMID:19888214

Dragon pulse information management system (DPIMS): A unique model-based approach to implementing domain agnostic system of systems and behaviors

NASA Astrophysics Data System (ADS)

Anderson, Thomas S.

2016-05-01

The Global Information Network Architecture is an information technology based on Vector Relational Data Modeling, a unique computational paradigm, DoD network certified by USARMY as the Dragon Pulse Informa- tion Management System. This network available modeling environment for modeling models, where models are configured using domain relevant semantics and use network available systems, sensors, databases and services as loosely coupled component objects and are executable applications. Solutions are based on mission tactics, techniques, and procedures and subject matter input. Three recent ARMY use cases are discussed a) ISR SoS. b) Modeling and simulation behavior validation. c) Networked digital library with behaviors.

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

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

PubMed

Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing

2017-01-01

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks.

PubMed

Hu, Jialu; Kehr, Birte; Reinert, Knut

2014-02-15

Owing to recent advancements in high-throughput technologies, protein-protein interaction networks of more and more species become available in public databases. The question of how to identify functionally conserved proteins across species attracts a lot of attention in computational biology. Network alignments provide a systematic way to solve this problem. However, most existing alignment tools encounter limitations in tackling this problem. Therefore, the demand for faster and more efficient alignment tools is growing. We present a fast and accurate algorithm, NetCoffee, which allows to find a global alignment of multiple protein-protein interaction networks. NetCoffee searches for a global alignment by maximizing a target function using simulated annealing on a set of weighted bipartite graphs that are constructed using a triplet approach similar to T-Coffee. To assess its performance, NetCoffee was applied to four real datasets. Our results suggest that NetCoffee remedies several limitations of previous algorithms, outperforms all existing alignment tools in terms of speed and nevertheless identifies biologically meaningful alignments. The source code and data are freely available for download under the GNU GPL v3 license at https://code.google.com/p/netcoffee/.

Robust scalable stabilisability conditions for large-scale heterogeneous multi-agent systems with uncertain nonlinear interactions: towards a distributed computing architecture

NASA Astrophysics Data System (ADS)

Manfredi, Sabato

2016-06-01

Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network size. Finally, a numerical example shows the applicability of the proposed method and its advantage in terms of computational complexity when compared with the existing approaches.

Collective network routing

DOEpatents

Hoenicke, Dirk

2014-12-02

Disclosed are a unified method and apparatus to classify, route, and process injected data packets into a network so as to belong to a plurality of logical networks, each implementing a specific flow of data on top of a common physical network. The method allows to locally identify collectives of packets for local processing, such as the computation of the sum, difference, maximum, minimum, or other logical operations among the identified packet collective. Packets are injected together with a class-attribute and an opcode attribute. Network routers, employing the described method, use the packet attributes to look-up the class-specific route information from a local route table, which contains the local incoming and outgoing directions as part of the specifically implemented global data flow of the particular virtual network.

Cross layer optimization for cloud-based radio over optical fiber networks

NASA Astrophysics Data System (ADS)

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong; Yang, Hui; Meng, Luoming

2016-07-01

To adapt the 5G communication, the cloud radio access network is a paradigm introduced by operators which aggregates all base stations computational resources into a cloud BBU pool. The interaction between RRH and BBU or resource schedule among BBUs in cloud have become more frequent and complex with the development of system scale and user requirement. It can promote the networking demand among RRHs and BBUs, and force to form elastic optical fiber switching and networking. In such network, multiple stratum resources of radio, optical and BBU processing unit have interweaved with each other. In this paper, we propose a novel multiple stratum optimization (MSO) architecture for cloud-based radio over optical fiber networks (C-RoFN) with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. MSO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation and path provisioning latency.

Analysis Tools for Interconnected Boolean Networks With Biological Applications.

PubMed

Chaves, Madalena; Tournier, Laurent

2018-01-01

Boolean networks with asynchronous updates are a class of logical models particularly well adapted to describe the dynamics of biological networks with uncertain measures. The state space of these models can be described by an asynchronous state transition graph, which represents all the possible exits from every single state, and gives a global image of all the possible trajectories of the system. In addition, the asynchronous state transition graph can be associated with an absorbing Markov chain, further providing a semi-quantitative framework where it becomes possible to compute probabilities for the different trajectories. For large networks, however, such direct analyses become computationally untractable, given the exponential dimension of the graph. Exploiting the general modularity of biological systems, we have introduced the novel concept of asymptotic graph , computed as an interconnection of several asynchronous transition graphs and recovering all asymptotic behaviors of a large interconnected system from the behavior of its smaller modules. From a modeling point of view, the interconnection of networks is very useful to address for instance the interplay between known biological modules and to test different hypotheses on the nature of their mutual regulatory links. This paper develops two new features of this general methodology: a quantitative dimension is added to the asymptotic graph, through the computation of relative probabilities for each final attractor and a companion cross-graph is introduced to complement the method on a theoretical point of view.

ICPP: Approach for Understanding Complexity of Plasma

NASA Astrophysics Data System (ADS)

Sato, Tetsuya

2000-10-01

In this talk I wish to present an IT system that could promote Science of Complexity. In order to deal with a seemingly `complex' phenomenon, which means `beyond analytical manipulation', computer simulation is a viable powerful tool. However, complexity implies a concept beyond the horizon of reductionism. Therefore, rather than simply solving a complex phenomenon for a given boundary condition, one must establish an intelligent way of attacking mutual evolution of a system and its environment. NIFS-TCSC has been developing a prototype system that consists of supercomputers, virtual reality devices and high-speed network system. Let us explain this by picking up a global atmospheric circulation group, global oceanic circulation group and local weather prediction group. Local weather prediction group predicts the local change of the weather such as the creation of cloud and rain in the near future under the global conditions obtained by the global atmospheric and ocean groups. The global groups run simulations by modifying the local heat source/sink evaluated by the local weather prediction and then obtain the global conditions in the next time step. By repeating such a feedback performance one can predict the mutual evolution of the local system and its environment. Mutual information exchanges among multiple groups are carried out instantaneously by the networked common virtual reality space in which 3-D global and local images of the atmospheric and oceanic circulation and the cloud and rain maps are arbitrarily manipulated by any of the groups and commonly viewed. The present networking system has a great advantage that any simulation groups can freely and arbitrarily change their alignment, so that mutual evolution of any stratum system can become tractable by utilizing this network system.

A global network for the control of snail-borne disease using satellite surveillance and geographic information systems.

PubMed

Malone, J B; Bergquist, N R; Huh, O K; Bavia, M E; Bernardi, M; El Bahy, M M; Fuentes, M V; Kristensen, T K; McCarroll, J C; Yilma, J M; Zhou, X N

2001-04-27

At a team residency sponsored by the Rockefeller Foundation in Bellagio, Italy, 10-14 April 2000 an organizational plan was conceived to create a global network of collaborating health workers and earth scientists dedicated to the development of computer-based models that can be used for improved control programs for schistosomiasis and other snail-borne diseases of medical and veterinary importance. The models will be assembled using GIS methods, global climate model data, sensor data from earth observing satellites, disease prevalence data, the distribution and abundance of snail hosts, and digital maps of key environmental factors that affect development and propagation of snail-borne disease agents. A work plan was developed for research collaboration and data sharing, recruitment of new contributing researchers, and means of access of other medical scientists and national control program managers to GIS models that may be used for more effective control of snail-borne disease. Agreement was reached on the use of compatible GIS formats, software, methods and data resources, including the definition of a 'minimum medical database' to enable seamless incorporation of results from each regional GIS project into a global model. The collaboration plan calls for linking a 'central resource group' at the World Health Organization, the Food and Agriculture Organization, Louisiana State University and the Danish Bilharziasis Laboratory with regional GIS networks to be initiated in Eastern Africa, Southern Africa, West Africa, Latin America and Southern Asia. An Internet site, www.gnosisGIS.org, (GIS Network On Snail-borne Infections with special reference to Schistosomiasis), has been initiated to allow interaction of team members as a 'virtual research group'. When completed, the site will point users to a toolbox of common resources resident on computers at member organizations, provide assistance on routine use of GIS health maps in selected national disease control programs and provide a forum for development of GIS models to predict the health impacts of water development projects and climate variation.

Belle II grid computing: An overview of the distributed data management system.

NASA Astrophysics Data System (ADS)

Bansal, Vikas; Schram, Malachi; Belle Collaboration, II

2017-01-01

The Belle II experiment at the SuperKEKB collider in Tsukuba, Japan, will start physics data taking in 2018 and will accumulate 50/ab of e +e- collision data, about 50 times larger than the data set of the Belle experiment. The computing requirements of Belle II are comparable to those of a Run I LHC experiment. Computing at this scale requires efficient use of the compute grids in North America, Asia and Europe and will take advantage of upgrades to the high-speed global network. We present the architecture of data flow and data handling as a part of the Belle II computing infrastructure.

Effects of packet retransmission with finite packet lifetime on traffic capacity in scale-free networks

NASA Astrophysics Data System (ADS)

Jiang, Zhong-Yuan; Ma, Jian-Feng

Existing routing strategies such as the global dynamic routing [X. Ling, M. B. Hu, R. Jiang and Q. S. Wu, Phys. Rev. E 81, 016113 (2010)] can achieve very high traffic capacity at the cost of extremely long packet traveling delay. In many real complex networks, especially for real-time applications such as the instant communication software, extremely long packet traveling time is unacceptable. In this work, we propose to assign a finite Time-to-Live (TTL) parameter for each packet. To guarantee every packet to arrive at its destination within its TTL, we assume that a packet is retransmitted by its source once its TTL expires. We employ source routing mechanisms in the traffic model to avoid the routing-flaps induced by the global dynamic routing. We compose extensive simulations to verify our proposed mechanisms. With small TTL, the effects of packet retransmission on network traffic capacity are obvious, and the phase transition from flow free state to congested state occurs. For the purpose of reducing the computation frequency of the routing table, we employ a computing cycle Tc within which the routing table is recomputed once. The simulation results show that the traffic capacity decreases with increasing Tc. Our work provides a good insight into the understanding of effects of packet retransmission with finite packet lifetime on traffic capacity in scale-free networks.

GLOBECOM '89 - IEEE Global Telecommunications Conference and Exhibition, Dallas, TX, Nov. 27-30, 1989, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

The present conference discusses topics in multiwavelength network technology and its applications, advanced digital radio systems in their propagation environment, mobile radio communications, switching programmability, advancements in computer communications, integrated-network management and security, HDTV and image processing in communications, basic exchange communications radio advancements in digital switching, intelligent network evolution, speech coding for telecommunications, and multiple access communications. Also discussed are network designs for quality assurance, recent progress in coherent optical systems, digital radio applications, advanced communications technologies for mobile users, communication software for switching systems, AI and expert systems in network management, intelligent multiplexing nodes, video and image coding, network protocols and performance, system methods in quality and reliability, the design and simulation of lightwave systems, local radio networks, mobile satellite communications systems, fiber networks restoration, packet video networks, human interfaces for future networks, and lightwave networking.

Implementability of two-qubit unitary operations over the butterfly network and the ladder network with free classical communication

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

Akibue, Seiseki; Murao, Mio

2014-12-04

We investigate distributed implementation of two-qubit unitary operations over two primitive networks, the butterfly network and the ladder network, as a first step to apply network coding for quantum computation. By classifying two-qubit unitary operations in terms of the Kraus-Cirac number, the number of non-zero parameters describing the global part of two-qubit unitary operations, we analyze which class of two-qubit unitary operations is implementable over these networks with free classical communication. For the butterfly network, we show that two classes of two-qubit unitary operations, which contain all Clifford, controlled-unitary and matchgate operations, are implementable over the network. For the laddermore » network, we show that two-qubit unitary operations are implementable over the network if and only if their Kraus-Cirac number do not exceed the number of the bridges of the ladder.« less

Differentially Private Distributed Sensing

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

Fink, Glenn A.

The growth of the Internet of Things (IoT) creates the possibility of decentralized systems of sensing and actuation, potentially on a global scale. IoT devices connected to cloud networks can offer Sensing and Actuation as a Service (SAaaS) enabling networks of sensors to grow to a global scale. But extremely large sensor networks can violate privacy, especially in the case where IoT devices are mobile and connected directly to the behaviors of people. The thesis of this paper is that by adapting differential privacy (adding statistically appropriate noise to query results) to groups of geographically distributed sensors privacy could bemore » maintained without ever sending all values up to a central curator and without compromising the overall accuracy of the data collected. This paper outlines such a scheme and performs an analysis of differential privacy techniques adapted to edge computing in a simulated sensor network where ground truth is known. The positive and negative outcomes of employing differential privacy in distributed networks of devices are discussed and a brief research agenda is presented.« less

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

DOE PAGES

Ozmen, Ozgur; Yilmaz, Levent; Smith, Jeffrey

2016-02-09

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovationmore » potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Ultimately, the insight gained can help policy-makers to design GPS environments and promote innovation.« less

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

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

Ozmen, Ozgur; Yilmaz, Levent; Smith, Jeffrey

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovationmore » potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Ultimately, the insight gained can help policy-makers to design GPS environments and promote innovation.« less

Model-based redesign of global transcription regulation

PubMed Central

Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso

2009-01-01

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology. PMID:19188257

GIANT API: an application programming interface for functional genomics.

PubMed

Roberts, Andrew M; Wong, Aaron K; Fisk, Ian; Troyanskaya, Olga G

2016-07-08

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Climate Science's Globally Distributed Infrastructure

NASA Astrophysics Data System (ADS)

Williams, D. N.

2016-12-01

The Earth System Grid Federation (ESGF) is primarily funded by the Department of Energy's (DOE's) Office of Science (the Office of Biological and Environmental Research [BER] Climate Data Informatics Program and the Office of Advanced Scientific Computing Research Next Generation Network for Science Program), the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF), the European Infrastructure for the European Network for Earth System Modeling (IS-ENES), and the Australian National University (ANU). Support also comes from other U.S. federal and international agencies. The federation works across multiple worldwide data centers and spans seven international network organizations to provide users with the ability to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a series of geographically distributed peer nodes that are independently administered and united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP; output used by the Intergovernmental Panel on Climate Change assessment reports), multiple model intercomparison projects (MIPs; endorsed by the World Climate Research Programme [WCRP]), and the Accelerated Climate Modeling for Energy (ACME; ESGF is included in the overarching ACME workflow process to store model output). ESGF is a successful example of integration of disparate open-source technologies into a cohesive functional system that serves the needs the global climate science community. Data served by ESGF includes not only model output but also observational data from satellites and instruments, reanalysis, and generated images.

Multiple core computer processor with globally-accessible local memories

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

Shalf, John; Donofrio, David; Oliker, Leonid

A multi-core computer processor including a plurality of processor cores interconnected in a Network-on-Chip (NoC) architecture, a plurality of caches, each of the plurality of caches being associated with one and only one of the plurality of processor cores, and a plurality of memories, each of the plurality of memories being associated with a different set of at least one of the plurality of processor cores and each of the plurality of memories being configured to be visible in a global memory address space such that the plurality of memories are visible to two or more of the plurality ofmore » processor cores.« less

Linking microscopic and macroscopic response in disordered solids

NASA Astrophysics Data System (ADS)

Hexner, Daniel; Liu, Andrea J.; Nagel, Sidney R.

2018-06-01

The modulus of a rigid network of harmonic springs depends on the sum of the energies in each of the bonds due to an applied distortion such as compression in the case of the bulk modulus or shear in the case of the shear modulus. However, the distortion need not be global. Here we introduce a local modulus, Li, associated with changing the equilibrium length of a single bond, i , in the network. We show that Li is useful for understanding many aspects of the mechanical response of the entire system. It allows an efficient computation of how the removal of any bond changes the global properties such as the bulk and shear moduli. Furthermore, it allows a prediction of the distribution of these changes and clarifies why the changes of these two moduli due to removal of a bond are uncorrelated; these are the essential ingredients necessary for the efficient manipulation of network properties by bond removal.

Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis.

PubMed

Goch, Caspar J; Stieltjes, Bram; Henze, Romy; Hering, Jan; Poustka, Luise; Meinzer, Hans-Peter; Maier-Hein, Klaus H

2014-05-01

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices. We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke's Area, Broca's Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography. We show that the network centrality of Wernicke's area is significantly (p<0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD. The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.

Cross stratum resources protection in fog-computing-based radio over fiber networks for 5G services

NASA Astrophysics Data System (ADS)

Guo, Shaoyong; Shao, Sujie; Wang, Yao; Yang, Hui

2017-09-01

In order to meet the requirement of internet of things (IoT) and 5G, the cloud radio access network is a paradigm which converges all base stations computational resources into a cloud baseband unit (BBU) pool, while the distributed radio frequency signals are collected by remote radio head (RRH). A precondition for centralized processing in the BBU pool is an interconnection fronthaul network with high capacity and low delay. However, it has become more complex and frequent in the interaction between RRH and BBU and resource scheduling among BBUs in cloud. Cloud radio over fiber network has been proposed in our previous work already. In order to overcome the complexity and latency, in this paper, we first present a novel cross stratum resources protection (CSRP) architecture in fog-computing-based radio over fiber networks (F-RoFN) for 5G services. Additionally, a cross stratum protection (CSP) scheme considering the network survivability is introduced in the proposed architecture. The CSRP with CSP scheme can effectively pull the remote processing resource locally to implement the cooperative radio resource management, enhance the responsiveness and resilience to the dynamic end-to-end 5G service demands, and globally optimize optical network, wireless and fog resources. The feasibility and efficiency of the proposed architecture with CSP scheme are verified on our software defined networking testbed in terms of service latency, transmission success rate, resource occupation rate and blocking probability.

Dexmedetomidine Disrupts the Local and Global Efficiencies of Large-scale Brain Networks.

PubMed

Hashmi, Javeria A; Loggia, Marco L; Khan, Sheraz; Gao, Lei; Kim, Jieun; Napadow, Vitaly; Brown, Emery N; Akeju, Oluwaseun

2017-03-01

A clear understanding of the neural basis of consciousness is fundamental to research in clinical and basic neuroscience disciplines and anesthesia. Recently, decreased efficiency of information integration was suggested as a core network feature of propofol-induced unconsciousness. However, it is unclear whether this finding can be generalized to dexmedetomidine, which has a different molecular target. Dexmedetomidine was administered as a 1-μg/kg bolus over 10 min, followed by a 0.7-μg · kg · h infusion to healthy human volunteers (age range, 18 to 36 yr; n = 15). Resting-state functional magnetic resonance imaging data were acquired during baseline, dexmedetomidine-induced altered arousal, and recovery states. Zero-lag correlations between resting-state functional magnetic resonance imaging signals extracted from 131 brain parcellations were used to construct weighted brain networks. Network efficiency, degree distribution, and node strength were computed using graph analysis. Parcellated brain regions were also mapped to known resting-state networks to study functional connectivity changes. Dexmedetomidine significantly reduced the local and global efficiencies of graph theory-derived networks. Dexmedetomidine also reduced the average brain connectivity strength without impairing the degree distribution. Functional connectivity within and between all resting-state networks was modulated by dexmedetomidine. Dexmedetomidine is associated with a significant drop in the capacity for efficient information transmission at both the local and global levels. These changes result from reductions in the strength of connectivity and also manifest as reduced within and between resting-state network connectivity. These findings strengthen the hypothesis that conscious processing relies on an efficient system of information transfer in the brain.

SIR model on a dynamical network and the endemic state of an infectious disease

NASA Astrophysics Data System (ADS)

Dottori, M.; Fabricius, G.

2015-09-01

In this work we performed a numerical study of an epidemic model that mimics the endemic state of whooping cough in the pre-vaccine era. We considered a stochastic SIR model on dynamical networks that involve local and global contacts among individuals and analysed the influence of the network properties on the characterization of the quasi-stationary state. We computed probability density functions (PDF) for infected fraction of individuals and found that they are well fitted by gamma functions, excepted the tails of the distributions that are q-exponentials. We also computed the fluctuation power spectra of infective time series for different networks. We found that network effects can be partially absorbed by rescaling the rate of infective contacts of the model. An explicit relation between the effective transmission rate of the disease and the correlation of susceptible individuals with their infective nearest neighbours was obtained. This relation quantifies the known screening of infective individuals observed in these networks. We finally discuss the goodness and limitations of the SIR model with homogeneous mixing and parameters taken from epidemiological data to describe the dynamic behaviour observed in the networks studied.

Education in the Emerging Media Democracy.

ERIC Educational Resources Information Center

Rowe, Gary R.

1994-01-01

Discusses changes in media in a changing democratic society. Topics addressed include new visions of learning that incorporate computers and telecommunications; the use of multimedia and interactivity in education; the concept of a global village, with examples from CNN (Cable News Network); and changing from print literacy to media literacy. (LRW)

Toward Global Communication Networks: How Television is Forging New Thinking Patterns.

ERIC Educational Resources Information Center

Adams, Dennis M.; Fuchs, Mary

1986-01-01

Recent alliances between communication providers and computer manufacturers will lead to new technological combinations that will deliver visually-based ideas and information to a worldwide audience. Urges that those in charge of future video programs to consider their effects on children's language skills, thinking patterns, and intellectual…

Rich client data exploration and research prototyping for NOAA

NASA Astrophysics Data System (ADS)

Grossberg, Michael; Gladkova, Irina; Guch, Ingrid; Alabi, Paul; Shahriar, Fazlul; Bonev, George; Aizenman, Hannah

2009-08-01

Data from satellites and model simulations is increasing exponentially as observations and model computing power improve rapidly. Not only is technology producing more data, but it often comes from sources all over the world. Researchers and scientists who must collaborate are also located globally. This work presents a software design and technologies which will make it possible for groups of researchers to explore large data sets visually together without the need to download these data sets locally. The design will also make it possible to exploit high performance computing remotely and transparently to analyze and explore large data sets. Computer power, high quality sensing, and data storage capacity have improved at a rate that outstrips our ability to develop software applications that exploit these resources. It is impractical for NOAA scientists to download all of the satellite and model data that may be relevant to a given problem and the computing environments available to a given researcher range from supercomputers to only a web browser. The size and volume of satellite and model data are increasing exponentially. There are at least 50 multisensor satellite platforms collecting Earth science data. On the ground and in the sea there are sensor networks, as well as networks of ground based radar stations, producing a rich real-time stream of data. This new wealth of data would have limited use were it not for the arrival of large-scale high-performance computation provided by parallel computers, clusters, grids, and clouds. With these computational resources and vast archives available, it is now possible to analyze subtle relationships which are global, multi-modal and cut across many data sources. Researchers, educators, and even the general public, need tools to access, discover, and use vast data center archives and high performance computing through a simple yet flexible interface.

A systems approach to integrative biology: an overview of statistical methods to elucidate association and architecture.

PubMed

Ciaccio, Mark F; Finkle, Justin D; Xue, Albert Y; Bagheri, Neda

2014-07-01

An organism's ability to maintain a desired physiological response relies extensively on how cellular and molecular signaling networks interpret and react to environmental cues. The capacity to quantitatively predict how networks respond to a changing environment by modifying signaling regulation and phenotypic responses will help inform and predict the impact of a changing global enivronment on organisms and ecosystems. Many computational strategies have been developed to resolve cue-signal-response networks. However, selecting a strategy that answers a specific biological question requires knowledge both of the type of data being collected, and of the strengths and weaknesses of different computational regimes. We broadly explore several computational approaches, and we evaluate their accuracy in predicting a given response. Specifically, we describe how statistical algorithms can be used in the context of integrative and comparative biology to elucidate the genomic, proteomic, and/or cellular networks responsible for robust physiological response. As a case study, we apply this strategy to a dataset of quantitative levels of protein abundance from the mussel, Mytilus galloprovincialis, to uncover the temperature-dependent signaling network. © The Author 2014. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Understanding spatial and temporal patterning of astrocyte calcium transients via interactions between network transport and extracellular diffusion

NASA Astrophysics Data System (ADS)

Shtrahman, E.; Maruyama, D.; Olariu, E.; Fink, C. G.; Zochowski, M.

2017-02-01

Astrocytes form interconnected networks in the brain and communicate via calcium signaling. We investigate how modes of coupling between astrocytes influence the spatio-temporal patterns of calcium signaling within astrocyte networks and specifically how these network interactions promote coordination within this group of cells. To investigate these complex phenomena, we study reduced cultured networks of astrocytes and neurons. We image the spatial temporal patterns of astrocyte calcium activity and quantify how perturbing the coupling between astrocytes influences astrocyte activity patterns. To gain insight into the pattern formation observed in these cultured networks, we compare the experimentally observed calcium activity patterns to the patterns produced by a reduced computational model, where we represent astrocytes as simple units that integrate input through two mechanisms: gap junction coupling (network transport) and chemical release (extracellular diffusion). We examine the activity patterns in the simulated astrocyte network and their dependence upon these two coupling mechanisms. We find that gap junctions and extracellular chemical release interact in astrocyte networks to modulate the spatiotemporal patterns of their calcium dynamics. We show agreement between the computational and experimental findings, which suggests that the complex global patterns can be understood as a result of simple local coupling mechanisms.

Global Alignment of Pairwise Protein Interaction Networks for Maximal Common Conserved Patterns

DOE PAGES

Tian, Wenhong; Samatova, Nagiza F.

2013-01-01

A number of tools for the alignment of protein-protein interaction (PPI) networks have laid the foundation for PPI network analysis. Most of alignment tools focus on finding conserved interaction regions across the PPI networks through either local or global mapping of similar sequences. Researchers are still trying to improve the speed, scalability, and accuracy of network alignment. In view of this, we introduce a connected-components based fast algorithm, HopeMap, for network alignment. Observing that the size of true orthologs across species is small comparing to the total number of proteins in all species, we take a different approach based onmore » a precompiled list of homologs identified by KO terms. Applying this approach to S. cerevisiae (yeast) and D. melanogaster (fly), E. coli K12 and S. typhimurium , E. coli K12 and C. crescenttus , we analyze all clusters identified in the alignment. The results are evaluated through up-to-date known gene annotations, gene ontology (GO), and KEGG ortholog groups (KO). Comparing to existing tools, our approach is fast with linear computational cost, highly accurate in terms of KO and GO terms specificity and sensitivity, and can be extended to multiple alignments easily.« less

Mahali: Space Weather Monitoring Using Multicore Mobile Devices

NASA Astrophysics Data System (ADS)

Pankratius, V.; Lind, F. D.; Coster, A. J.; Erickson, P. J.; Semeter, J. L.

2013-12-01

Analysis of Total Electron Content (TEC) measurements derived from Global Positioning System (GPS) signals has led to revolutionary new data products for space weather monitoring and ionospheric research. However, the current sensor network is sparse, especially over the oceans and in regions like Africa and Siberia, and the full potential of dense, global, real-time TEC monitoring remains to be realized. The Mahali project will prototype a revolutionary architecture that uses mobile devices, such as phones and tablets, to form a global space weather monitoring network. Mahali exploits the existing GPS infrastructure - more specifically, delays in multi-frequency GPS signals observed at the ground - to acquire a vast set of global TEC projections, with the goal of imaging multi-scale variability in the global ionosphere at unprecedented spatial and temporal resolution. With connectivity available worldwide, mobile devices are excellent candidates to establish crowd sourced global relays that feed multi-frequency GPS sensor data into a cloud processing environment. Once the data is within the cloud, it is relatively straightforward to reconstruct the structure of the space environment, and its dynamic changes. This vision is made possible owing to advances in multicore technology that have transformed mobile devices into parallel computers with several processors on a chip. For example, local data can be pre-processed, validated with other sensors nearby, and aggregated when transmission is temporarily unavailable. Intelligent devices can also autonomously decide the most practical way of transmitting data with in any given context, e.g., over cell networks or Wifi, depending on availability, bandwidth, cost, energy usage, and other constraints. In the long run, Mahali facilitates data collection from remote locations such as deserts or on oceans. For example, mobile devices on ships could collect time-tagged measurements that are transmitted at a later point in time when some connectivity is available. Our concept of the overall Mahali system will employ both auto-tuning and machine learning techniques to cope with the opportunistic nature of data collection, computational load distribution on mobile devices and in the cloud, and fault-tolerance in a dynamically changing network. "Kila Mahali" means "everywhere" in the Swahili language. This project will follow that spirit by enabling space weather data collection even in the most remote places, resulting in dramatic improvements in observational gaps that exist in space weather research today. The dense network may enable the use of the entire ionosphere as a sensor to monitor geophysical events from earthquakes to tsunamis, and other natural disasters.

Molecular clock on a neutral network.

PubMed

Raval, Alpan

2007-09-28

The number of fixed mutations accumulated in an evolving population often displays a variance that is significantly larger than the mean (the overdispersed molecular clock). By examining a generic evolutionary process on a neutral network of high-fitness genotypes, we establish a formalism for computing all cumulants of the full probability distribution of accumulated mutations in terms of graph properties of the neutral network, and use the formalism to prove overdispersion of the molecular clock. We further show that significant overdispersion arises naturally in evolution when the neutral network is highly sparse, exhibits large global fluctuations in neutrality, and small local fluctuations in neutrality. The results are also relevant for elucidating aspects of neutral network topology from empirical measurements of the substitution process.

Molecular Clock on a Neutral Network

NASA Astrophysics Data System (ADS)

Raval, Alpan

2007-09-01

The number of fixed mutations accumulated in an evolving population often displays a variance that is significantly larger than the mean (the overdispersed molecular clock). By examining a generic evolutionary process on a neutral network of high-fitness genotypes, we establish a formalism for computing all cumulants of the full probability distribution of accumulated mutations in terms of graph properties of the neutral network, and use the formalism to prove overdispersion of the molecular clock. We further show that significant overdispersion arises naturally in evolution when the neutral network is highly sparse, exhibits large global fluctuations in neutrality, and small local fluctuations in neutrality. The results are also relevant for elucidating aspects of neutral network topology from empirical measurements of the substitution process.

Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.; Clare, Loren P.

2013-01-01

Contact Graph Routing (CGR) for Delay/Disruption Tolerant Networking (DTN) space-based networks makes use of the predictable nature of node contacts to make real-time routing decisions given unpredictable traffic patterns. The contact graph will have been disseminated to all nodes before the start of route computation. CGR was designed for space-based networking environments where future contact plans are known or are independently computable (e.g., using known orbital dynamics). For each data item (known as a bundle in DTN), a node independently performs route selection by examining possible paths to the destination. Route computation could conceivably run thousands of times a second, so computational load is important. This work refers to the simulation software model of Enhanced Contact Graph Routing (ECGR) for DTN Bundle Protocol in JPL's MACHETE simulation tool. The simulation model was used for performance analysis of CGR and led to several performance enhancements. The simulation model was used to demonstrate the improvements of ECGR over CGR as well as other routing methods in space network scenarios. ECGR moved to using earliest arrival time because it is a global monotonically increasing metric that guarantees the safety properties needed for the solution's correctness since route re-computation occurs at each node to accommodate unpredicted changes (e.g., traffic pattern, link quality). Furthermore, using earliest arrival time enabled the use of the standard Dijkstra algorithm for path selection. The Dijkstra algorithm for path selection has a well-known inexpensive computational cost. These enhancements have been integrated into the open source CGR implementation. The ECGR model is also useful for route metric experimentation and comparisons with other DTN routing protocols particularly when combined with MACHETE's space networking models and Delay Tolerant Link State Routing (DTLSR) model.

Optimal convergence in naming game with geography-based negotiation on small-world networks

NASA Astrophysics Data System (ADS)

Liu, Run-Ran; Wang, Wen-Xu; Lai, Ying-Cheng; Chen, Guanrong; Wang, Bing-Hong

2011-01-01

We propose a negotiation strategy to address the effect of geography on the dynamics of naming games over small-world networks. Communication and negotiation frequencies between two agents are determined by their geographical distance in terms of a parameter characterizing the correlation between interaction strength and the distance. A finding is that there exists an optimal parameter value leading to fastest convergence to global consensus on naming. Numerical computations and a theoretical analysis are provided to substantiate our findings.

Deferred Compilation: The Automation of Run-Time Code Generation

DTIC Science & Technology

1993-12-01

can bte amortizted over many late computations ’iCPW931. For example, in a itmandard MtL implementation of a network cotmmunications *ystem, Biagioni ...with global variables and abstract data types. Science of Computer Pr"rnMmminq, 16(2):151-195. Septernber 1991. BHL93’ Edoaxdo Biagioni , Robert Harper...16(2):151-195. September 1991. 311L93i Edoardo Biagioni , Robert Harper, and Peter Lee. Standard NIL signatures for a protocol stack. Technical

Resting-State Connectivity of the Left Frontal Cortex to the Default Mode and Dorsal Attention Network Supports Reserve in Mild Cognitive Impairment.

PubMed

Franzmeier, Nicolai; Göttler, Jens; Grimmer, Timo; Drzezga, Alexander; Áraque-Caballero, Miguel A; Simon-Vermot, Lee; Taylor, Alexander N W; Bürger, Katharina; Catak, Cihan; Janowitz, Daniel; Müller, Claudia; Duering, Marco; Sorg, Christian; Ewers, Michael

2017-01-01

Reserve refers to the phenomenon of relatively preserved cognition in disproportion to the extent of neuropathology, e.g., in Alzheimer's disease. A putative functional neural substrate underlying reserve is global functional connectivity of the left lateral frontal cortex (LFC, Brodmann Area 6/44). Resting-state fMRI-assessed global LFC-connectivity is associated with protective factors (education) and better maintenance of memory in mild cognitive impairment (MCI). Since the LFC is a hub of the fronto-parietal control network that regulates the activity of other networks, the question arises whether LFC-connectivity to specific networks rather than the whole-brain may underlie reserve. We assessed resting-state fMRI in 24 MCI and 16 healthy controls (HC) and in an independent validation sample (23 MCI/32 HC). Seed-based LFC-connectivity to seven major resting-state networks (i.e., fronto-parietal, limbic, dorsal-attention, somatomotor, default-mode, ventral-attention, visual) was computed, reserve was quantified as residualized memory performance after accounting for age and hippocampal atrophy. In both samples of MCI, LFC-activity was anti-correlated with the default-mode network (DMN), but positively correlated with the dorsal-attention network (DAN). Greater education predicted stronger LFC-DMN-connectivity (anti-correlation) and LFC-DAN-connectivity. Stronger LFC-DMN and LFC-DAN-connectivity each predicted higher reserve, consistently in both MCI samples. No associations were detected for LFC-connectivity to other networks. These novel results extend our previous findings on global functional connectivity of the LFC, showing that LFC-connectivity specifically to the DAN and DMN, two core memory networks, enhances reserve in the memory domain in MCI.

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

Computationally efficient characterization of potential energy surfaces based on fingerprint distances

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

Schaefer, Bastian; Goedecker, Stefan, E-mail: stefan.goedecker@unibas.ch

2016-07-21

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This methodmore » allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.« less

Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference

PubMed Central

2011-01-01

The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB’s goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design. PMID:22372736

Active matter logic for autonomous microfluidics

NASA Astrophysics Data System (ADS)

Woodhouse, Francis G.; Dunkel, Jörn

2017-04-01

Chemically or optically powered active matter plays an increasingly important role in materials design, but its computational potential has yet to be explored systematically. The competition between energy consumption and dissipation imposes stringent physical constraints on the information transport in active flow networks, facilitating global optimization strategies that are not well understood. Here, we combine insights from recent microbial experiments with concepts from lattice-field theory and non-equilibrium statistical mechanics to introduce a generic theoretical framework for active matter logic. Highlighting conceptual differences with classical and quantum computation, we demonstrate how the inherent non-locality of incompressible active flow networks can be utilized to construct universal logical operations, Fredkin gates and memory storage in set-reset latches through the synchronized self-organization of many individual network components. Our work lays the conceptual foundation for developing autonomous microfluidic transport devices driven by bacterial fluids, active liquid crystals or chemically engineered motile colloids.

Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome.

PubMed

Rees, Christopher L; Wheeler, Diek W; Hamilton, David J; White, Charise M; Komendantov, Alexander O; Ascoli, Giorgio A

2016-01-01

We computed the potential connectivity map of all known neuron types in the rodent hippocampal formation by supplementing scantly available synaptic data with spatial distributions of axons and dendrites from the open-access knowledge base Hippocampome.org. The network that results from this endeavor, the broadest and most complete for a mammalian cortical region at the neuron-type level to date, contains more than 3200 connections among 122 neuron types across six subregions. Analyses of these data using graph theory metrics unveil the fundamental architectural principles of the hippocampal circuit. Globally, we identify a highly specialized topology minimizing communication cost; a modular structure underscoring the prominence of the trisynaptic loop; a core set of neuron types serving as information-processing hubs as well as a distinct group of particular antihub neurons; a nested, two-tier rich club managing much of the network traffic; and an innate resilience to random perturbations. At the local level, we uncover the basic building blocks, or connectivity patterns, that combine to produce complex global functionality, and we benchmark their utilization in the circuit relative to random networks. Taken together, these results provide a comprehensive connectivity profile of the hippocampus, yielding novel insights on its functional operations at the computationally crucial level of neuron types.

Improving Environmental Literacy through GO3 Citizen Science Project

NASA Astrophysics Data System (ADS)

Wilkening, B.

2011-12-01

In the Global Ozone (GO3) Project students measure ground-level ozone on a continuous basis and upload their results to a global network used by atmospheric scientists and schools. Students learn important concepts such as chemical measurement methods; instrumentation; calibration; data acquisition using computers; data quality; statistics; data analysis and graphing; posting of data to the web; the chemistry of air pollution; stratospheric ozone depletion and global climate change. Students collaborate with researchers and other students globally in the GO3 network. Wilson K-8 School is located in a suburban area in Pima County, Arizona. Throughout the year we receive high ozone alert days. Prior to joining the GO3 project, my students were unaware of air pollution alerts, risks and causes. In the past when Pima County issued alerts to the school, they were posted on signs around the school. No explanation was provided to the students and the signs were often left up for days. This discounted the potential health effects of the situation, resulting in the alerts effectively being ignored. The GO3 project is transforming both my students and our school community. Now my students are:

• Performing science research
• Utilizing technology and increasing their skills
• Collaborating in a responsible manner on the global GO3 social network
• Communicating their work to the community
• Issuing their own ozone alerts to their school
• Advocating for actions that will improve air quality

Big Data and High-Performance Computing in Global Seismology

NASA Astrophysics Data System (ADS)

Bozdag, Ebru; Lefebvre, Matthieu; Lei, Wenjie; Peter, Daniel; Smith, James; Komatitsch, Dimitri; Tromp, Jeroen

2014-05-01

Much of our knowledge of Earth's interior is based on seismic observations and measurements. Adjoint methods provide an efficient way of incorporating 3D full wave propagation in iterative seismic inversions to enhance tomographic images and thus our understanding of processes taking place inside the Earth. Our aim is to take adjoint tomography, which has been successfully applied to regional and continental scale problems, further to image the entire planet. This is one of the extreme imaging challenges in seismology, mainly due to the intense computational requirements and vast amount of high-quality seismic data that can potentially be assimilated. We have started low-resolution inversions (T > 30 s and T > 60 s for body and surface waves, respectively) with a limited data set (253 carefully selected earthquakes and seismic data from permanent and temporary networks) on Oak Ridge National Laboratory's Cray XK7 "Titan" system. Recent improvements in our 3D global wave propagation solvers, such as a GPU version of the SPECFEM3D_GLOBE package, will enable us perform higher-resolution (T > 9 s) and longer duration (~180 m) simulations to take the advantage of high-frequency body waves and major-arc surface waves, thereby improving imbalanced ray coverage as a result of the uneven global distribution of sources and receivers. Our ultimate goal is to use all earthquakes in the global CMT catalogue within the magnitude range of our interest and data from all available seismic networks. To take the full advantage of computational resources, we need a solid framework to manage big data sets during numerical simulations, pre-processing (i.e., data requests and quality checks, processing data, window selection, etc.) and post-processing (i.e., pre-conditioning and smoothing kernels, etc.). We address the bottlenecks in our global seismic workflow, which are mainly coming from heavy I/O traffic during simulations and the pre- and post-processing stages, by defining new data formats for seismograms and outputs of our 3D solvers (i.e., meshes, kernels, seismic models, etc.) based on ORNL's ADIOS libraries. We will discuss our global adjoint tomography workflow on HPC systems as well as the current status of our global inversions.

Artificial Neural Network L* from different magnetospheric field models

NASA Astrophysics Data System (ADS)

Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

2011-12-01

The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

Octree-based Global Earthquake Simulations

NASA Astrophysics Data System (ADS)

Ramirez-Guzman, L.; Juarez, A.; Bielak, J.; Salazar Monroy, E. F.

2017-12-01

Seismological research has motivated recent efforts to construct more accurate three-dimensional (3D) velocity models of the Earth, perform global simulations of wave propagation to validate models, and also to study the interaction of seismic fields with 3D structures. However, traditional methods for seismogram computation at global scales are limited by computational resources, relying primarily on traditional methods such as normal mode summation or two-dimensional numerical methods. We present an octree-based mesh finite element implementation to perform global earthquake simulations with 3D models using topography and bathymetry with a staircase approximation, as modeled by the Carnegie Mellon Finite Element Toolchain Hercules (Tu et al., 2006). To verify the implementation, we compared the synthetic seismograms computed in a spherical earth against waveforms calculated using normal mode summation for the Preliminary Earth Model (PREM) for a point source representation of the 2014 Mw 7.3 Papanoa, Mexico earthquake. We considered a 3 km-thick ocean layer for stations with predominantly oceanic paths. Eigen frequencies and eigen functions were computed for toroidal, radial, and spherical oscillations in the first 20 branches. Simulations are valid at frequencies up to 0.05 Hz. Matching among the waveforms computed by both approaches, especially for long period surface waves, is excellent. Additionally, we modeled the Mw 9.0 Tohoku-Oki earthquake using the USGS finite fault inversion. Topography and bathymetry from ETOPO1 are included in a mesh with more than 3 billion elements; constrained by the computational resources available. We compared estimated velocity and GPS synthetics against observations at regional and teleseismic stations of the Global Seismological Network and discuss the differences among observations and synthetics, revealing that heterogeneity, particularly in the crust, needs to be considered.

Test-Retest Reliability of Graph Metrics in Functional Brain Networks: A Resting-State fNIRS Study

PubMed Central

Niu, Haijing; Li, Zhen; Liao, Xuhong; Wang, Jinhui; Zhao, Tengda; Shu, Ni; Zhao, Xiaohu; He, Yong

2013-01-01

Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network research. This study also provides important guidance on the choice of network metrics of interest for future applied research in developmental and clinical neuroscience. PMID:24039763

TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE.

PubMed

Prasad, Gautam; Nir, Talia M; Toga, Arthur W; Thompson, Paul M

2013-04-01

Brain connectivity declines in Alzheimer's disease (AD), both functionally and structurally. Connectivity maps and networks derived from diffusion-based tractography offer new ways to track disease progression and to understand how AD affects the brain. Here we set out to identify (1) which fiber network measures show greatest differences between AD patients and controls, and (2) how these effects depend on the density of fibers extracted by the tractography algorithm. We computed brain networks from diffusion-weighted images (DWI) of the brain, in 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD). We derived connectivity matrices and network topology measures, for each subject, from whole-brain tractography and cortical parcellations. We used an ODF lookup table to speed up fiber extraction, and to exploit the full information in the orientation distribution function (ODF). This made it feasible to compute high density connectivity maps. We used accelerated tractography to compute a large number of fibers to understand what effect fiber density has on network measures and in distinguishing different disease groups in our data. We focused on global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity measures computed from weighted and binary undirected connectivity matrices. Of all these measures, the mean nodal degree best distinguished diagnostic groups. High-density fiber matrices were most helpful for picking up the more subtle clinical differences, e.g. between mild cognitively impaired (MCI) and normals, or for distinguishing subtypes of MCI (early versus late). Care is needed in clinical analyses of brain connectivity, as the density of extracted fibers may affect how well a network measure can pick up differences between patients and controls.

Chain-Based Communication in Cylindrical Underwater Wireless Sensor Networks

PubMed Central

Javaid, Nadeem; Jafri, Mohsin Raza; Khan, Zahoor Ali; Alrajeh, Nabil; Imran, Muhammad; Vasilakos, Athanasios

2015-01-01

Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, i.e., rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate. PMID:25658394

Medical reliable network using concatenated channel codes through GSM network.

PubMed

Ahmed, Emtithal; Kohno, Ryuji

2013-01-01

Although the 4(th) generation (4G) of global mobile communication network, i.e. Long Term Evolution (LTE) coexisting with the 3(rd) generation (3G) has successfully started; the 2(nd) generation (2G), i.e. Global System for Mobile communication (GSM) still playing an important role in many developing countries. Without any other reliable network infrastructure, GSM can be applied for tele-monitoring applications, where high mobility and low cost are necessary. A core objective of this paper is to introduce the design of a more reliable and dependable Medical Network Channel Code system (MNCC) through GSM Network. MNCC design based on simple concatenated channel code, which is cascade of an inner code (GSM) and an extra outer code (Convolution Code) in order to protect medical data more robust against channel errors than other data using the existing GSM network. In this paper, the MNCC system will provide Bit Error Rate (BER) equivalent to the BER for medical tele monitoring of physiological signals, which is 10(-5) or less. The performance of the MNCC has been proven and investigated using computer simulations under different channels condition such as, Additive White Gaussian Noise (AWGN), Rayleigh noise and burst noise. Generally the MNCC system has been providing better performance as compared to GSM.

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.

Reduced hemispheric asymmetry of brain anatomical networks in attention deficit hyperactivity disorder.

PubMed

Li, Dandan; Li, Ting; Niu, Yan; Xiang, Jie; Cao, Rui; Liu, Bo; Zhang, Hui; Wang, Bin

2018-05-11

Despite many studies reporting a variety of alterations in brain networks in patients with attention deficit hyperactivity disorder (ADHD), alterations in hemispheric anatomical networks are still unclear. In this study, we investigated topology alterations in hemispheric white matter in patients with ADHD and the relationship between these alterations and clinical features of the illness. Weighted hemispheric brain anatomical networks were first constructed for each of 40 right-handed patients with ADHD and 53 matched normal controls. Then, graph theoretical approaches were utilized to compute hemispheric topological properties. The small-world property was preserved in the hemispheric network. Furthermore, a significant group-by-hemisphere interaction was revealed in global efficiency, local efficiency and characteristic path length, attributed to the significantly reduced hemispheric asymmetry of global and local integration in patients with ADHD compared with normal controls. Specifically, reduced asymmetric regional efficiency was found in three regions. Finally, we found that the abnormal asymmetry of hemispheric brain anatomical network topology and regional efficiency were both associated with clinical features (the Adult ADHD Self-Report Scale and Wechsler Adult Intelligence Scale) in patients. Our findings provide new insights into the lateralized nature of hemispheric dysconnectivity and highlight the potential for using brain network measures of hemispheric asymmetry as neural biomarkers for ADHD and its clinical features.

The contribution of glacier melt to streamflow

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

Schaner, Neil; Voisin, Nathalie; Nijssen, Bart

2012-09-13

Ongoing and projected future changes in glacier extent and water storage globally have lead to concerns about the implications for water supplies. However, the current magnitude of glacier contributions to river runoff is not well known, nor is the population at risk to future glacier changes. We estimate an upper bound on glacier melt contribution to seasonal streamflow by computing the energy balance of glaciers globally. Melt water quantities are computed as a fraction of total streamflow simulated using a hydrology model and the melt fraction is tracked down the stream network. In general, our estimates of the glacier meltmore » contribution to streamflow are lower than previously published values. Nonetheless, we find that globally an estimated 225 (36) million people live in river basins where maximum seasonal glacier melt contributes at least 10% (25%) of streamflow, mostly in the High Asia region.« less

Observing the Global Water Cycle from Space

NASA Technical Reports Server (NTRS)

Hildebrand, P. H.

2004-01-01

This paper presents an approach to measuring all major components of the water cycle from space. Key elements of the global water cycle are discussed in terms of the storage of water-in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers, and in terms of the global fluxes of water between these reservoirs. Approaches to measuring or otherwise evaluating the global water cycle are presented, and the limitations on known accuracy for many components of the water cycle are discussed, as are the characteristic spatial and temporal scales of the different water cycle components. Using these observational requirements for a global water cycle observing system, an approach to measuring the global water cycle from space is developed. The capabilities of various active and passive microwave instruments are discussed, as is the potential of supporting measurements from other sources. Examples of space observational systems, including TRMM/GPM precipitation measurement, cloud radars, soil moisture, sea surface salinity, temperature and humidity profiling, other measurement approaches and assimilation of the microwave and other data into interpretative computer models are discussed to develop the observational possibilities. The selection of orbits is then addressed, for orbit selection and antenna size/beamwidth considerations determine the sampling characteristics for satellite measurement systems. These considerations dictate a particular set of measurement possibilities, which are then matched to the observational sampling requirements based on the science. The results define a network of satellite instrumentation systems, many in low Earth orbit, a few in geostationary orbit, and all tied together through a sampling network that feeds the observations into a data-assimilative computer model.

Deep hierarchical attention network for video description

NASA Astrophysics Data System (ADS)

Li, Shuohao; Tang, Min; Zhang, Jun

2018-03-01

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

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

NCSTRL: Design and Deployment of a Globally Distributed Digital Library.

ERIC Educational Resources Information Center

Davies, James R.; Lagoze, Carl

2000-01-01

Discusses the development of a digital library architecture that allows the creation of digital libraries within the World Wide Web. Describes a digital library, NCSTRL (Networked Computer Science Technical Research Library), within which the work has taken place and explains Dienst, a protocol and architecture for distributed digital libraries.…

Advocating Global Forest Issues on the Internet.

ERIC Educational Resources Information Center

Kempf, Alois

Sustainable development, biological diversity and conservation of tropical forests are only a few of the hot environmental and political topics where the actors involved have started to make use of the world-wide computer networks. The Internet (as a transport medium of information exchange) and the World Wide Web (as the favorite service to…

A Travel Agent in Cyber School: The Internet and the Library Media Program.

ERIC Educational Resources Information Center

LeBaron, John F.; And Others

The global computing networks that are revolutionizing our society have created an opportunity for school libraries and librarians. Taking the position that librarians occupy key positions in the educational technology revolution, this book explores how technology-enhanced education improvements fit together with the library media program and how…

Codifying a Next-Generation Education System: New York City iSchool

ERIC Educational Resources Information Center

Education Development Center, Inc, 2009

2009-01-01

The world outside schools is changing rapidly with the advances of technology and economic requirements for a 21st-century global citizenry. Today, technology has moved into one's everyday life and is becoming a pervasive part of how one works, learns, and plays. Similarly, networked communications and computer technology have transformed the…

The Genie Is Out of the Bottle

ERIC Educational Resources Information Center

Katz, Richard N.

2004-01-01

Starting in the late 1960s, data networks were created to connect supercomputers and, later, other intelligent devices. A revolution in communications was in the making. By the late 1970s, computing and communications technologies were leading us from a world of local markets trading in capital goods to one of global markets trading in capital…

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

PubMed Central

Kraft, Reuben H.; Mckee, Phillip Justin; Dagro, Amy M.; Grafton, Scott T.

2012-01-01

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the “damaged” network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times () network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight. PMID:22915997

Tensor network states and algorithms in the presence of a global SU(2) symmetry

NASA Astrophysics Data System (ADS)

Singh, Sukhwinder; Vidal, Guifre

2012-11-01

The benefits of exploiting the presence of symmetries in tensor network algorithms have been extensively demonstrated in the context of matrix product states (MPSs). These include the ability to select a specific symmetry sector (e.g., with a given particle number or spin), to ensure the exact preservation of total charge, and to significantly reduce computational costs. Compared to the case of a generic tensor network, the practical implementation of symmetries in the MPS is simplified by the fact that tensors only have three indices (they are trivalent, just as the Clebsch-Gordan coefficients of the symmetry group) and are organized as a one-dimensional array of tensors, without closed loops. Instead, a more complex tensor network, one where tensors have a larger number of indices and/or a more elaborate network structure, requires a more general treatment. In two recent papers, namely, (i) [Singh, Pfeifer, and Vidal, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.82.050301 82, 050301 (2010)] and (ii) [Singh, Pfeifer, and Vidal, Phys. Rev. BPRBMDO1098-012110.1103/PhysRevB.83.115125 83, 115125 (2011)], we described how to incorporate a global internal symmetry into a generic tensor network algorithm based on decomposing and manipulating tensors that are invariant under the symmetry. In (i) we considered a generic symmetry group G that is compact, completely reducible, and multiplicity free, acting as a global internal symmetry. Then, in (ii) we described the implementation of Abelian group symmetries in much more detail, considering a U(1) symmetry (e.g., conservation of global particle number) as a concrete example. In this paper, we describe the implementation of non-Abelian group symmetries in great detail. For concreteness, we consider an SU(2) symmetry (e.g., conservation of global quantum spin). Our formalism can be readily extended to more exotic symmetries associated with conservation of total fermionic or anyonic charge. As a practical demonstration, we describe the SU(2)-invariant version of the multiscale entanglement renormalization ansatz and apply it to study the low-energy spectrum of a quantum spin chain with a global SU(2) symmetry.

Buffered coscheduling for parallel programming and enhanced fault tolerance

DOEpatents

Petrini, Fabrizio [Los Alamos, NM; Feng, Wu-chun [Los Alamos, NM

2006-01-31

A computer implemented method schedules processor jobs on a network of parallel machine processors or distributed system processors. Control information communications generated by each process performed by each processor during a defined time interval is accumulated in buffers, where adjacent time intervals are separated by strobe intervals for a global exchange of control information. A global exchange of the control information communications at the end of each defined time interval is performed during an intervening strobe interval so that each processor is informed by all of the other processors of the number of incoming jobs to be received by each processor in a subsequent time interval. The buffered coscheduling method of this invention also enhances the fault tolerance of a network of parallel machine processors or distributed system processors

A network approach to decentralized coordination of energy production-consumption grids.

PubMed

Omodei, Elisa; Arenas, Alex

2018-01-01

Energy grids are facing a relatively new paradigm consisting in the formation of local distributed energy sources and loads that can operate in parallel independently from the main power grid (usually called microgrids). One of the main challenges in microgrid-like networks management is that of self-adapting to the production and demands in a decentralized coordinated way. Here, we propose a stylized model that allows to analytically predict the coordination of the elements in the network, depending on the network topology. Surprisingly, almost global coordination is attained when users interact locally, with a small neighborhood, instead of the obvious but more costly all-to-all coordination. We compute analytically the optimal value of coordinated users in random homogeneous networks. The methodology proposed opens a new way of confronting the analysis of energy demand-side management in networked systems.

A wireless sensor network based personnel positioning scheme in coal mines with blind areas.

PubMed

Liu, Zhigao; Li, Chunwen; Wu, Danchen; Dai, Wenhan; Geng, Shaobo; Ding, Qingqing

2010-01-01

This paper proposes a novel personnel positioning scheme for a tunnel network with blind areas, which compared with most existing schemes offers both low-cost and high-precision. Based on the data models of tunnel networks, measurement networks and mobile miners, the global positioning method is divided into four steps: (1) calculate the real time personnel location in local areas using a location engine, and send it to the upper computer through the gateway; (2) correct any localization errors resulting from the underground tunnel environmental interference; (3) determine the global three-dimensional position by coordinate transformation; (4) estimate the personnel locations in the blind areas. A prototype system constructed to verify the positioning performance shows that the proposed positioning system has good reliability, scalability, and positioning performance. In particular, the static localization error of the positioning system is less than 2.4 m in the underground tunnel environment and the moving estimation error is below 4.5 m in the corridor environment. The system was operated continuously over three months without any failures.

A Wireless Sensor Network Based Personnel Positioning Scheme in Coal Mines with Blind Areas

PubMed Central

Liu, Zhigao; Li, Chunwen; Wu, Danchen; Dai, Wenhan; Geng, Shaobo; Ding, Qingqing

2010-01-01

This paper proposes a novel personnel positioning scheme for a tunnel network with blind areas, which compared with most existing schemes offers both low-cost and high-precision. Based on the data models of tunnel networks, measurement networks and mobile miners, the global positioning method is divided into four steps: (1) calculate the real time personnel location in local areas using a location engine, and send it to the upper computer through the gateway; (2) correct any localization errors resulting from the underground tunnel environmental interference; (3) determine the global three-dimensional position by coordinate transformation; (4) estimate the personnel locations in the blind areas. A prototype system constructed to verify the positioning performance shows that the proposed positioning system has good reliability, scalability, and positioning performance. In particular, the static localization error of the positioning system is less than 2.4 m in the underground tunnel environment and the moving estimation error is below 4.5 m in the corridor environment. The system was operated continuously over three months without any failures. PMID:22163446

Examination of China’s performance and thematic evolution in quantum cryptography research using quantitative and computational techniques

PubMed Central

2018-01-01

This study performed two phases of analysis to shed light on the performance and thematic evolution of China’s quantum cryptography (QC) research. First, large-scale research publication metadata derived from QC research published from 2001–2017 was used to examine the research performance of China relative to that of global peers using established quantitative and qualitative measures. Second, this study identified the thematic evolution of China’s QC research using co-word cluster network analysis, a computational science mapping technique. The results from the first phase indicate that over the past 17 years, China’s performance has evolved dramatically, placing it in a leading position. Among the most significant findings is the exponential rate at which all of China’s performance indicators (i.e., Publication Frequency, citation score, H-index) are growing. China’s H-index (a normalized indicator) has surpassed all other countries’ over the last several years. The second phase of analysis shows how China’s main research focus has shifted among several QC themes, including quantum-key-distribution, photon-optical communication, network protocols, and quantum entanglement with an emphasis on applied research. Several themes were observed across time periods (e.g., photons, quantum-key-distribution, secret-messages, quantum-optics, quantum-signatures); some themes disappeared over time (e.g., computer-networks, attack-strategies, bell-state, polarization-state), while others emerged more recently (e.g., quantum-entanglement, decoy-state, unitary-operation). Findings from the first phase of analysis provide empirical evidence that China has emerged as the global driving force in QC. Considering China is the premier driving force in global QC research, findings from the second phase of analysis provide an understanding of China’s QC research themes, which can provide clarity into how QC technologies might take shape. QC and science and technology policy researchers can also use these findings to trace previous research directions and plan future lines of research. PMID:29385151

Examination of China's performance and thematic evolution in quantum cryptography research using quantitative and computational techniques.

PubMed

Olijnyk, Nicholas V

2018-01-01

This study performed two phases of analysis to shed light on the performance and thematic evolution of China's quantum cryptography (QC) research. First, large-scale research publication metadata derived from QC research published from 2001-2017 was used to examine the research performance of China relative to that of global peers using established quantitative and qualitative measures. Second, this study identified the thematic evolution of China's QC research using co-word cluster network analysis, a computational science mapping technique. The results from the first phase indicate that over the past 17 years, China's performance has evolved dramatically, placing it in a leading position. Among the most significant findings is the exponential rate at which all of China's performance indicators (i.e., Publication Frequency, citation score, H-index) are growing. China's H-index (a normalized indicator) has surpassed all other countries' over the last several years. The second phase of analysis shows how China's main research focus has shifted among several QC themes, including quantum-key-distribution, photon-optical communication, network protocols, and quantum entanglement with an emphasis on applied research. Several themes were observed across time periods (e.g., photons, quantum-key-distribution, secret-messages, quantum-optics, quantum-signatures); some themes disappeared over time (e.g., computer-networks, attack-strategies, bell-state, polarization-state), while others emerged more recently (e.g., quantum-entanglement, decoy-state, unitary-operation). Findings from the first phase of analysis provide empirical evidence that China has emerged as the global driving force in QC. Considering China is the premier driving force in global QC research, findings from the second phase of analysis provide an understanding of China's QC research themes, which can provide clarity into how QC technologies might take shape. QC and science and technology policy researchers can also use these findings to trace previous research directions and plan future lines of research.

Biology Inspired Approach for Communal Behavior in Sensor Networks

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2006-01-01

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster.

PubMed

Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S

2017-05-28

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au 147 ), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au 147 , and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au 147 is performed, and it is concluded that Au 147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster

NASA Astrophysics Data System (ADS)

Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S.

2017-05-01

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au147), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au147, and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au147 is performed, and it is concluded that Au147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric.

PubMed

Lee, Hyekyoung; Chung, Moo K; Kang, Hyejin; Kim, Boong-Nyun; Lee, Dong Soo

2011-01-01

The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.

Lifespan anxiety is reflected in human amygdala cortical connectivity

PubMed Central

He, Ye; Xu, Ting; Zhang, Wei

2016-01-01

Abstract The amygdala plays a pivotal role in processing anxiety and connects to large‐scale brain networks. However, intrinsic functional connectivity (iFC) between amygdala and these networks has rarely been examined in relation to anxiety, especially across the lifespan. We employed resting‐state functional MRI data from 280 healthy adults (18–83.5 yrs) to elucidate the relationship between anxiety and amygdala iFC with common cortical networks including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default network. Global and network‐specific iFC were separately computed as mean iFC of amygdala with the entire cerebral cortex and each cortical network. We detected negative correlation between global positive amygdala iFC and trait anxiety. Network‐specific associations between amygdala iFC and anxiety were also detectable. Specifically, the higher iFC strength between the left amygdala and the limbic network predicted lower state anxiety. For the trait anxiety, left amygdala anxiety–connectivity correlation was observed in both somatomotor and dorsal attention networks, whereas the right amygdala anxiety–connectivity correlation was primarily distributed in the frontoparietal and ventral attention networks. Ventral attention network exhibited significant anxiety–gender interactions on its iFC with amygdala. Together with findings from additional vertex‐wise analysis, these data clearly indicated that both low‐level sensory networks and high‐level associative networks could contribute to detectable predictions of anxiety behaviors by their iFC profiles with the amygdala. This set of systems neuroscience findings could lead to novel functional network models on neural correlates of human anxiety and provide targets for novel treatment strategies on anxiety disorders. Hum Brain Mapp 37:1178–1193, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:26859312

Proactive health computing.

PubMed

Timpka, T

2001-08-01

In an analysis departing from the global health situation, the foundation for a change of paradigm in health informatics based on socially embedded information infrastructures and technologies is identified and discussed. It is shown how an increasing computing and data transmitting capacity can be employed for proactive health computing. As a foundation for ubiquitous health promotion and prevention of disease and injury, proactive health systems use data from multiple sources to supply individuals and communities evidence-based information on means to improve their state of health and avoid health risks. The systems are characterised by: (1) being profusely connected to the world around them, using perceptual interfaces, sensors and actuators; (2) responding to external stimuli at faster than human speeds; (3) networked feedback loops; and (4) humans remaining in control, while being left outside the primary computing loop. The extended scientific mission of this new partnership between computer science, electrical engineering and social medicine is suggested to be the investigation of how the dissemination of information and communication technology on democratic grounds can be made even more important for global health than sanitation and urban planning became a century ago.

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

NASA Astrophysics Data System (ADS)

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae-Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee-Hyun; Lee, Joseph; Mashour, George A.; Lee, Uncheol

2017-04-01

Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks.

Altered gray matter organization in children and adolescents with ADHD: a structural covariance connectome study

PubMed Central

Griffiths, K R; Grieve, S M; Kohn, M R; Clarke, S; Williams, L M; Korgaonkar, M S

2016-01-01

Although multiple studies have reported structural deficits in multiple brain regions in attention-deficit hyperactivity disorder (ADHD), we do not yet know if these deficits reflect a more systematic disruption to the anatomical organization of large-scale brain networks. Here we used a graph theoretical approach to quantify anatomical organization in children and adolescents with ADHD. We generated anatomical networks based on covariance of gray matter volumes from 92 regions across the brain in children and adolescents with ADHD (n=34) and age- and sex-matched healthy controls (n=28). Using graph theory, we computed metrics that characterize both the global organization of anatomical networks (interconnectivity (clustering), integration (path length) and balance of global integration and localized segregation (small-worldness)) and their local nodal measures (participation (degree) and interaction (betweenness) within a network). Relative to Controls, ADHD participants exhibited altered global organization reflected in more clustering or network segregation. Locally, nodal degree and betweenness were increased in the subcortical amygdalae in ADHD, but reduced in cortical nodes in the anterior cingulate, posterior cingulate, mid temporal pole and rolandic operculum. In ADHD, anatomical networks were disrupted and reflected an emphasis on subcortical local connections centered around the amygdala, at the expense of cortical organization. Brains of children and adolescents with ADHD may be anatomically configured to respond impulsively to the automatic significance of stimulus input without having the neural organization to regulate and inhibit these responses. These findings provide a novel addition to our current understanding of the ADHD connectome. PMID:27824356

Low Temperature Performance of High-Speed Neural Network Circuits

NASA Technical Reports Server (NTRS)

Duong, T.; Tran, M.; Daud, T.; Thakoor, A.

1995-01-01

Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.

Deterministic and stochastic algorithms for resolving the flow fields in ducts and networks using energy minimization

NASA Astrophysics Data System (ADS)

Sochi, Taha

2016-09-01

Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton and global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel-Bulkley, Ellis, Ree-Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of computational fluid dynamics for solving the flow fields in tubes and networks for various types of Newtonian and non-Newtonian fluids.

A more randomly organized grey matter network is associated with deteriorating language and global cognition in individuals with subjective cognitive decline.

PubMed

Verfaillie, Sander C J; Slot, Rosalinde E R; Dicks, Ellen; Prins, Niels D; Overbeek, Jozefien M; Teunissen, Charlotte E; Scheltens, Philip; Barkhof, Frederik; van der Flier, Wiesje M; Tijms, Betty M

2018-03-30

Grey matter network disruptions in Alzheimer's disease (AD) are associated with worse cognitive impairment cross-sectionally. Our aim was to investigate whether indications of a more random network organization are associated with longitudinal decline in specific cognitive functions in individuals with subjective cognitive decline (SCD). We included 231 individuals with SCD who had annually repeated neuropsychological assessment (3 ± 1 years; n = 646 neuropsychological investigations) available from the Amsterdam Dementia Cohort (54% male, age: 63 ± 9, MMSE: 28 ± 2). Single-subject grey matter networks were extracted from baseline 3D-T1 MRI scans and we computed basic network (size, degree, connectivity density) and higher-order (path length, clustering, betweenness centrality, normalized path length [lambda] and normalized clustering [gamma]) parameters at whole brain and/or regional levels. We tested associations of network parameters with baseline and annual cognition (memory, attention, executive functioning, language composite scores, and global cognition [all domains with MMSE]) using linear mixed models, adjusted for age, sex, education, scanner and total gray matter volume. Lower network size was associated with steeper decline in language (β ± SE = 0.12 ± 0.05, p < 0.05FDR). Higher-order network parameters showed no cross-sectional associations. Lower gamma and lambda values were associated with steeper decline in global cognition (gamma: β ± SE = 0.06 ± 0.02); lambda: β ± SE = 0.06 ± 0.02), language (gamma: β ± SE = 0.11 ± 0.04; lambda: β ± SE = 0.12 ± 0.05; all p < 0.05FDR). Lower path length values in precuneus and fronto-temporo-occipital cortices were associated with a steeper decline in global cognition. A more randomly organized grey matter network was associated with a steeper decline of cognitive functioning, possibly indicating the start of cognitive impairment. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Integration, Networking, and Global Biobanking in the Age of New Biology.

PubMed

Karimi-Busheri, Feridoun; Rasouli-Nia, Aghdass

2015-01-01

Scientific revolution is changing the world forever. Many new disciplines and fields have emerged with unlimited possibilities and opportunities. Biobanking is one of many that is benefiting from revolutionary milestones in human genome, post-genomic, and computer and bioinformatics discoveries. The storage, management, and analysis of massive clinical and biological data sets cannot be achieved without a global collaboration and networking. At the same time, biobanking is facing many significant challenges that need to be addressed and solved including dealing with an ever increasing complexity of sample storage and retrieval, data management and integration, and establishing common platforms in a global context. The overall picture of the biobanking of the future, however, is promising. Many population-based biobanks have been formed, and more are under development. It is certain that amazing discoveries will emerge from this large-scale method of preserving and accessing human samples. Signs of a healthy collaboration between industry, academy, and government are encouraging.

Effecting a broadcast with an allreduce operation on a parallel computer

DOEpatents

Almasi, Gheorghe; Archer, Charles J.; Ratterman, Joseph D.; Smith, Brian E.

2010-11-02

A parallel computer comprises a plurality of compute nodes organized into at least one operational group for collective parallel operations. Each compute node is assigned a unique rank and is coupled for data communications through a global combining network. One compute node is assigned to be a logical root. A send buffer and a receive buffer is configured. Each element of a contribution of the logical root in the send buffer is contributed. One or more zeros corresponding to a size of the element are injected. An allreduce operation with a bitwise OR using the element and the injected zeros is performed. And the result for the allreduce operation is determined and stored in each receive buffer.

Technical Assessment: Integrated Photonics

DTIC Science & Technology

2015-10-01

in global internet protocol traffic as a function of time by local access technology. Photonics continues to play a critical role in enabling this...communication networks. This has enabled services like the internet , high performance computing, and power-efficient large-scale data centers. The...signal processing, quantum information science, and optics for free space applications. However major obstacles challenge the implementation of

Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks

USGS Publications Warehouse

Karanovic, Marinko; Muffels, Christopher T.; Tonkin, Matthew J.; Hunt, Randall J.

2012-01-01

Models of environmental systems have become increasingly complex, incorporating increasingly large numbers of parameters in an effort to represent physical processes on a scale approaching that at which they occur in nature. Consequently, the inverse problem of parameter estimation (specifically, model calibration) and subsequent uncertainty analysis have become increasingly computation-intensive endeavors. Fortunately, advances in computing have made computational power equivalent to that of dozens to hundreds of desktop computers accessible through a variety of alternate means: modelers have various possibilities, ranging from traditional Local Area Networks (LANs) to cloud computing. Commonly used parameter estimation software is well suited to take advantage of the availability of such increased computing power. Unfortunately, logistical issues become increasingly important as an increasing number and variety of computers are brought to bear on the inverse problem. To facilitate efficient access to disparate computer resources, the PESTCommander program documented herein has been developed to provide a Graphical User Interface (GUI) that facilitates the management of model files ("file management") and remote launching and termination of "slave" computers across a distributed network of computers ("run management"). In version 1.0 described here, PESTCommander can access and ascertain resources across traditional Windows LANs: however, the architecture of PESTCommander has been developed with the intent that future releases will be able to access computing resources (1) via trusted domains established in Wide Area Networks (WANs) in multiple remote locations and (2) via heterogeneous networks of Windows- and Unix-based operating systems. The design of PESTCommander also makes it suitable for extension to other computational resources, such as those that are available via cloud computing. Version 1.0 of PESTCommander was developed primarily to work with the parameter estimation software PEST; the discussion presented in this report focuses on the use of the PESTCommander together with Parallel PEST. However, PESTCommander can be used with a wide variety of programs and models that require management, distribution, and cleanup of files before or after model execution. In addition to its use with the Parallel PEST program suite, discussion is also included in this report regarding the use of PESTCommander with the Global Run Manager GENIE, which was developed simultaneously with PESTCommander.

Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading - An approach based on multiplex networks

NASA Astrophysics Data System (ADS)

Kan, Jia-Qian; Zhang, Hai-Feng

2017-03-01

In this paper, we study the interplay between the epidemic spreading and the diffusion of awareness in multiplex networks. In the model, an infectious disease can spread in one network representing the paths of epidemic spreading (contact network), leading to the diffusion of awareness in the other network (information network), and then the diffusion of awareness will cause individuals to take social distances, which in turn affects the epidemic spreading. As for the diffusion of awareness, we assume that, on the one hand, individuals can be informed by other aware neighbors in information network, on the other hand, the susceptible individuals can be self-awareness induced by the infected neighbors in the contact networks (local information) or mass media (global information). Through Markov chain approach and numerical computations, we find that the density of infected individuals and the epidemic threshold can be affected by the structures of the two networks and the effective transmission rate of the awareness. However, we prove that though the introduction of the self-awareness can lower the density of infection, which cannot increase the epidemic threshold no matter of the local information or global information. Our finding is remarkably different to many previous results on single-layer network: local information based behavioral response can alter the epidemic threshold. Furthermore, our results indicate that the nodes with more neighbors (hub nodes) in information networks are easier to be informed, as a result, their risk of infection in contact networks can be effectively reduced.

Empirical comparison of heuristic load distribution in point-to-point multicomputer networks

NASA Technical Reports Server (NTRS)

Grunwald, Dirk C.; Nazief, Bobby A. A.; Reed, Daniel A.

1990-01-01

The study compared several load placement algorithms using instrumented programs and synthetic program models. Salient characteristics of these program traces (total computation time, total number of messages sent, and average message time) span two orders of magnitude. Load distribution algorithms determine the initial placement for processes, a precursor to the more general problem of load redistribution. It is found that desirable workload distribution strategies will place new processes globally, rather than locally, to spread processes rapidly, but that local information should be used to refine global placement.

Non-dynamic decimeter tracking of earth satellites using the Global Positioning System

NASA Technical Reports Server (NTRS)

Yunck, T. P.; Wu, S. C.

1986-01-01

A technique is described for employing the Global Positioning System (GPS) to determine the position of a low earth orbiter with decimeter accuracy without the need for user dynamic models. A differential observing strategy is used requiring a GPS receiver on the user vehicle and a network of six ground receivers. The technique uses the continuous record of position change obtained from GPS carrier phase to smooth position measurements made with pseudo-range. The result is a computationally efficient technique that can deliver decimeter accuracy down to the lowest altitude orbits.

Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications

PubMed Central

Blacklock, Kristin; Verkhivker, Gennady M.

2014-01-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks. PMID:24922508

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications.

PubMed

Blacklock, Kristin; Verkhivker, Gennady M

2014-06-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

Epidemic modeling in complex realities.

PubMed

Colizza, Vittoria; Barthélemy, Marc; Barrat, Alain; Vespignani, Alessandro

2007-04-01

In our global world, the increasing complexity of social relations and transport infrastructures are key factors in the spread of epidemics. In recent years, the increasing availability of computer power has enabled both to obtain reliable data allowing one to quantify the complexity of the networks on which epidemics may propagate and to envision computational tools able to tackle the analysis of such propagation phenomena. These advances have put in evidence the limits of homogeneous assumptions and simple spatial diffusion approaches, and stimulated the inclusion of complex features and heterogeneities relevant in the description of epidemic diffusion. In this paper, we review recent progresses that integrate complex systems and networks analysis with epidemic modelling and focus on the impact of the various complex features of real systems on the dynamics of epidemic spreading.

The internet of things and the development of network technology in China

NASA Astrophysics Data System (ADS)

Wang, Ruxin; Zhao, Jianzhen; Ma, Hangtong

2018-04-01

The English name of the Internet of Things the Internet of Things, referred to as: the IOT. Internet of Things through the pass, radio frequency identification technology, global positioning system technology, real-time acquisition of any monitoring, connectivity, interactive objects or processes, collecting their sound, light, heat, electricity, mechanics, chemistry, biology, the location of a variety of the information you need network access through a variety of possible things and things, objects and people in the Pan-link intelligent perception of items and processes, identification and management. The Internet of Things IntelliSense recognition technology and pervasive computing, ubiquitous network integration application, known as the third wave of the world's information industry development following the computer, the Internet. Not so much the Internet of Things is a network, as Internet of Things services and applications, Internet of Things is also seen as Internet application development. Therefore, the application of innovation is the core of the development of Internet of Things, and 2.0 of the user experience as the core innovation is the soul of Things.

Applying differential dynamic logic to reconfigurable biological networks.

PubMed

Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

2017-09-01

Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

Enabling Research Network Connectivity to Clouds with Virtual Router Technology

NASA Astrophysics Data System (ADS)

Seuster, R.; Casteels, K.; Leavett-Brown, CR; Paterson, M.; Sobie, RJ

2017-10-01

The use of opportunistic cloud resources by HEP experiments has significantly increased over the past few years. Clouds that are owned or managed by the HEP community are connected to the LHCONE network or the research network with global access to HEP computing resources. Private clouds, such as those supported by non-HEP research funds are generally connected to the international research network; however, commercial clouds are either not connected to the research network or only connect to research sites within their national boundaries. Since research network connectivity is a requirement for HEP applications, we need to find a solution that provides a high-speed connection. We are studying a solution with a virtual router that will address the use case when a commercial cloud has research network connectivity in a limited region. In this situation, we host a virtual router in our HEP site and require that all traffic from the commercial site transit through the virtual router. Although this may increase the network path and also the load on the HEP site, it is a workable solution that would enable the use of the remote cloud for low I/O applications. We are exploring some simple open-source solutions. In this paper, we present the results of our studies and how it will benefit our use of private and public clouds for HEP computing.

Global information infrastructure.

PubMed

Lindberg, D A

1994-01-01

The High Performance Computing and Communications Program (HPCC) is a multiagency federal initiative under the leadership of the White House Office of Science and Technology Policy, established by the High Performance Computing Act of 1991. It has been assigned a critical role in supporting the international collaboration essential to science and to health care. Goals of the HPCC are to extend USA leadership in high performance computing and networking technologies; to improve technology transfer for economic competitiveness, education, and national security; and to provide a key part of the foundation for the National Information Infrastructure. The first component of the National Institutes of Health to participate in the HPCC, the National Library of Medicine (NLM), recently issued a solicitation for proposals to address a range of issues, from privacy to 'testbed' networks, 'virtual reality,' and more. These efforts will build upon the NLM's extensive outreach program and other initiatives, including the Unified Medical Language System (UMLS), MEDLARS, and Grateful Med. New Internet search tools are emerging, such as Gopher and 'Knowbots'. Medicine will succeed in developing future intelligent agents to assist in utilizing computer networks. Our ability to serve patients is so often restricted by lack of information and knowledge at the time and place of medical decision-making. The new technologies, properly employed, will also greatly enhance our ability to serve the patient.

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

PubMed Central

Guo, Wenzhong; Xiong, Naixue; Chao, Han-Chieh; Hussain, Sajid; Chen, Guolong

2011-01-01

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms. PMID:22163971

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

A network approach to decentralized coordination of energy production-consumption grids

PubMed Central

Arenas, Alex

2018-01-01

Energy grids are facing a relatively new paradigm consisting in the formation of local distributed energy sources and loads that can operate in parallel independently from the main power grid (usually called microgrids). One of the main challenges in microgrid-like networks management is that of self-adapting to the production and demands in a decentralized coordinated way. Here, we propose a stylized model that allows to analytically predict the coordination of the elements in the network, depending on the network topology. Surprisingly, almost global coordination is attained when users interact locally, with a small neighborhood, instead of the obvious but more costly all-to-all coordination. We compute analytically the optimal value of coordinated users in random homogeneous networks. The methodology proposed opens a new way of confronting the analysis of energy demand-side management in networked systems. PMID:29364962

Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

NASA Astrophysics Data System (ADS)

Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen

2013-08-01

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

The DYNES Instrument: A Description and Overview

NASA Astrophysics Data System (ADS)

Zurawski, Jason; Ball, Robert; Barczyk, Artur; Binkley, Mathew; Boote, Jeff; Boyd, Eric; Brown, Aaron; Brown, Robert; Lehman, Tom; McKee, Shawn; Meekhof, Benjeman; Mughal, Azher; Newman, Harvey; Rozsa, Sandor; Sheldon, Paul; Tackett, Alan; Voicu, Ramiro; Wolff, Stephen; Yang, Xi

2012-12-01

Scientific innovation continues to increase requirements for the computing and networking infrastructures of the world. Collaborative partners, instrumentation, storage, and processing facilities are often geographically and topologically separated, as is the case with LHC virtual organizations. These separations challenge the technology used to interconnect available resources, often delivered by Research and Education (R&E) networking providers, and leads to complications in the overall process of end-to-end data management. Capacity and traffic management are key concerns of R&E network operators; a delicate balance is required to serve both long-lived, high capacity network flows, as well as more traditional end-user activities. The advent of dynamic circuit services, a technology that enables the creation of variable duration, guaranteed bandwidth networking channels, allows for the efficient use of common network infrastructures. These gains are seen particularly in locations where overall capacity is scarce compared to the (sustained peak) needs of user communities. Related efforts, including those of the LHCOPN [3] operations group and the emerging LHCONE [4] project, may take advantage of available resources by designating specific network activities as a “high priority”, allowing reservation of dedicated bandwidth or optimizing for deadline scheduling and predicable delivery patterns. This paper presents the DYNES instrument, an NSF funded cyberinfrastructure project designed to facilitate end-to-end dynamic circuit services [2]. This combination of hardware and software innovation is being deployed across R&E networks in the United States at selected end-sites located on University Campuses. DYNES is peering with international efforts in other countries using similar solutions, and is increasing the reach of this emerging technology. This global data movement solution could be integrated into computing paradigms such as cloud and grid computing platforms, and through the use of APIs can be integrated into existing data movement software.

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

PubMed Central

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P.; Gerstein, Mark

2010-01-01

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems. PMID:20439753

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks.

PubMed

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P; Gerstein, Mark

2010-05-18

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers' continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

Alignment and integration of complex networks by hypergraph-based spectral clustering

NASA Astrophysics Data System (ADS)

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Alignment and integration of complex networks by hypergraph-based spectral clustering.

PubMed

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Reconstructing cerebrovascular networks under local physiological constraints by integer programming

DOE PAGES

Rempfler, Markus; Schneider, Matthias; Ielacqua, Giovanna D.; ...

2015-04-23

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to the probabilistic model. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (µCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of ourmore » probabilistic model. As a result, we perform experiments on micro magnetic resonance angiography (µMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.« less

Quantitative evaluation of simulated functional brain networks in graph theoretical analysis.

PubMed

Lee, Won Hee; Bullmore, Ed; Frangou, Sophia

2017-02-01

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

How Data Becomes Physics: Inside the RACF

ScienceCinema

Ernst, Michael; Rind, Ofer; Rajagopalan, Srini; Lauret, Jerome; Pinkenburg, Chris

2018-06-22

The RHIC & ATLAS Computing Facility (RACF) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory sits at the center of a global computing network. It connects more than 2,500 researchers around the world with the data generated by millions of particle collisions taking place each second at Brookhaven Lab's Relativistic Heavy Ion Collider (RHIC, a DOE Office of Science User Facility for nuclear physics research), and the ATLAS experiment at the Large Hadron Collider in Europe. Watch this video to learn how the people and computing resources of the RACF serve these scientists to turn petabytes of raw data into physics discoveries.

Analysis hierarchical model for discrete event systems

NASA Astrophysics Data System (ADS)

Ciortea, E. M.

2015-11-01

The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Carter, David; Wetzel, Scott

2000-01-01

The NASA SLR Operational Center is responsible for: 1) NASA SLR network control, sustaining engineering, and logistics; 2) ILRS mission operations; and 3) ILRS and NASA SLR data operations. NASA SLR network control and sustaining engineering tasks include technical support, daily system performance monitoring, system scheduling, operator training, station status reporting, system relocation, logistics and support of the ILRS Networks and Engineering Working Group. These activities ensure the NASA SLR systems are meeting ILRS and NASA mission support requirements. ILRS mission operations tasks include mission planning, mission analysis, mission coordination, development of mission support plans, and support of the ILRS Missions Working Group. These activities ensure than new mission and campaign requirements are coordinated with the ILRS. Global Normal Points (NP) data, NASA SLR FullRate (FR) data, and satellite predictions are managed as part of data operations. Part of this operation includes supporting the ILRS Data Formats and Procedures Working Group. Global NP data operations consist of receipt, format and data integrity verification, archiving and merging. This activity culminates in the daily electronic transmission of NP files to the CDDIS. Currently of all these functions are automated. However, to ensure the timely and accurate flow of data, regular monitoring and maintenance of the operational software systems, computer systems and computer networking are performed. Tracking statistics between the stations and the data centers are compared periodically to eliminate lost data. Future activities in this area include sub-daily (i.e., hourly) NP data management, more stringent data integrity tests, and automatic station notification of format and data integrity issues.

Algorithm for Training a Recurrent Multilayer Perceptron

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Rais, Omar T.; Menon, Sunil K.; Atiya, Amir F.

2004-01-01

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

A Comparison Between Heliosat-2 and Artificial Neural Network Methods for Global Horizontal Irradiance Retrievals over Desert Environments

NASA Astrophysics Data System (ADS)

Ghedira, H.; Eissa, Y.

2012-12-01

Global horizontal irradiance (GHI) retrievals at the surface of any given location could be used for preliminary solar resource assessments. More accurately, the direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) are also required to estimate the global tilt irradiance, mainly used for fixed flat plate collectors. Two different satellite-based models for solar irradiance retrievals have been applied over the desert environment of the United Arab Emirates (UAE). Both models employ channels of the SEVIRI instrument, onboard the geostationary satellite Meteosat Second Generation, as their main inputs. The satellite images used in this study have a temporal resolution of 15-min and a spatial resolution of 3-km. The objective of this study is to compare between the GHI retrieved using the Heliosat-2 method and an artificial neural network (ANN) ensemble method over the UAE. The high-resolution visible channel of SEVIRI is used in the Heliosat-2 method to derive the cloud index. The cloud index is then used to compute the cloud transmission, while the cloud-free GHI is computed from the Linke turbidity factor. The product of the cloud transmission and the cloud-free GHI denotes the estimated GHI. A constant underestimation is observed in the estimated GHI over the dataset available in the UAE. Therefore, the cloud-free DHI equation in the model was recalibrated to fix the bias. After recalibration, results over the UAE show a root mean square error (RMSE) value of 10.1% and a mean bias error (MBE) of -0.5%. As for the ANN approach, six thermal channels of SEVIRI were used to estimate the DHI and the total optical depth of the atmosphere (δ). An ensemble approach is employed to obtain a better generalizability of the results, as opposed to using one single weak network. The DNI is then computed from the estimated δ using the Beer-Bouguer-Lambert law. The GHI is computed from the DNI and DHI estimates. The RMSE for the estimated GHI obtained over an independent dataset over the UAE is 7.2% and the MBE is +1.9%. The results obtained by the two methods have shown that both the recalibrated Heliosat-2 and the ANN ensemble methods estimate the GHI at a 15-min resolution with high accuracy. The advantage of the ANN ensemble approach is that it derives the GHI from accurate DNI and DHI estimates. The DNI and DHI estimates are valuable when computing the global tilt irradiance. Also, accurate DNI estimates are beneficial for preliminary site selection for concentrating solar powered plants.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Massive Cloud-Based Big Data Processing for Ocean Sensor Networks and Remote Sensing

NASA Astrophysics Data System (ADS)

Schwehr, K. D.

2017-12-01

Until recently, the work required to integrate and analyze data for global-scale environmental issues was prohibitive both in cost and availability. Traditional desktop processing systems are not able to effectively store and process all the data, and super computer solutions are financially out of the reach of most people. The availability of large-scale cloud computing has created tools that are usable by small groups and individuals regardless of financial resources or locally available computational resources. These systems give scientists and policymakers the ability to see how critical resources are being used across the globe with little or no barrier to entry. Google Earth Engine has the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra, MODIS Aqua, and Global Land Data Assimilation Systems (GLDAS) data catalogs available live online. Here we demonstrate these data to calculate the correlation between lagged chlorophyll and rainfall to identify areas of eutrophication, matching these events to ocean currents from datasets like HYbrid Coordinate Ocean Model (HYCOM) to check if there are constraints from oceanographic configurations. The system can provide addition ground truth with observations from sensor networks like the International Comprehensive Ocean-Atmosphere Data Set / Voluntary Observing Ship (ICOADS/VOS) and Argo floats. This presentation is intended to introduce users to the datasets, programming idioms, and functionality of Earth Engine for large-scale, data-driven oceanography.

An economic model of friendship and enmity for measuring social balance in networks

NASA Astrophysics Data System (ADS)

Lee, Kyu-Min; Shin, Euncheol; You, Seungil

2017-12-01

We propose a dynamic economic model of networks where agents can be friends or enemies with one another. This is a decentralized relationship model in that agents decide whether to change their relationships so as to minimize their imbalanced triads. In this model, there is a single parameter, which we call social temperature, that captures the degree to which agents care about social balance in their relationships. We show that the global structure of relationship configuration converges to a unique stationary distribution. Using this stationary distribution, we characterize the maximum likelihood estimator of the social temperature parameter. Since the estimator is computationally challenging to calculate from real social network datasets, we provide a simple simulation algorithm and verify its performance with real social network datasets.

Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes

PubMed Central

2013-01-01

Background MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed. Results We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms. Conclusions In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis. PMID:24103777

Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Liong, Shie-Yui; He, Shan

2010-05-01

Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

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

Big questions, big science: meeting the challenges of global ecology

Treesearch

David Schimel; Michael Keller

2015-01-01

Ecologists are increasingly tackling questions that require significant infrastucture, large experiments, networks of observations, and complex data and computation. Key hypotheses in ecology increasingly require more investment, and larger data sets to be tested than can be collected by a single investigator’s or s group of investigator’s labs, sustained for longer...

Correlation Research on the Application of E-Learning to Students' Self-Regulated Learning Ability, Motivational Beliefs, and Academic Performance

ERIC Educational Resources Information Center

Liu, Hsu Kuan

2016-01-01

Delivering knowledge and economic information through the Internet has been so popular that a lot of countries make major information technology construction plans to enhance competitiveness. In this case, sharing educational resources and narrowing the rural-urban education divide with computer networks have become a common trend globally.…

2015 Marine Corps Security Environment Forecast: Futures 2030-2045

DTIC Science & Technology

2015-01-01

The technologies that make the iPhone “smart” were publically funded—the Internet, wireless networks, the global positioning system, microelectronics...Energy Revolution (63 percent);  Internet of Things (ubiquitous sensors embedded in interconnected computing devices) (50 percent);  “Sci-Fi...Neuroscience & artificial intelligence - Sensors /control systems -Power & energy -Human-robot interaction Robots/autonomous systems will become part of the

Networking the Global Maritime Partnership

DTIC Science & Technology

2008-06-01

how do the navies of disparate nations that desire to operate together at sea obtain the requisite, compatible C4ISR (command, control, communications ...compatible C4ISR (command, control, communications , computers, intelligence, surveillance, and reconnaissance) systems that will enable them to truly...partnership. Coalition Naval Operations Maritime coalitions have existed for two and one-half millennia and navies have communicated at sea for

The Cognitive, Perceptual, and Neural Bases of Skilled Performance

DTIC Science & Technology

1988-09-01

shunting, masking field, bidirectional associative memory, Volterra - Lotka , Gilpin-Ayala, ani Eigen-Schuster models. The Cohen-Grossberg model thus...field, bidirectional associative memory, Volterra - Lotka , Gilpin-Ayala, and Eigen-Schuster models. A Liapunov functional method is described for...storage by neural networks: A general model and global Liapunov method. In E.L. Schwartz (Ed.), Computational neuroscience. Cambridge, MA: MIT Press

A Collaborative Education Network for Advancing Climate Literacy using Data Visualization Technology

NASA Astrophysics Data System (ADS)

McDougall, C.; Russell, E. L.; Murray, M.; Bendel, W. B.

2013-12-01

One of the more difficult issues in engaging broad audiences with scientific research is to present it in a way that is intuitive, captivating and up-to-date. Over the past ten years, the National Oceanic and Atmospheric Administration (NOAA) has made significant progress in this area through Science On a Sphere(R) (SOS). SOS is a room-sized, global display system that uses computers and video projectors to display Earth systems data onto a six-foot diameter sphere, analogous to a giant animated globe. This well-crafted data visualization system serves as a way to integrate and display global change phenomena; including polar ice melt, projected sea level rise, ocean acidification and global climate models. Beyond a display for individual data sets, SOS provides a holistic global perspective that highlights the interconnectedness of Earth systems, nations and communities. SOS is now a featured exhibit at more than 100 science centers, museums, universities, aquariums and other institutions around the world reaching more than 33 million visitors every year. To facilitate the development of how this data visualization technology and these visualizations could be used with public audiences, we recognized the need for the exchange of information among the users. To accomplish this, we established the SOS Users Collaborative Network. This network consists of the institutions that have an SOS system or partners who are creating content and educational programming for SOS. When we began the Network in 2005, many museums had limited capacity to both incorporate real-time, authentic scientific data about the Earth system and interpret global change visualizations. They needed not only the visualization platform and the scientific content, but also assistance with methods of approach. We needed feedback from these users on how to craft understandable visualizations and how to further develop the SOS platform to support learning. Through this Network and the collaboration among members, we have, collectively, been able to advance all of our efforts. The member institutions, through regular face-to-face workshops and an online community, share practices in creation and cataloging of datasets, new methods for delivering content via SOS, and updates on the SOS system and software. One hallmark of the SOS Users Collaborative Network is that it exemplifies an ideal partnership between federal science agencies and informal science education institutions. The science agencies (including NOAA, NASA, and the Department of Energy) provide continuously updated global datasets, scientific expertise, funding, and support. In turn, museums act as trusted public providers of scientific information, provide audience-appropriate presentations, localized relevance to global phenomena and a forum for discussing the complex science and repercussions of global change. We will discuss the characteristics of this Network that maximize collaboration and what we're learning as a community to improve climate literacy.

Outputs of paired Gabor filters summed across the background frame of reference predict the direction of movement

NASA Technical Reports Server (NTRS)

Lawton, Teri B.

1989-01-01

A cortical neural network that computes the visibility of shifts in the direction of movement is proposed. The network computes: (1) the magnitude of the position difference between the test and background patterns, (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern, and (3) using global processes that pool the output from paired even- and odd-symmetric simple and complex cells across the spatial extent of the background frame of reference the direction a test pattern moved relative to a textured background. Evidence that magnocellular pathways are used to discriminate the direction of movement is presented. Since magnocellular pathways are used to discriminate the direction of movement, this task is not affected by small pattern changes such as jitter, short presentations, blurring, and different background contrasts that result when the veiling illumination in a scene changes.

An outer approximation method for the road network design problem

PubMed Central

2018-01-01

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well. PMID:29590111

An outer approximation method for the road network design problem.

PubMed

Asadi Bagloee, Saeed; Sarvi, Majid

2018-01-01

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well.

Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

PubMed

Grossi, Giuliano

2009-08-01

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph theory.

Initial-stage examination of a testbed for the big data transfer over parallel links. The SDN approach

NASA Astrophysics Data System (ADS)

Khoruzhnikov, S. E.; Grudinin, V. A.; Sadov, O. L.; Shevel, A. E.; Titov, V. B.; Kairkanov, A. B.

2015-04-01

The transfer of Big Data over a computer network is an important and unavoidable operation in the past, present, and in any feasible future. A large variety of astronomical projects produces the Big Data. There are a number of methods to transfer the data over a global computer network (Internet) with a range of tools. In this paper we consider the transfer of one piece of Big Data from one point in the Internet to another, in general over a long-range distance: many thousand kilometers. Several free of charge systems to transfer the Big Data are analyzed here. The most important architecture features are emphasized, and the idea is discussed to add the SDN OpenFlow protocol technique for fine-grain tuning of the data transfer process over several parallel data links.

Global adaptation in networks of selfish components: emergent associative memory at the system scale.

PubMed

Watson, Richard A; Mills, Rob; Buckley, C L

2011-01-01

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.

Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

PubMed

Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

2017-07-04

This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

Estimating the Size of a Large Network and its Communities from a Random Sample

PubMed Central

Chen, Lin; Karbasi, Amin; Crawford, Forrest W.

2017-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K, and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios. PMID:28867924

Estimating the Size of a Large Network and its Communities from a Random Sample.

PubMed

Chen, Lin; Karbasi, Amin; Crawford, Forrest W

2016-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = ( V, E ) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G ( W ) be the induced subgraph in G of the vertices in W . In addition to G ( W ), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K , and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios.

The GONG Site Survey. [solar oscillations

NASA Technical Reports Server (NTRS)

Hill, Frank; Ambastha, Ashok; Ball, Warren; Duhalde, Oscar; Farris, Don; Fischer, George; Hieda, Les; Zhen, Huang; Ingram, Bob; Jackson, Patty

1988-01-01

The Global Oscillation Network Group (GONG) project is planning to place six observing stations around the world to observe the solar oscillations as continuously as possible. The procedures that are being used to select the six sites are described. Results of measurements of cloud cover obtained by networks of 6 (out of 10) radiometers show a duty cycle of over 93 percent, with the first diurnal sidelobe in the window power spectrum suppressed by a factor of 400. The results are in good agreement with the predictions of a computer model of the expected cloud cover at individual sites.

Satellite -Based Networks for U-Health & U-Learning

NASA Astrophysics Data System (ADS)

Graschew, G.; Roelofs, T. A.; Rakowsky, S.; Schlag, P. M.

2008-08-01

The use of modern Information and Communication Technologies (ICT) as enabling tools for healthcare services (eHealth) introduces new ways of creating ubiquitous access to high-level medical care for all, anytime and anywhere (uHealth). Satellite communication constitutes one of the most flexible methods of broadband communication offering high reliability and cost-effectiveness of connections meeting telemedicine communication requirements. Global networks and the use of computers for educational purposes stimulate and support the development of virtual universities for e-learning. Especially real-time interactive applications can play an important role in tailored and personalised services.

e-Science and data management resources on the Web.

PubMed

Gore, Sally A

2011-01-01

The way research is conducted has changed over time, from simple experiments to computer modeling and simulation, from individuals working in isolated laboratories to global networks of researchers collaborating on a single topic. Often, this new paradigm results in the generation of staggering amounts of data. The intensive use of data and the existence of networks of researchers characterize e-Science. The role of libraries and librarians in e-Science has been a topic of interest for some time now. This column looks at tools, resources, and projects that demonstrate successful collaborations between libraries and researchers in e-Science.

An Innovative Approach to Bridge a Skill Gap and Grow a Workforce Pipeline: The Computer System, Cluster, and Networking Summer Institute

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

Connor, Carolyn Marie; Jacobson, Andree Lars; Bonnie, Amanda Marie

Sustainable and effective computing infrastructure depends critically on the skills and expertise of domain scientists and of committed and well-trained advanced computing professionals. But, in its ongoing High Performance Computing (HPC) work, Los Alamos National Laboratory noted a persistent shortage of well-prepared applicants, particularly for entry-level cluster administration, file systems administration, and high speed networking positions. Further, based upon recruiting efforts and interactions with universities graduating students in related majors of interest (e.g., computer science (CS)), there has been a long standing skillset gap, as focused training in HPC topics is typically lacking or absent in undergraduate and in evenmore » many graduate programs. Given that the effective operation and use of HPC systems requires specialized and often advanced training, that there is a recognized HPC skillset gap, and that there is intense global competition for computing and computational science talent, there is a long-standing and critical need for innovative approaches to help bridge the gap and create a well-prepared, next generation HPC workforce. Our paper places this need in the context of the HPC work and workforce requirements at Los Alamos National Laboratory (LANL) and presents one such innovative program conceived to address the need, bridge the gap, and grow an HPC workforce pipeline at LANL. The Computer System, Cluster, and Networking Summer Institute (CSCNSI) completed its 10th year in 2016. The story of the CSCNSI and its evolution is detailed below with a description of the design of its Boot Camp, and a summary of its success and some key factors that have enabled that success.« less

An Innovative Approach to Bridge a Skill Gap and Grow a Workforce Pipeline: The Computer System, Cluster, and Networking Summer Institute

DOE PAGES

Connor, Carolyn Marie; Jacobson, Andree Lars; Bonnie, Amanda Marie; ...

2016-11-01

Sustainable and effective computing infrastructure depends critically on the skills and expertise of domain scientists and of committed and well-trained advanced computing professionals. But, in its ongoing High Performance Computing (HPC) work, Los Alamos National Laboratory noted a persistent shortage of well-prepared applicants, particularly for entry-level cluster administration, file systems administration, and high speed networking positions. Further, based upon recruiting efforts and interactions with universities graduating students in related majors of interest (e.g., computer science (CS)), there has been a long standing skillset gap, as focused training in HPC topics is typically lacking or absent in undergraduate and in evenmore » many graduate programs. Given that the effective operation and use of HPC systems requires specialized and often advanced training, that there is a recognized HPC skillset gap, and that there is intense global competition for computing and computational science talent, there is a long-standing and critical need for innovative approaches to help bridge the gap and create a well-prepared, next generation HPC workforce. Our paper places this need in the context of the HPC work and workforce requirements at Los Alamos National Laboratory (LANL) and presents one such innovative program conceived to address the need, bridge the gap, and grow an HPC workforce pipeline at LANL. The Computer System, Cluster, and Networking Summer Institute (CSCNSI) completed its 10th year in 2016. The story of the CSCNSI and its evolution is detailed below with a description of the design of its Boot Camp, and a summary of its success and some key factors that have enabled that success.« less

Development of Integration Framework for Sensor Network and Satellite Image based on OGC Web Services

NASA Astrophysics Data System (ADS)

Ninsawat, Sarawut; Yamamoto, Hirokazu; Kamei, Akihide; Nakamura, Ryosuke; Tsuchida, Satoshi; Maeda, Takahisa

2010-05-01

With the availability of network enabled sensing devices, the volume of information being collected by networked sensors has increased dramatically in recent years. Over 100 physical, chemical and biological properties can be sensed using in-situ or remote sensing technology. A collection of these sensor nodes forms a sensor network, which is easily deployable to provide a high degree of visibility into real-world physical processes as events unfold. The sensor observation network could allow gathering of diverse types of data at greater spatial and temporal resolution, through the use of wired or wireless network infrastructure, thus real-time or near-real time data from sensor observation network allow researchers and decision-makers to respond speedily to events. However, in the case of environmental monitoring, only a capability to acquire in-situ data periodically is not sufficient but also the management and proper utilization of data also need to be careful consideration. It requires the implementation of database and IT solutions that are robust, scalable and able to interoperate between difference and distributed stakeholders to provide lucid, timely and accurate update to researchers, planners and citizens. The GEO (Global Earth Observation) Grid is primarily aiming at providing an e-Science infrastructure for the earth science community. The GEO Grid is designed to integrate various kinds of data related to the earth observation using the grid technology, which is developed for sharing data, storage, and computational powers of high performance computing, and is accessible as a set of services. A comprehensive web-based system for integrating field sensor and data satellite image based on various open standards of OGC (Open Geospatial Consortium) specifications has been developed. Web Processing Service (WPS), which is most likely the future direction of Web-GIS, performs the computation of spatial data from distributed data sources and returns the outcome in a standard format. The interoperability capabilities and Service Oriented Architecture (SOA) of web services allow incorporating between sensor network measurement available from Sensor Observation Service (SOS) and satellite remote sensing data from Web Mapping Service (WMS) as distributed data sources for WPS. Various applications have been developed to demonstrate the efficacy of integrating heterogeneous data source. For example, the validation of the MODIS aerosol products (MOD08_D3, the Level-3 MODIS Atmosphere Daily Global Product) by ground-based measurements using the sunphotometer (skyradiometer, Prede POM-02) installed at Phenological Eyes Network (PEN) sites in Japan. Furthermore, the web-based framework system for studying a relationship between calculated Vegetation Index from MODIS satellite image surface reflectance (MOD09GA, the Surface Reflectance Daily L2G Global 1km and 500m Product) and Gross Primary Production (GPP) field measurement at flux tower site in Thailand and Japan has been also developed. The success of both applications will contribute to maximize data utilization and improve accuracy of information by validate MODIS satellite products using high degree of accuracy and temporal measurement of field measurement data.

Network placement optimization for large-scale distributed system

NASA Astrophysics Data System (ADS)

Ren, Yu; Liu, Fangfang; Fu, Yunxia; Zhou, Zheng

2018-01-01

The network geometry strongly influences the performance of the distributed system, i.e., the coverage capability, measurement accuracy and overall cost. Therefore the network placement optimization represents an urgent issue in the distributed measurement, even in large-scale metrology. This paper presents an effective computer-assisted network placement optimization procedure for the large-scale distributed system and illustrates it with the example of the multi-tracker system. To get an optimal placement, the coverage capability and the coordinate uncertainty of the network are quantified. Then a placement optimization objective function is developed in terms of coverage capabilities, measurement accuracy and overall cost. And a novel grid-based encoding approach for Genetic algorithm is proposed. So the network placement is optimized by a global rough search and a local detailed search. Its obvious advantage is that there is no need for a specific initial placement. At last, a specific application illustrates this placement optimization procedure can simulate the measurement results of a specific network and design the optimal placement efficiently.

Role models for complex networks

NASA Astrophysics Data System (ADS)

Reichardt, J.; White, D. R.

2007-11-01

We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.

Exploring biological and social networks to better understand and treat diabetes mellitus. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Belgardt, Bengt-Frederik; Jarasch, Alexander; Lammert, Eckhard

2018-03-01

Improvements and breakthroughs in computational sciences in the last 20 years have paralleled the rapid gain of influence of social networks on our daily life. As timely reviewed by Perc and colleagues [1], understanding and treating complex human diseases, such as type 2 diabetes (T2D), from which already more than 5% of the global population suffer, will necessitate analyzing and understanding the multi-layered and interconnected networks that usually keep physiological functions intact, but are disturbed in disease states. These networks range from intra- and intercellular networks influencing cell behavior (e.g., secretion of insulin in response to food intake and anabolic response to insulin) to social networks influencing human behavior (e.g., food intake and physical activity). This commentary first expands on the background of pancreatic beta cell networks in human health and T2D, briefly introduces exosomes as novel signals exchanged between distant cellular networks, and finally discusses potential pitfalls and chances in network analyses with regards to experimental data acquisition and processing.

High-resolution method for evolving complex interface networks

NASA Astrophysics Data System (ADS)

Pan, Shucheng; Hu, Xiangyu Y.; Adams, Nikolaus A.

2018-04-01

In this paper we describe a high-resolution transport formulation of the regional level-set approach for an improved prediction of the evolution of complex interface networks. The novelty of this method is twofold: (i) construction of local level sets and reconstruction of a global level set, (ii) local transport of the interface network by employing high-order spatial discretization schemes for improved representation of complex topologies. Various numerical test cases of multi-region flow problems, including triple-point advection, single vortex flow, mean curvature flow, normal driven flow, dry foam dynamics and shock-bubble interaction show that the method is accurate and suitable for a wide range of complex interface-network evolutions. Its overall computational cost is comparable to the Semi-Lagrangian regional level-set method while the prediction accuracy is significantly improved. The approach thus offers a viable alternative to previous interface-network level-set method.

Global Anomaly Detection in Two-Dimensional Symmetry-Protected Topological Phases

NASA Astrophysics Data System (ADS)

Bultinck, Nick; Vanhove, Robijn; Haegeman, Jutho; Verstraete, Frank

2018-04-01

Edge theories of symmetry-protected topological phases are well known to possess global symmetry anomalies. In this Letter we focus on two-dimensional bosonic phases protected by an on-site symmetry and analyze the corresponding edge anomalies in more detail. Physical interpretations of the anomaly in terms of an obstruction to orbifolding and constructing symmetry-preserving boundaries are connected to the cohomology classification of symmetry-protected phases in two dimensions. Using the tensor network and matrix product state formalism we numerically illustrate our arguments and discuss computational detection schemes to identify symmetry-protected order in a ground state wave function.

Executing scatter operation to parallel computer nodes by repeatedly broadcasting content of send buffer partition corresponding to each node upon bitwise OR operation

DOEpatents

Archer, Charles J [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2009-11-06

Executing a scatter operation on a parallel computer includes: configuring a send buffer on a logical root, the send buffer having positions, each position corresponding to a ranked node in an operational group of compute nodes and for storing contents scattered to that ranked node; and repeatedly for each position in the send buffer: broadcasting, by the logical root to each of the other compute nodes on a global combining network, the contents of the current position of the send buffer using a bitwise OR operation, determining, by each compute node, whether the current position in the send buffer corresponds with the rank of that compute node, if the current position corresponds with the rank, receiving the contents and storing the contents in a reception buffer of that compute node, and if the current position does not correspond with the rank, discarding the contents.

Globally scalable generation of high-resolution land cover from multispectral imagery

NASA Astrophysics Data System (ADS)

Stutts, S. Craig; Raskob, Benjamin L.; Wenger, Eric J.

2017-05-01

We present an automated method of generating high resolution ( 2 meter) land cover using a pattern recognition neural network trained on spatial and spectral features obtained from over 9000 WorldView multispectral images (MSI) in six distinct world regions. At this resolution, the network can classify small-scale objects such as individual buildings, roads, and irrigation ponds. This paper focuses on three key areas. First, we describe our land cover generation process, which involves the co-registration and aggregation of multiple spatially overlapping MSI, post-aggregation processing, and the registration of land cover to OpenStreetMap (OSM) road vectors using feature correspondence. Second, we discuss the generation of land cover derivative products and their impact in the areas of region reduction and object detection. Finally, we discuss the process of globally scaling land cover generation using cloud computing via Amazon Web Services (AWS).

A Global Repository for Planet-Sized Experiments and Observations

NASA Technical Reports Server (NTRS)

Williams, Dean; Balaji, V.; Cinquini, Luca; Denvil, Sebastien; Duffy, Daniel; Evans, Ben; Ferraro, Robert D.; Hansen, Rose; Lautenschlager, Michael; Trenham, Claire

2016-01-01

Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP) output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.

Seismograms live from around the world

USGS Publications Warehouse

Woodward, Robert L.; Shedlock, Kaye M.; Bolton, Harold F.

1999-01-01

You can view earthquakes as they happen! Seismograms from seismic stations around the world are broadcast live, via the Internet, and are updated every 30 minutes, With an Internet connection and a web browser, you can view current seismograms and earthquake locations on your own computer. With special software also available via the Internet, you can obtain seismic data as it arrives from a global network of seismograph stations.

HyspIRI Low Latency Concept and Benchmarks

NASA Technical Reports Server (NTRS)

Mandl, Dan

2010-01-01

Topics include HyspIRI low latency data ops concept, HyspIRI data flow, ongoing efforts, experiment with Web Coverage Processing Service (WCPS) approach to injecting new algorithms into SensorWeb, low fidelity HyspIRI IPM testbed, compute cloud testbed, open cloud testbed environment, Global Lambda Integrated Facility (GLIF) and OCC collaboration with Starlight, delay tolerant network (DTN) protocol benchmarking, and EO-1 configuration for preliminary DTN prototype.

A Hybrid Approach to Develop an Analytical Model for Enhancing the Service Quality of E-Learning

ERIC Educational Resources Information Center

Wu, Hung-Yi; Lin, Hsin-Yu

2012-01-01

The digital content industry is flourishing as a result of the rapid development of technology and the widespread use of computer networks. As has been reported, the market size of the global e-learning (i.e., distance education and telelearning) will reach USD 49.6 billion in 2014. However, to retain and/or increase the market share associated…

Network structure impacts global commodity trade growth and resilience.

PubMed

Kharrazi, Ali; Rovenskaya, Elena; Fath, Brian D

2017-01-01

Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks' redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks.

Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella.

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

McDermott, Jason E.; Yoon, Hyunjin; Nakayasu, Ernesto S.

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated a large amount of datamore » and driven development of computational approaches required for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird’s eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.« less

Analyzing milestoning networks for molecular kinetics: definitions, algorithms, and examples.

PubMed

Viswanath, Shruthi; Kreuzer, Steven M; Cardenas, Alfredo E; Elber, Ron

2013-11-07

Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstra's, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstra's algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.

Water Intelligence and the Cyber-Infrastructure Revolution

NASA Astrophysics Data System (ADS)

Cline, D. W.

2015-12-01

As an intrinsic factor in national security, the global economy, food and energy production, and human and ecological health, fresh water resources are increasingly being considered by an ever-widening array of stakeholders. The U.S. intelligence community has identified water as a key factor in the Nation's security risk profile. Water industries are growing rapidly, and seek to revolutionize the role of water in the global economy, making water an economic value rather than a limitation on operations. Recent increased focus on the complex interrelationships and interdependencies between water, food, and energy signal a renewed effort to move towards integrated water resource management. Throughout all of this, hydrologic extremes continue to wreak havoc on communities and regions around the world, in some cases threatening long-term economic stability. This increased attention on water coincides with the "second IT revolution" of cyber-infrastructure (CI). The CI concept is a convergence of technology, data, applications and human resources, all coalescing into a tightly integrated global grid of computing, information, networking and sensor resources, and ultimately serving as an engine of change for collaboration, education and scientific discovery and innovation. In the water arena, we have unprecedented opportunities to apply the CI concept to help address complex water challenges and shape the future world of water resources - on both science and socio-economic application fronts. Providing actionable local "water intelligence" nationally or globally is now becoming feasible through high-performance computing, data technologies, and advanced hydrologic modeling. Further development on all of these fronts appears likely and will help advance this much-needed capability. Lagging behind are water observation systems, especially in situ networks, which need significant innovation to keep pace with and help fuel rapid advancements in water intelligence.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

PubMed

Navlakha, Saket; Barth, Alison L; Bar-Joseph, Ziv

2015-07-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

PubMed Central

Navlakha, Saket; Barth, Alison L.; Bar-Joseph, Ziv

2015-01-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains. PMID:26217933

Toward a Scalable Visualization System for Network Traffic Monitoring

NASA Astrophysics Data System (ADS)

Malécot, Erwan Le; Kohara, Masayoshi; Hori, Yoshiaki; Sakurai, Kouichi

With the multiplication of attacks against computer networks, system administrators are required to monitor carefully the traffic exchanged by the networks they manage. However, that monitoring task is increasingly laborious because of the augmentation of the amount of data to analyze. And that trend is going to intensify with the explosion of the number of devices connected to computer networks along with the global rise of the available network bandwidth. So system administrators now heavily rely on automated tools to assist them and simplify the analysis of the data. Yet, these tools provide limited support and, most of the time, require highly skilled operators. Recently, some research teams have started to study the application of visualization techniques to the analysis of network traffic data. We believe that this original approach can also allow system administrators to deal with the large amount of data they have to process. In this paper, we introduce a tool for network traffic monitoring using visualization techniques that we developed in order to assist the system administrators of our corporate network. We explain how we designed the tool and some of the choices we made regarding the visualization techniques to use. The resulting tool proposes two linked representations of the network traffic and activity, one in 2D and the other in 3D. As 2D and 3D visualization techniques have different assets, we resulted in combining them in our tool to take advantage of their complementarity. We finally tested our tool in order to evaluate the accuracy of our approach.

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

Network structure impacts global commodity trade growth and resilience

PubMed Central

Rovenskaya, Elena; Fath, Brian D.

2017-01-01

Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks’ redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks. PMID:28207790

Biomorphic Multi-Agent Architecture for Persistent Computing

NASA Technical Reports Server (NTRS)

Lodding, Kenneth N.; Brewster, Paul

2009-01-01

A multi-agent software/hardware architecture, inspired by the multicellular nature of living organisms, has been proposed as the basis of design of a robust, reliable, persistent computing system. Just as a multicellular organism can adapt to changing environmental conditions and can survive despite the failure of individual cells, a multi-agent computing system, as envisioned, could adapt to changing hardware, software, and environmental conditions. In particular, the computing system could continue to function (perhaps at a reduced but still reasonable level of performance) if one or more component( s) of the system were to fail. One of the defining characteristics of a multicellular organism is unity of purpose. In biology, the purpose is survival of the organism. The purpose of the proposed multi-agent architecture is to provide a persistent computing environment in harsh conditions in which repair is difficult or impossible. A multi-agent, organism-like computing system would be a single entity built from agents or cells. Each agent or cell would be a discrete hardware processing unit that would include a data processor with local memory, an internal clock, and a suite of communication equipment capable of both local line-of-sight communications and global broadcast communications. Some cells, denoted specialist cells, could contain such additional hardware as sensors and emitters. Each cell would be independent in the sense that there would be no global clock, no global (shared) memory, no pre-assigned cell identifiers, no pre-defined network topology, and no centralized brain or control structure. Like each cell in a living organism, each agent or cell of the computing system would contain a full description of the system encoded as genes, but in this case, the genes would be components of a software genome.

Collecting data from a sensor network in a single-board computer

NASA Astrophysics Data System (ADS)

Casciati, F.; Casciati, S.; Chen, Z.-C.; Faravelli, L.; Vece, M.

2015-07-01

The EU-FP7 project SPARTACUS, currently in progress, sees the international cooperation of several partners toward the design and implementation of a satellite based asset tracking for supporting emergency management in crisis operations. Due to the emergency environment, one has to rely on a low power consumption wireless communication. Therefore, the communication hardware and software must be designed to match requirements which can only be foreseen at the level of more or less likely scenarios. The latter aspect suggests a deep use of a simulator (instead of a real network of sensors) to cover extreme situations. The former power consumption remark suggests the use of a minimal computer (Raspberry Pi) as data collector. In this paper, the results of a broad simulation campaign are reported in order to investigate the accuracy of the received data and the global power consumption for each of the considered scenarios.

Emergent Adaptive Noise Reduction from Communal Cooperation of Sensor Grid

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Jones, Michael G.; Nark, Douglas M.; Lodding, Kenneth N.

2010-01-01

In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant

Material discovery by combining stochastic surface walking global optimization with a neural network.

PubMed

Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-09-01

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

PubMed

Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

2016-11-01

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Emerging computer technologies and the news media of the future

NASA Technical Reports Server (NTRS)

Vrabel, Debra A.

1993-01-01

The media environment of the future may be dramatically different from what exists today. As new computing and communications technologies evolve and synthesize to form a global, integrated communications system of networks, public domain hardware and software, and consumer products, it will be possible for citizens to fulfill most information needs at any time and from any place, to obtain desired information easily and quickly, to obtain information in a variety of forms, and to experience and interact with information in a variety of ways. This system will transform almost every institution, every profession, and every aspect of human life--including the creation, packaging, and distribution of news and information by media organizations. This paper presents one vision of a 21st century global information system and how it might be used by citizens. It surveys some of the technologies now on the market that are paving the way for new media environment.

Hybrid epidemics--a case study on computer worm conficker.

PubMed

Zhang, Changwang; Zhou, Shi; Chain, Benjamin M

2015-01-01

Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm's spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm's effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols.

Hybrid Epidemics—A Case Study on Computer Worm Conficker

PubMed Central

Zhang, Changwang; Zhou, Shi; Chain, Benjamin M.

2015-01-01

Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm’s spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm’s effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols. PMID:25978309

An Object-Oriented Network-Centric Software Architecture for Physical Computing

NASA Astrophysics Data System (ADS)

Palmer, Richard

1997-08-01

Recent developments in object-oriented computer languages and infrastructure such as the Internet, Web browsers, and the like provide an opportunity to define a more productive computational environment for scientific programming that is based more closely on the underlying mathematics describing physics than traditional programming languages such as FORTRAN or C++. In this talk I describe an object-oriented software architecture for representing physical problems that includes classes for such common mathematical objects as geometry, boundary conditions, partial differential and integral equations, discretization and numerical solution methods, etc. In practice, a scientific program written using this architecture looks remarkably like the mathematics used to understand the problem, is typically an order of magnitude smaller than traditional FORTRAN or C++ codes, and hence easier to understand, debug, describe, etc. All objects in this architecture are ``network-enabled,'' which means that components of a software solution to a physical problem can be transparently loaded from anywhere on the Internet or other global network. The architecture is expressed as an ``API,'' or application programmers interface specification, with reference embeddings in Java, Python, and C++. A C++ class library for an early version of this API has been implemented for machines ranging from PC's to the IBM SP2, meaning that phidentical codes run on all architectures.

Network-based statistical comparison of citation topology of bibliographic databases

PubMed Central

Šubelj, Lovro; Fiala, Dalibor; Bajec, Marko

2014-01-01

Modern bibliographic databases provide the basis for scientific research and its evaluation. While their content and structure differ substantially, there exist only informal notions on their reliability. Here we compare the topological consistency of citation networks extracted from six popular bibliographic databases including Web of Science, CiteSeer and arXiv.org. The networks are assessed through a rich set of local and global graph statistics. We first reveal statistically significant inconsistencies between some of the databases with respect to individual statistics. For example, the introduced field bow-tie decomposition of DBLP Computer Science Bibliography substantially differs from the rest due to the coverage of the database, while the citation information within arXiv.org is the most exhaustive. Finally, we compare the databases over multiple graph statistics using the critical difference diagram. The citation topology of DBLP Computer Science Bibliography is the least consistent with the rest, while, not surprisingly, Web of Science is significantly more reliable from the perspective of consistency. This work can serve either as a reference for scholars in bibliometrics and scientometrics or a scientific evaluation guideline for governments and research agencies. PMID:25263231

Implementing Journaling in a Linux Shared Disk File System

NASA Technical Reports Server (NTRS)

Preslan, Kenneth W.; Barry, Andrew; Brassow, Jonathan; Cattelan, Russell; Manthei, Adam; Nygaard, Erling; VanOort, Seth; Teigland, David; Tilstra, Mike; O'Keefe, Matthew;

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

PubMed

Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

Spectra of English evolving word co-occurrence networks

NASA Astrophysics Data System (ADS)

Liang, Wei

2017-02-01

Spectral analysis is a powerful tool that provides global measures of the network properties. In this paper, 200 English articles are collected. A word co-occurrence network is constructed from each single article (denoted by single network). Furthermore, 5 large English word co-occurrence networks are constructed (denoted by large network). Spectra of their adjacency matrices are computed. The largest eigenvalue, λ1, depends on the network size N and the number of edges E as λ1 ∝N0.66 and λ1 ∝E0.54, respectively. The number of different eigenvalues, Nλ, increase in the manner of Nλ ∝N0.58 and Nλ ∝E0.47. The middle part of the spectral distribution can be fitted by a line with slope - 0.01 in each of the large networks, whereas two segments with the same slope - 0.03 for 0 ≪ N < 260 and - 0.02 for 260 < N < 2800 are needed for the single networks. An "M"-shape distribution appears in each of the spectral densities of the large networks. These and other results can provide useful insight into the structural properties of English linguistic networks.

Net2Align: An Algorithm For Pairwise Global Alignment of Biological Networks

PubMed Central

Wadhwab, Gulshan; Upadhyayaa, K. C.

2016-01-01

The amount of data on molecular interactions is growing at an enormous pace, whereas the progress of methods for analysing this data is still lacking behind. Particularly, in the area of comparative analysis of biological networks, where one wishes to explore the similarity between two biological networks, this holds a potential problem. In consideration that the functionality primarily runs at the network level, it advocates the need for robust comparison methods. In this paper, we describe Net2Align, an algorithm for pairwise global alignment that can perform node-to-node correspondences as well as edge-to-edge correspondences into consideration. The uniqueness of our algorithm is in the fact that it is also able to detect the type of interaction, which is essential in case of directed graphs. The existing algorithm is only able to identify the common nodes but not the common edges. Another striking feature of the algorithm is that it is able to remove duplicate entries in case of variable datasets being aligned. This is achieved through creation of a local database which helps exclude duplicate links. In a pervasive computational study on gene regulatory network, we establish that our algorithm surpasses its counterparts in its results. Net2Align has been implemented in Java 7 and the source code is available as supplementary files. PMID:28356678

Space physics analysis network node directory (The Yellow Pages): Fourth edition

NASA Technical Reports Server (NTRS)

Peters, David J.; Sisson, Patricia L.; Green, James L.; Thomas, Valerie L.

1989-01-01

The Space Physics Analysis Network (SPAN) is a component of the global DECnet Internet, which has over 17,000 host computers. The growth of SPAN from its implementation in 1981 to its present size of well over 2,500 registered SPAN host computers, has created a need for users to acquire timely information about the network through a central source. The SPAN Network Information Center (SPAN-NIC) an online facility managed by the National Space Science Data Center (NSSDC) was developed to meet this need for SPAN-wide information. The remote node descriptive information in this document is not currently contained in the SPAN-NIC database, but will be incorporated in the near future. Access to this information is also available to non-DECnet users over a variety of networks such as Telenet, the NASA Packet Switched System (NPSS), and the TCP/IP Internet. This publication serves as the Yellow Pages for SPAN node information. The document also provides key information concerning other computer networks connected to SPAN, nodes associated with each SPAN routing center, science discipline nodes, contacts for primary SPAN nodes, and SPAN reference information. A section on DECnet Internetworking discusses SPAN connections with other wide-area DECnet networks (many with thousands of nodes each). Another section lists node names and their disciplines, countries, and institutions in the SPAN Network Information Center Online Data Base System. All remote sites connected to US-SPAN and European-SPAN (E-SPAN) are indexed. Also provided is information on the SPAN tail circuits, i.e., those remote nodes connected directly to a SPAN routing center, which is the local point of contact for resolving SPAN-related problems. Reference material is included for those who wish to know more about SPAN. Because of the rapid growth of SPAN, the SPAN Yellow Pages is reissued periodically.

A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems.

PubMed

Wang, Lujia; Liu, Ming; Meng, Max Q-H

2017-02-01

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.

PubMed

Wöhling, Thomas; Geiges, Andreas; Nowak, Wolfgang

2016-11-01

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types. © 2016, National Ground Water Association.

The Air Force Interactive Meteorological System: A Research Tool for Satellite Meteorology

DTIC Science & Technology

1992-12-02

NFARnet itself is a subnet to the global computer network INTERNET that links nearly all U.S. government research facilities and universi- ties along...required input to a generalized mathematical solution to the satellite/earth coordinate transform used for earth location of GOES sensor data. A direct...capability also exists to convert absolute coordinates to relative coordinates for transformations associated with gridded fields. 3. Spatial objective

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa).

PubMed

Liu, Shiwei; Liu, Yihui; Zhao, Jiawei; Cai, Shitao; Qian, Hongmei; Zuo, Kaijing; Zhao, Lingxia; Zhang, Lida

2017-04-01

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Remote Earth Sciences data collection using ACTS

NASA Technical Reports Server (NTRS)

Evans, Robert H.

1992-01-01

Given the focus on global change and the attendant scope of such research, we anticipate significant growth of requirements for investigator interaction, processing system capabilities, and availability of data sets. The increased complexity of global processes requires interdisciplinary teams to address them; the investigators will need to interact on a regular basis; however, it is unlikely that a single institution will house sufficient investigators with the required breadth of skills. The complexity of the computations may also require resources beyond those located within a single institution; this lack of sufficient computational resources leads to a distributed system located at geographically dispersed institutions. Finally the combination of long term data sets like the Pathfinder datasets and the data to be gathered by new generations of satellites such as SeaWiFS and MODIS-N yield extra-ordinarily large amounts of data. All of these factors combine to increase demands on the communications facilities available; the demands are generating requirements for highly flexible, high capacity networks. We have been examining the applicability of the Advanced Communications Technology Satellite (ACTS) to address the scientific, computational, and, primarily, communications questions resulting from global change research. As part of this effort three scenarios for oceanographic use of ACTS have been developed; a full discussion of this is contained in Appendix B.

Information processing architecture of functionally defined clusters in the macaque cortex.

PubMed

Shen, Kelly; Bezgin, Gleb; Hutchison, R Matthew; Gati, Joseph S; Menon, Ravi S; Everling, Stefan; McIntosh, Anthony R

2012-11-28

Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Generation, estimation, utilization, availability and compatibility aspects of geodetic and meteorological data

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

Luetzow, H.B.v.

1983-08-01

Following an introduction, the paper discusses in section 2 the collection or generation of final geodetic data from conventional surveys, satellite observations, satellite altimetry, the Global Positioning System, and moving base gravity gradiometers. Section 3 covers data utilization and accuracy aspects including gravity programmed inertial positioning and subterraneous mass detection. Section 4 addresses the usefulness and limitation of the collocation method of physical geodesy. Section 5 is concerned with the computation of classical climatological data. In section 6, meteorological data assimilation is considered. Section 7 deals with correlated aspects of initial data generation with emphasis on initial wind field determination,more » parameterized and classical hydrostatic prediction models, non-hydrostatic prediction, computational networks, and computer capacity. The paper concludes that geodetic and meteorological data are expected to become increasingly more diversified and voluminous both regionally and globally, that its general availability will be more or less restricted for some time to come, that its quality and quantity are subject to change, and that meteorological data generation, accuracy and density have to be considered in conjunction with advanced as well as cost-effective numerical weather prediction models and associated computational efforts.« less

How Standard Are the Standard Barriers to E-commerce Adoption? Empirical Evidence from Australia, Sweden and the USA

NASA Astrophysics Data System (ADS)

Macflregor, Robert; Vrazalic, Lejla; Carlsson, Sten; Pratt, Jean; Harris, Matthew

Despite their size, small to medium enterprises (SMEs) are increasingly turning to global markets. This development has been enabled by the advent of electronic commerce technology. There are numerous definitions of e-comrnerce in the literature, however, fundamentally e-commerce can best be described as "the buying and selling of information, products, and services via computer networks" (Kalakota & Whinston, 997, p.3). Ecommerce has the potential to become a source of competitive advantage to the SME sector because it is a cost effective way of accessing customers and being 'wired to the global marketplace'.

Status report on the USGS component of the Global Seismographic Network

NASA Astrophysics Data System (ADS)

Gee, L. S.; Bolton, H. F.; Derr, J.; Ford, D.; Gyure, G.; Hutt, C. R.; Ringler, A.; Storm, T.; Wilson, D.

2010-12-01

As recently as four years ago, the average age of a datalogger in the portion of the Global Seismographic Network (GSN) operated by the United States Geological Survey (USGS) was 16 years - an eternity in the lifetime of computers. The selection of the Q330HR in 2006 as the “next generation” datalogger by an Incorporated Research Institutions for Seismology (IRIS) selection committee opened the door for upgrading the GSN. As part of the “next generation” upgrades, the USGS is replacing a single Q680 system with two Q330HRs and a field processor to provide the same capability. The functionality includes digitizing, timing, event detection, conversion into miniSEED records, archival of miniSEED data on the ASP and telemetry of the miniSEED data using International Deployment of Accelerometers (IDA) Authenticated Disk Protocol (IACP). At many sites, Quanterra Balers are also being deployed. The Q330HRs feature very low power consumption (which will increase reliability) and higher resolution than the Q680 systems. Furthermore, this network-wide upgrade provides the opportunity to correct known station problems, standardize the installation of secondary sensors and accelerometers, replace the feedback electronics of STS-1 sensors, and perform checks of absolute system sensitivity and sensor orientation. The USGS upgrades began with ANMO in May, 2008. Although we deployed Q330s at KNTN and WAKE in the fall of 2007 (and in the installation of the Caribbean network), these deployments did not include the final software configuration for the GSN upgrades. Following this start, the USGS installed six additional sites in FY08. With funding from the American Recovery and Reinvestment Act and the USGS GSN program, 14 stations were upgraded in FY09. Twenty-one stations are expected to be upgraded in FY10. These systematic network-wide upgrades will improve the reliability and data quality of the GSN, with the end goal of providing the Earth science community high quality seismic data with global coverage. The Global Seismographic Network is operated as a partnership among the National Science Foundation, IRIS, IDA, and the USGS.

Anchor Node Localization for Wireless Sensor Networks Using Video and Compass Information Fusion

PubMed Central

Pescaru, Dan; Curiac, Daniel-Ioan

2014-01-01

Distributed sensing, computing and communication capabilities of wireless sensor networks require, in most situations, an efficient node localization procedure. In the case of random deployments in harsh or hostile environments, a general localization process within global coordinates is based on a set of anchor nodes able to determine their own position using GPS receivers. In this paper we propose another anchor node localization technique that can be used when GPS devices cannot accomplish their mission or are considered to be too expensive. This novel technique is based on the fusion of video and compass data acquired by the anchor nodes and is especially suitable for video- or multimedia-based wireless sensor networks. For these types of wireless networks the presence of video cameras is intrinsic, while the presence of digital compasses is also required for identifying the cameras' orientations. PMID:24594614

A network model for characterizing brine channels in sea ice

NASA Astrophysics Data System (ADS)

Lieblappen, Ross M.; Kumar, Deip D.; Pauls, Scott D.; Obbard, Rachel W.

2018-03-01

The brine pore space in sea ice can form complex connected structures whose geometry is critical in the governance of important physical transport processes between the ocean, sea ice, and surface. Recent advances in three-dimensional imaging using X-ray micro-computed tomography have enabled the visualization and quantification of the brine network morphology and variability. Using imaging of first-year sea ice samples at in situ temperatures, we create a new mathematical network model to characterize the topology and connectivity of the brine channels. This model provides a statistical framework where we can characterize the pore networks via two parameters, depth and temperature, for use in dynamical sea ice models. Our approach advances the quantification of brine connectivity in sea ice, which can help investigations of bulk physical properties, such as fluid permeability, that are key in both global and regional sea ice models.

Analysis of context dependence in social interaction networks of a massively multiplayer online role-playing game.

PubMed

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations.

PubMed

Cai, Zuowei; Huang, Lihong; Zhang, Lingling

2015-05-01

This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback controller and using some analytic techniques, new testable algebraic criteria are obtained to realize two different kinds of global exponential synchronization of the drive-response system. Moreover, we give the estimated rate of exponential synchronization which depends on the delays and system parameters. The obtained results extend some previous works on synchronization of delayed neural networks not only with continuous activations but also with discontinuous activations. Finally, numerical examples are provided to show the correctness of our analysis via computer simulations. Our method and theoretical results have a leading significance in the design of synchronized neural network circuits involving discontinuous factors and time-varying delays. Copyright © 2015 Elsevier Ltd. All rights reserved.

A biologically inspired immunization strategy for network epidemiology.

PubMed

Liu, Yang; Deng, Yong; Jusup, Marko; Wang, Zhen

2016-07-07

Well-known immunization strategies, based on degree centrality, betweenness centrality, or closeness centrality, either neglect the structural significance of a node or require global information about the network. We propose a biologically inspired immunization strategy that circumvents both of these problems by considering the number of links of a focal node and the way the neighbors are connected among themselves. The strategy thus measures the dependence of the neighbors on the focal node, identifying the ability of this node to spread the disease. Nodes with the highest ability in the network are the first to be immunized. To test the performance of our method, we conduct numerical simulations on several computer-generated and empirical networks, using the susceptible-infected-recovered (SIR) model. The results show that the proposed strategy largely outperforms the existing well-known strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neuroendocrine and immune network re-modeling in chronic fatigue syndrome: an exploratory analysis.

PubMed

Fuite, Jim; Vernon, Suzanne D; Broderick, Gordon

2008-12-01

This work investigates the significance of changes in association patterns linking indicators of neuroendocrine and immune activity in patients with chronic fatigue syndrome (CFS). Gene sets preferentially expressed in specific immune cell isolates were integrated with neuroendocrine data from a large population-based study. Co-expression patterns linking immune cell activity with hypothalamic-pituitary-adrenal (HPA), thyroidal (HPT) and gonadal (HPG) axis status were computed using mutual information criteria. Networks in control and CFS subjects were compared globally in terms of a weighted graph edit distance. Local re-modeling of node connectivity was quantified by node degree and eigenvector centrality measures. Results indicate statistically significant differences between CFS and control networks determined mainly by re-modeling around pituitary and thyroid nodes as well as an emergent immune sub-network. Findings align with known mechanisms of chronic inflammation and support possible immune-mediated loss of thyroid function in CFS exacerbated by blunted HPA axis responsiveness.

Model of community emergence in weighted social networks

NASA Astrophysics Data System (ADS)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

A multi-scale network method for two-phase flow in porous media

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

Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces withinmore » each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.« less

Using the D-Wave 2X Quantum Computer to Explore the Formation of Global Terrorist Networks

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

Ambrosiano, John Joseph; Roberts, Randy Mark; Sims, Benjamin Hayden

Social networks with signed edges (+/-) play an important role in an area of social network theory called structural balance. In these networks, edges represent relationships that are labeled as either friendly (+) or hostile (-). A signed social network is balanced only if all cycles of three or more nodes in the graph have an odd number of hostile edges. A fundamental property of a balanced network is that it can be cleanly divided into 2 factions, where all relationships within each faction are friendly, and all relationships between members of different factions are hostile. The more unbalanced amore » network is, the more edges will fail to adhere to this rule, making factions more ambiguous. Social theory suggests unbalanced networks should be unstable, a finding that has been supported by research on gangs, which shows that unbalanced relationships are associated with greater violence, possibly due to this increased ambiguity about factional allegiances (Nakamura et al). One way to estimate the imbalance in a network, if only edge relationships are known, is to assign nodes to factions that minimize the number of violations of the edge rule described above. This problem is known to be computationally NP-hard. However, Facchetti et al. have pointed out that it is equivalent to an Ising model with a Hamiltonian that effectively counts the number of edge rule violations. Therefore, finding the assignment of factions that minimizes energy of the equivalent Ising system yields an estimate of the imbalance in the network. Based on the Ising model equivalence of the signed-social network balance problem, we have used the D-Wave 2X quantum annealing computer to explore some aspects of signed social networks. Because connectivity in the D-Wave computer is limited to its particular native topology, arbitrary networks cannot be represented directly. Rather, they must be “embedded” using a technique in which multiple qubits are chained together with special weights to simulate a collection of nodes with the required connectivity. This limits the size of a fully connected network in the D-Wave to about 50 simulated nodes, using all of the approximately 1150 qubits in the machine. In order to keep within this limitation, while exploring a problem of potential social relevance, we constructed time series of historical network snapshots from Stanford’s Mapping Militants Project, where nodes represent militant organizations, and edges represent either alliances or rivalries between organizations. We constructed two series from different theaters – Iraq and Syria – spanning timelines from about 2000 to 2016, each with networks whose maximum size was in the 20-30 node range. Computationally, our experience suggests D-Wave technology is promising, providing fast, nearly constant scaling of computational effort in the main part of the calculation that relies on the quantum annealing cycle. However, the cost of embedding an arbitrary network of interest in the D-Wave native topology scales poorly. If the embedding cost can be amortized relative to the annealing cycle, it may be possible to gain a substantial advantage over classical computing methods, provided a large enough network can be accommodated by partitioning into subnetworks or some similar strategy. In terms of our application to networks of militant organizations, we found a rise in network imbalance in the Syrian theater that appears to correspond roughly with the entrance of the Islamic State into a milieu already populated with other groups, a phenomenon we plan to explore in more detail. In these very preliminary results, we also noticed that during at least one period where both the size and imbalance of the network increased substantially, the imbalance per edge seemed to remain fairly steady. This may suggest some adaptive behavior among the participating factions, which may also warrant further exploration.« less

Multiscaling statistics of high frequency global solar radiation data in the Guadeloupean Archipelago

NASA Astrophysics Data System (ADS)

Calif, R.; Schmitt, F. G.; Huang, Y.; Soubdhan, T.

2013-12-01

The part of the solar power production from photovoltaiccs systems is constantly increasing in the electric grids. Solar energy converter devices such as photovoltaic cells are very sensitive to instantaneous solar radiation fluctuations. Thus rapid variation of solar radiation due to changes in the local meteorological condition can induce large amplitude fluctuations of the produced electrical power and reduce the overall efficiency of the system. When large amount of photovoltaic electricity is send into a weak or small electricity network such as island network, the electric grid security can be in jeopardy due to these power fluctuations. The integration of this energy into the electrical network remains a major challenge, due to the high variability of solar radiation in time and space. To palliate these difficulties, it is essential to identify the characteristic of these fluctuations in order to anticipate the eventuality of power shortage or power surge. A good knowledge of the intermittency of global solar radiation is crucial for selecting the location of a solar power plant and predicting the generation of electricity. This work presents a multifractal analysis study of 367 daily global solar radiation sequences measured with a sampling rate of 1 Hz over one year at Guadeloupean Archipelago (French West Indies) located at 16o15'N latitude and 60o30'W longitude. The mean power spectrum computed follows a power law behaviour close to the Kolmogorov spectrum. The intermittent and multifractal properties of global solar radiation data are investigated using several methods. Under this basis, a characterization for each day using three multifractal parameters is proposed.

State Support: A Prerequisite for Global Health Network Effectiveness Comment on "Four Challenges that Global Health Networks Face".

PubMed

Marten, Robert; Smith, Richard D

2017-07-24

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks' success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks' effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Sense-making for intelligence analysis on social media data

NASA Astrophysics Data System (ADS)

Pritzkau, Albert

2016-05-01

Social networks, in particular online social networks as a subset, enable the analysis of social relationships which are represented by interaction, collaboration, or other sorts of influence between people. Any set of people and their internal social relationships can be modelled as a general social graph. These relationships are formed by exchanging emails, making phone calls, or carrying out a range of other activities that build up the network. This paper presents an overview of current approaches to utilizing social media as a ubiquitous sensor network in the context of national and global security. Exploitation of social media is usually an interdisciplinary endeavour, in which the relevant technologies and methods are identified and linked in order ultimately demonstrate selected applications. Effective and efficient intelligence is usually accomplished in a combined human and computer effort. Indeed, the intelligence process heavily depends on combining a human's flexibility, creativity, and cognitive ability with the bandwidth and processing power of today's computers. To improve the usability and accuracy of the intelligence analysis we will have to rely on data-processing tools at the level of natural language. Especially the collection and transformation of unstructured data into actionable, structured data requires scalable computational algorithms ranging from Artificial Intelligence, via Machine Learning, to Natural Language Processing (NLP). To support intelligence analysis on social media data, social media analytics is concerned with developing and evaluating computational tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data. Analytics methods are employed to extract of significant patterns that might not be obvious. As a result, different data representations rendering distinct aspects of content and interactions serve as a means to adapt the focus of the intelligence analysis to specific information requests.

International global network of fiducial stations: Scientific and implementation issues

NASA Astrophysics Data System (ADS)

1991-11-01

In this report, an ad hoc panel of the National Research Council's Committee on Geodesy, Board of Earth Sciences and Resources (1) evaluates the scientific importance of a global network of fiducial sites, monitored very precisely, using a combination of surface- and space-geodetic techniques; (2) examines strategies for implementing and operating such a network; and (3) assesses whether such a network would provide a suitable global infrastructure for geodetic and other geophysical systems of the next century. The panel concludes that a global network of fiducial sites would be a valuable tool for addressing global change issues and play a critical role in providing a reference frame for scientific Earth missions. The panel suggests that existing global networks be integrated and anticipates that such a network would grow from about 30 to the ultimate size of about 200 fiducial sites. It is noted that such a global network will provide a long-term infrastructure for geodetic and geophysical studies. The panel expects that these fiducial sites would evolve into terrestrial observatories or laboratories that would permit more comprehensive studies of the Earth than those now possible.

International global network of fiducial stations: Scientific and implementation issues

NASA Technical Reports Server (NTRS)

1991-01-01

In this report, an ad hoc panel of the National Research Council's Committee on Geodesy, Board of Earth Sciences and Resources (1) evaluates the scientific importance of a global network of fiducial sites, monitored very precisely, using a combination of surface- and space-geodetic techniques; (2) examines strategies for implementing and operating such a network; and (3) assesses whether such a network would provide a suitable global infrastructure for geodetic and other geophysical systems of the next century. The panel concludes that a global network of fiducial sites would be a valuable tool for addressing global change issues and play a critical role in providing a reference frame for scientific Earth missions. The panel suggests that existing global networks be integrated and anticipates that such a network would grow from about 30 to the ultimate size of about 200 fiducial sites. It is noted that such a global network will provide a long-term infrastructure for geodetic and geophysical studies. The panel expects that these fiducial sites would evolve into terrestrial observatories or laboratories that would permit more comprehensive studies of the Earth than those now possible.

A mixed SIR-SIS model to contain a virus spreading through networks with two degrees

NASA Astrophysics Data System (ADS)

Essouifi, Mohamed; Achahbar, Abdelfattah

Due to the fact that the “nodes” and “links” of real networks are heterogeneous, to model computer viruses prevalence throughout the Internet, we borrow the idea of the reduced scale free network which was introduced recently. The purpose of this paper is to extend the previous deterministic two subchains of Susceptible-Infected-Susceptible (SIS) model into a mixed Susceptible-Infected-Recovered and Susceptible-Infected-Susceptible (SIR-SIS) model to contain the computer virus spreading over networks with two degrees. Moreover, we develop its stochastic counterpart. Due to the high protection and security taken for hubs class, we suggest to treat it by using SIR epidemic model rather than the SIS one. The analytical study reveals that the proposed model admits a stable viral equilibrium. Thus, it is shown numerically that the mean dynamic behavior of the stochastic model is in agreement with the deterministic one. Unlike the infection densities i2 and i which both tend to a viral equilibrium for both approaches as in the previous study, i1 tends to the virus-free equilibrium. Furthermore, since a proportion of infectives are recovered, the global infection density i is minimized. Therefore, the permanent presence of viruses in the network due to the lower-degree nodes class. Many suggestions are put forward for containing viruses propagation and minimizing their damages.

Interdependent networks - Topological percolation research and application in finance

NASA Astrophysics Data System (ADS)

Zhou, Di

This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks and applying simulation and numerical methods to study the robustness of the network system, and ii) applying statistical physics concepts and methods to quantitatively analyze complex systems and applying the theoretical framework to study real-world systems. In part I, we focus on developing theories of interdependent networks as well as building computer simulation models, which includes three parts: 1) We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the networks significantly changes the critical density of failures, which can trigger the total disruption of the two-network system. Specifically, we find that the assortativity within a single network decreases the robustness of the entire system. 2) We study the percolation behavior of two interdependent scale-free (SF) networks under random failure of 1-p fraction of nodes. We find that as the coupling strength q between the two networks reduces from 1 (fully coupled) to 0 (no coupling), there exist two critical coupling strengths q1 and q2 , which separate the behaviors of the giant component as a function of p into three different regions, and for q2 < q < q 1 , we observe a hybrid order phase transition phenomenon. 3) We study the robustness of n interdependent networks with partially support-dependent relationship both analytically and numerically. We study a starlike network of n Erdos-Renyi (ER), SF networks and a looplike network of n ER networks, and we find for starlike networks, their phase transition regions change with n, but for looplike networks the phase regions change with average degree k . In part II, we apply concepts and methods developed in statistical physics to study economic systems. We analyze stock market indices and foreign exchange daily returns for 60 countries over the period of 1999-2012. We build a multi-layer network model based on different correlation measures, and introduce a dynamic network model to simulate and analyze the initializing and spreading of financial crisis. Using different computational approaches and econometric tests, we find atypical behavior of the cross correlations and community formations in the financial networks that we study during the financial crisis of 2008. For example, the overall correlation of stock market increases during crisis while the correlation between stock market and foreign exchange market decreases. The dramatic increase in correlations between a specific nation and other nations may indicate that this nation could trigger a global financial crisis. Specifically, core countries that have higher correlations with other countries and larger Gross Domestic Product (GDP) values spread financial crisis quite effectively, yet some countries with small GDPs like Greece and Cyprus are also effective in propagating systemic risk and spreading global financial crisis.

Resting-state global functional connectivity as a biomarker of cognitive reserve in mild cognitive impairment.

PubMed

Franzmeier, N; Caballero, M Á Araque; Taylor, A N W; Simon-Vermot, L; Buerger, K; Ertl-Wagner, B; Mueller, C; Catak, C; Janowitz, D; Baykara, E; Gesierich, B; Duering, M; Ewers, M

2017-04-01

Cognitive reserve (CR) shows protective effects in Alzheimer's disease (AD) and reduces the risk of dementia. Despite the clinical significance of CR, a clinically useful diagnostic biomarker of brain changes underlying CR in AD is not available yet. Our aim was to develop a fully-automated approach applied to fMRI to produce a biomarker associated with CR in subjects at increased risk of AD. We computed resting-state global functional connectivity (GFC), i.e. the average connectivity strength, for each voxel within the cognitive control network, which may sustain CR due to its central role in higher cognitive function. In a training sample including 43 mild cognitive impairment (MCI) subjects and 24 healthy controls (HC), we found that MCI subjects with high CR (> median of years of education, CR+) showed increased frequency of high GFC values compared to MCI-CR- and HC. A summary index capturing such a surplus frequency of high GFC was computed (called GFC reserve (GFC-R) index). GFC-R discriminated MCI-CR+ vs. MCI-CR-, with the area under the ROC = 0.84. Cross-validation in an independently recruited test sample of 23 MCI subjects showed that higher levels of the GFC-R index predicted higher years of education and an alternative questionnaire-based proxy of CR, controlled for memory performance, gray matter of the cognitive control network, white matter hyperintensities, age, and gender. In conclusion, the GFC-R index that captures GFC changes within the cognitive control network provides a biomarker candidate of functional brain changes of CR in patients at increased risk of AD.

Global thermal analysis of air-air cooled motor based on thermal network

NASA Astrophysics Data System (ADS)

Hu, Tian; Leng, Xue; Shen, Li; Liu, Haidong

2018-02-01

The air-air cooled motors with high efficiency, large starting torque, strong overload capacity, low noise, small vibration and other characteristics, are widely used in different department of national industry, but its cooling structure is complex, it requires the motor thermal management technology should be high. The thermal network method is a common method to calculate the temperature field of the motor, it has the advantages of small computation time and short time consuming, it can save a lot of time in the initial design phase of the motor. The domain analysis of air-air cooled motor and its cooler was based on thermal network method, the combined thermal network model was based, the main components of motor internal and external cooler temperature were calculated and analyzed, and the temperature rise test results were compared to verify the correctness of the combined thermal network model, the calculation method can satisfy the need of engineering design, and provide a reference for the initial and optimum design of the motor.

The LCOGT Network for Solar System Science

NASA Astrophysics Data System (ADS)

Lister, Tim

2012-10-01

Las Cumbres Observatory Global Telescope (LCOGT) network is a planned homogeneous network of over 35 telescopes at 6 locations in the northern and southern hemispheres. This network is versatile and designed to respond rapidly to target of opportunity events and also to do long term monitoring of slowly changing astronomical phenomena. The global coverage of the network and the apertures of telescope available make LCOGT ideal for follow-up and characterization of Solar System objects (e.g. asteroids, Kuiper Belt Objects, comets, Near-Earth Objects (NEOs)) and ultimately for the discovery of new objects. Currently LCOGT is operating the two 2m Faulkes Telescopes at Haleakala, Maui and Siding Spring Observatory, Australia and in March 2012 completed the install of the first member of the new 1m telescope network at McDonald Observatory, Texas. Further deployments of six to eight 1m telescopes to CTIO in Chile, SAAO in South Africa and Siding Spring Observatory are expected in late 2012-early 2013. I am using the growing LCOGT network to confirm newly detected NEO candidates produced by PanSTARRS (PS1) and other sky surveys and to obtain follow-up astrometry and photometry for radar-targeted objects. I have developed an automated system to retrieve new PS1 NEOs, compute orbits, plan observations and automatically schedule them for follow-up on the robotic telescopes of the LCOGT Network. In the future, LCOGT has proposed to develop a Minor Planet Investigation Project (MPIP) that will address the existing lack of resources for minor planet follow-up, takes advantage of ever-increasing new datasets, and develops a platform for broad public participation in relevant scientific exploration. We plan to produce a cloud-based Solar System investigation environment, a citizen science project (AgentNEO), and a cyberlearning environment, all under the umbrella of MPIP.

IPv6 Test Bed for Testing Aeronautical Applications

NASA Technical Reports Server (NTRS)

Wilkins, Ryan; Zernic, Michael; Dhas, Chris

2004-01-01

Aviation industries in United States and in Europe are undergoing a major paradigm shift in the introduction of new network technologies. In the US, NASA is also actively investigating the feasibility of IPv6 based networks for the aviation needs of the United States. In Europe, the Eurocontrol lead, Internet Protocol for Aviation Exchange (iPAX) Working Group is actively investigating the various ways of migrating the aviation authorities backbone infrastructure from X.25 based networks to an IPv6 based network. For the last 15 years, the global aviation community has pursued the development and implementation of an industry-specific set of communications standards known as the Aeronautical Telecommunications Network (ATN). These standards are now beginning to affect the emerging military Global Air Traffic Management (GATM) community as well as the commercial air transport community. Efforts are continuing to gain a full understanding of the differences and similarities between ATN and Internet architectures as related to Communications, Navigation, and Surveillance (CNS) infrastructure choices. This research paper describes the implementation of the IPv6 test bed at NASA GRC, and Computer Networks & Software, Inc. and these two test beds are interface to Eurocontrol over the IPv4 Internet. This research work looks into the possibility of providing QoS performance for Aviation application in an IPv6 network as is provided in an ATN based network. The test bed consists of three autonomous systems. The autonomous system represents CNS domain, NASA domain and a EUROCONTROL domain. The primary mode of connection between CNS IPv6 testbed and NASA and EUROCONTROL IPv6 testbed is initially a set of IPv6 over IPv4 tunnels. The aviation application under test (CPDLC) consists of two processes running on different IPv6 enabled machines.

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis.

PubMed

Ripp, Isabelle; Zur Nieden, Anna-Nora; Blankenagel, Sonja; Franzmeier, Nicolai; Lundström, Johan N; Freiherr, Jessica

2018-05-07

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing. © 2018 Wiley Periodicals, Inc.

Opinion formation in time-varying social networks: The case of the naming game

NASA Astrophysics Data System (ADS)

Maity, Suman Kalyan; Manoj, T. Venkat; Mukherjee, Animesh

2012-09-01

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

The Promise of Global Networks. 1999 Annual Review.

ERIC Educational Resources Information Center

Institute for Information Studies, Queenstown, MD.

This collection of commissioned papers provides a variety of perspectives on the impact of global information networks. The following articles are included: "The Promise of Global Networks: An Introduction" (Jorge Reina Schement); "Architecture and Expectations: Networks of the World--Unite!" (Marjory S. Blumenthal); "The…

Intelligent earthquake data processing for global adjoint tomography

NASA Astrophysics Data System (ADS)

Chen, Y.; Hill, J.; Li, T.; Lei, W.; Ruan, Y.; Lefebvre, M. P.; Tromp, J.

2016-12-01

Due to the increased computational capability afforded by modern and future computing architectures, the seismology community is demanding a more comprehensive understanding of the full waveform information from the recorded earthquake seismograms. Global waveform tomography is a complex workflow that matches observed seismic data with synthesized seismograms by iteratively updating the earth model parameters based on the adjoint state method. This methodology allows us to compute a very accurate model of the earth's interior. The synthetic data is simulated by solving the wave equation in the entire globe using a spectral-element method. In order to ensure the inversion accuracy and stability, both the synthesized and observed seismograms must be carefully pre-processed. Because the scale of the inversion problem is extremely large and there is a very large volume of data to both be read and written, an efficient and reliable pre-processing workflow must be developed. We are investigating intelligent algorithms based on a machine-learning (ML) framework that will automatically tune parameters for the data processing chain. One straightforward application of ML in data processing is to classify all possible misfit calculation windows into usable and unusable ones, based on some intelligent ML models such as neural network, support vector machine or principle component analysis. The intelligent earthquake data processing framework will enable the seismology community to compute the global waveform tomography using seismic data from an arbitrarily large number of earthquake events in the fastest, most efficient way.

Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

A comprehensive method for GNSS data quality determination to improve ionospheric data analysis.

PubMed

Kim, Minchan; Seo, Jiwon; Lee, Jiyun

2014-08-14

Global Navigation Satellite Systems (GNSS) are now recognized as cost-effective tools for ionospheric studies by providing the global coverage through worldwide networks of GNSS stations. While GNSS networks continue to expand to improve the observability of the ionosphere, the amount of poor quality GNSS observation data is also increasing and the use of poor-quality GNSS data degrades the accuracy of ionospheric measurements. This paper develops a comprehensive method to determine the quality of GNSS observations for the purpose of ionospheric studies. The algorithms are designed especially to compute key GNSS data quality parameters which affect the quality of ionospheric product. The quality of data collected from the Continuously Operating Reference Stations (CORS) network in the conterminous United States (CONUS) is analyzed. The resulting quality varies widely, depending on each station and the data quality of individual stations persists for an extended time period. When compared to conventional methods, the quality parameters obtained from the proposed method have a stronger correlation with the quality of ionospheric data. The results suggest that a set of data quality parameters when used in combination can effectively select stations with high-quality GNSS data and improve the performance of ionospheric data analysis.

A Comprehensive Method for GNSS Data Quality Determination to Improve Ionospheric Data Analysis

PubMed Central

Kim, Minchan; Seo, Jiwon; Lee, Jiyun

2014-01-01

Global Navigation Satellite Systems (GNSS) are now recognized as cost-effective tools for ionospheric studies by providing the global coverage through worldwide networks of GNSS stations. While GNSS networks continue to expand to improve the observability of the ionosphere, the amount of poor quality GNSS observation data is also increasing and the use of poor-quality GNSS data degrades the accuracy of ionospheric measurements. This paper develops a comprehensive method to determine the quality of GNSS observations for the purpose of ionospheric studies. The algorithms are designed especially to compute key GNSS data quality parameters which affect the quality of ionospheric product. The quality of data collected from the Continuously Operating Reference Stations (CORS) network in the conterminous United States (CONUS) is analyzed. The resulting quality varies widely, depending on each station and the data quality of individual stations persists for an extended time period. When compared to conventional methods, the quality parameters obtained from the proposed method have a stronger correlation with the quality of ionospheric data. The results suggest that a set of data quality parameters when used in combination can effectively select stations with high-quality GNSS data and improve the performance of ionospheric data analysis. PMID:25196005

Unified Lunar Control Network 2005 and Topographic Model

NASA Technical Reports Server (NTRS)

Archinal, B. A.; Rosiek, M. R.; Redding, B. L.

2005-01-01

There are currently two generally accepted lunar control networks. These are the Unified Lunar Control Network (ULCN) and the Clementine Lunar Control Network (CLCN), both derived by M. Davies and T. Colvin at RAND. We address here our efforts to merge and improve these networks into a new ULCN. The ULCN was described in the last major publication about a lunar control network. The statistics on this and the other networks discussed here. Images for this network are from the Apollo, Mariner 10, and Galileo missions, and Earth-based photographs. The importance of this network is that its accuracy is relatively well quantified and published information on the network is available. The CLCN includes measurements on 43,871 Clementine 750-nm images - the largest planetary control network ever computed. This purpose of this network was to determine the geometry for the Clementine Basemap Mosiac (CBM). The geometry of that mosaic was used to produce the Clementine UVVIS digital image model and the Near-Infrared Global Multispectral Map of the Moon from Clementine. Through the extensive use of these products, they and the underlying CLCN in effect define the generally accepted current coordinate system for reporting and describing the location of lunar coordinates. However, no publication describes the CLCN itself.

Data Visualization and Analysis Tools for the Global Precipitation Measurement (GPM) Validation Network

NASA Technical Reports Server (NTRS)

Morris, Kenneth R.; Schwaller, Mathew

2010-01-01

The Validation Network (VN) prototype for the Global Precipitation Measurement (GPM) Mission compares data from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR) to similar measurements from U.S. and international operational weather radars. This prototype is a major component of the GPM Ground Validation System (GVS). The VN provides a means for the precipitation measurement community to identify and resolve significant discrepancies between the ground radar (GR) observations and similar satellite observations. The VN prototype is based on research results and computer code described by Anagnostou et al. (2001), Bolen and Chandrasekar (2000), and Liao et al. (2001), and has previously been described by Morris, et al. (2007). Morris and Schwaller (2009) describe the PR-GR volume-matching algorithm used to create the VN match-up data set used for the comparisons. This paper describes software tools that have been developed for visualization and statistical analysis of the original and volume matched PR and GR data.

Broadband Satellite Technologies and Markets Assessed

NASA Technical Reports Server (NTRS)

Wallett, Thomas M.

1999-01-01

The current usage of broadband (data rate greater than 64 kilobits per second (kbs)) for multimedia network computer applications is increasing, and the need for network communications technologies and systems to support this use is also growing. Satellite technology will likely be an important part of the National Information Infrastructure (NII) and the Global Information Infrastructure (GII) in the next decade. Several candidate communications technologies that may be used to carry a portion of the increased data traffic have been reviewed, and estimates of the future demand for satellite capacity have been made. A study was conducted by the NASA Lewis Research Center to assess the satellite addressable markets for broadband applications. This study effort included four specific milestones: (1) assess the changing nature of broadband applications and their usage, (2) assess broadband satellite and terrestrial technologies, (3) estimate the size of the global satellite addressable market from 2000 to 2010, and (4) identify how the impact of future technology developments could increase the utility of satellite-based transport to serve this market.

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

Inferring global network properties from egocentric data with applications to epidemics.

PubMed

Britton, Tom; Trapman, Pieter

2015-03-01

Social networks are often only partly observed, and it is sometimes desirable to infer global properties of the network from 'egocentric' data. In the current paper, we study different types of egocentric data, and show which global network properties are consistent with data. Two global network properties are considered: the size of the largest connected component (the giant) and the size of an epidemic outbreak taking place on the network. The main conclusion is that, in most cases, egocentric data allow for a large range of possible sizes of the giant and the outbreak, implying that egocentric data carry very little information about these global properties. The asymptotic size of the giant and the outbreak is also characterized, assuming the network is selected uniformly among networks with prescribed egocentric data. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Military Cyberspace: From Evolution to Revolution

DTIC Science & Technology

2012-02-08

support the GCCs and enable USCYBERCOM to accomplish its mission? 15. SUBJECT TERMS Network Operations, Global Information Grid ( GIG ), Network...DATE: 08 February 2012 WORD COUNT: 5,405 PAGES: 30 KEY TERMS: Network Operations, Global Information Grid ( GIG ), Network Architecture...defense of the DOD global information grid ( GIG ). The DOD must pursue an enterprise approach to network management in the cyberspace domain to

Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study.

PubMed

Lenters, Lindsey M; Cole, Donald C; Godoy-Ruiz, Paula

2014-01-25

Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers' careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world.

Addressing Data Analysis Challenges in Gravitational Wave Searches Using the Particle Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

Weerathunga, Thilina Shihan

2017-08-01

Gravitational waves are a fundamental prediction of Einstein's General Theory of Relativity. The first experimental proof of their existence was provided by the Nobel Prize winning discovery by Taylor and Hulse of orbital decay in a binary pulsar system. The first detection of gravitational waves incident on earth from an astrophysical source was announced in 2016 by the LIGO Scientific Collaboration, launching the new era of gravitational wave (GW) astronomy. The signal detected was from the merger of two black holes, which is an example of sources called Compact Binary Coalescences (CBCs). Data analysis strategies used in the search for CBC signals are derivatives of the Maximum-Likelihood (ML) method. The ML method applied to data from a network of geographically distributed GW detectors--called fully coherent network analysis--is currently the best approach for estimating source location and GW polarization waveforms. However, in the case of CBCs, especially for lower mass systems (O(1M solar masses)) such as double neutron star binaries, fully coherent network analysis is computationally expensive. The ML method requires locating the global maximum of the likelihood function over a nine dimensional parameter space, where the computation of the likelihood at each point requires correlations involving O(104) to O(106) samples between the data and the corresponding candidate signal waveform template. Approximations, such as semi-coherent coincidence searches, are currently used to circumvent the computational barrier but incur a concomitant loss in sensitivity. We explored the effectiveness of Particle Swarm Optimization (PSO), a well-known algorithm in the field of swarm intelligence, in addressing the fully coherent network analysis problem. As an example, we used a four-detector network consisting of the two LIGO detectors at Hanford and Livingston, Virgo and Kagra, all having initial LIGO noise power spectral densities, and show that PSO can locate the global maximum with less than 240,000 likelihood evaluations for a component mass range of 1.0 to 10.0 solar masses at a realistic coherent network signal to noise ratio of 9.0. Our results show that PSO can successfully deliver a fully-coherent all-sky search with < (1/10 ) the number of likelihood evaluations needed for a grid-based search. Used as a follow-up step, the savings in the number of likelihood evaluations may also reduce latency in obtaining ML estimates of source parameters in semi-coherent searches.

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

NASA Astrophysics Data System (ADS)

Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

2016-12-01

The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

Efficient Evaluation Functions for Multi-Rover Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian; Tumer, Kagan

2004-01-01

Evolutionary computation can be a powerful tool in cresting a control policy for a single agent receiving local continuous input. This paper extends single-agent evolutionary computation to multi-agent systems, where a collection of agents strives to maximize a global fitness evaluation function that rates the performance of the entire system. This problem is solved in a distributed manner, where each agent evolves its own population of neural networks that are used as the control policies for the agent. Each agent evolves its population using its own agent-specific fitness evaluation function. We propose to create these agent-specific evaluation functions using the theory of collectives to avoid the coordination problem where each agent evolves a population that maximizes its own fitness function, yet the system has a whole achieves low values of the global fitness function. Instead we will ensure that each fitness evaluation function is both "aligned" with the global evaluation function and is "learnable," i.e., the agents can readily see how their behavior affects their evaluation function. We then show how these agent-specific evaluation functions outperform global evaluation methods by up to 600% in a domain where a set of rovers attempt to maximize the amount of information observed while navigating through a simulated environment.

Generating Seismograms with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of neural networks by estimating the quality and uncertainties of the generated seismograms.

Global 30m Height Above the Nearest Drainage

NASA Astrophysics Data System (ADS)

Donchyts, Gennadii; Winsemius, Hessel; Schellekens, Jaap; Erickson, Tyler; Gao, Hongkai; Savenije, Hubert; van de Giesen, Nick

2016-04-01

Variability of the Earth surface is the primary characteristics affecting the flow of surface and subsurface water. Digital elevation models, usually represented as height maps above some well-defined vertical datum, are used a lot to compute hydrologic parameters such as local flow directions, drainage area, drainage network pattern, and many others. Usually, it requires a significant effort to derive these parameters at a global scale. One hydrological characteristic introduced in the last decade is Height Above the Nearest Drainage (HAND): a digital elevation model normalized using nearest drainage. This parameter has been shown to be useful for many hydrological and more general purpose applications, such as landscape hazard mapping, landform classification, remote sensing and rainfall-runoff modeling. One of the essential characteristics of HAND is its ability to capture heterogeneities in local environments, difficult to measure or model otherwise. While many applications of HAND were published in the academic literature, no studies analyze its variability on a global scale, especially, using higher resolution DEMs, such as the new, one arc-second (approximately 30m) resolution version of SRTM. In this work, we will present the first global version of HAND computed using a mosaic of two DEMS: 30m SRTM and Viewfinderpanorama DEM (90m). The lower resolution DEM was used to cover latitudes above 60 degrees north and below 56 degrees south where SRTM is not available. We compute HAND using the unmodified version of the input DEMs to ensure consistency with the original elevation model. We have parallelized processing by generating a homogenized, equal-area version of HydroBASINS catchments. The resulting catchment boundaries were used to perform processing using 30m resolution DEM. To compute HAND, a new version of D8 local drainage directions as well as flow accumulation were calculated. The latter was used to estimate river head by incorporating fixed and variable thresholding methods. The resulting HAND dataset was analyzed regarding its spatial variability and to assess the global distribution of the main landform types: valley, ecotone, slope, and plateau. The method used to compute HAND was implemented using PCRaster software, running on Google Compute Engine platform running under Ubuntu Linux. The Google Earth Engine was used to perform mosaicing and clipping of the original DEMs as well as to provide access to the final product. The effort took about three months of computing time on eight core CPU virtual machine.

Generating clustered scale-free networks using Poisson based localization of edges

NASA Astrophysics Data System (ADS)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems.

PubMed

Mohammed, Abdul-Wahid; Xu, Yang; Hu, Haixiao; Agyemang, Brighter

2016-09-21

In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-06-04

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-03-01

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

Advanced algorithms for distributed fusion

NASA Astrophysics Data System (ADS)

Gelfand, A.; Smith, C.; Colony, M.; Bowman, C.; Pei, R.; Huynh, T.; Brown, C.

2008-03-01

The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an environment, centralized fusion approaches will have limited application due to the constraints of real-time communications networks and computational resources. To overcome these limitations, we are developing a formalized architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created and managed information network. This network will support the incorporation and utilization of low level tracking information within the Army Distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.

Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

PubMed Central

Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

Impact of the topology of global macroeconomic network on the spreading of economic crises.

PubMed

Lee, Kyu-Min; Yang, Jae-Suk; Kim, Gunn; Lee, Jaesung; Goh, Kwang-Il; Kim, In-mook

2011-03-31

Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of a network of interacting macroeconomic agents. In this regard, from the perspective of collective network dynamics, here we explore how the topology of the global macroeconomic network affects the patterns of spreading of economic crises. Using a simple toy model of crisis spreading, we demonstrate that an individual country's role in crisis spreading is not only dependent on its gross macroeconomic capacities, but also on its local and global connectivity profile in the context of the world economic network. We find that on one hand clustering of weak links at the regional scale can significantly aggravate the spread of crises, but on the other hand the current network structure at the global scale harbors higher tolerance of extreme crises compared to more "globalized" random networks. These results suggest that there can be a potential hidden cost in the ongoing globalization movement towards establishing less-constrained, trans-regional economic links between countries, by increasing vulnerability of the global economic system to extreme crises.

A globally convergent MC algorithm with an adaptive learning rate.

PubMed

Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian

2012-02-01

This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set.

PubMed

Kruschwitz, J D; Waller, L; Daedelow, L S; Walter, H; Veer, I M

2018-05-01

One hallmark example of a link between global topological network properties of complex functional brain connectivity and cognitive performance is the finding that general intelligence may depend on the efficiency of the brain's intrinsic functional network architecture. However, although this association has been featured prominently over the course of the last decade, the empirical basis for this broad association of general intelligence and global functional network efficiency is quite limited. In the current study, we set out to replicate the previously reported association between general intelligence and global functional network efficiency using the large sample size and high quality data of the Human Connectome Project, and extended the original study by testing for separate association of crystallized and fluid intelligence with global efficiency, characteristic path length, and global clustering coefficient. We were unable to provide evidence for the proposed association between general intelligence and functional brain network efficiency, as was demonstrated by van den Heuvel et al. (2009), or for any other association with the global network measures employed. More specifically, across multiple network definition schemes, ranging from voxel-level networks to networks of only 100 nodes, no robust associations and only very weak non-significant effects with a maximal R 2 of 0.01 could be observed. Notably, the strongest (non-significant) effects were observed in voxel-level networks. We discuss the possibility that the low power of previous studies and publication bias may have led to false positive results fostering the widely accepted notion of general intelligence being associated to functional global network efficiency. Copyright © 2018 Elsevier Inc. All rights reserved.

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

PubMed Central

Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

2016-01-01

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes. PMID:27148015

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain.

PubMed

Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

2016-01-01

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

Functional brain networks in schizophrenia: a review.

PubMed

Calhoun, Vince D; Eichele, Tom; Pearlson, Godfrey

2009-01-01

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

Global impacts of the meat trade on in-stream organic river pollution: the importance of spatially distributed hydrological conditions

NASA Astrophysics Data System (ADS)

Wen, Yingrong; Schoups, Gerrit; van de Giesen, Nick

2018-01-01

In many regions of the world, intensive livestock farming has become a significant source of organic river pollution. As the international meat trade is growing rapidly, the environmental impacts of meat production within one country can occur either domestically or internationally. The goal of this paper is to quantify the impacts of the international meat trade on global organic river pollution at multiple scales (national, regional and gridded). Using the biological oxygen demand (BOD) as an overall indicator of organic river pollution, we compute the spatially distributed organic pollution in global river networks with and without a meat trade, where the without-trade scenario assumes that meat imports are replaced by local production. Our analysis reveals a reduction in the livestock population and production of organic pollutants at the global scale as a result of the international meat trade. However, the actual environmental impact of trade, as quantified by in-stream BOD concentrations, is negative; i.e. we find a slight increase in polluted river segments. More importantly, our results show large spatial variability in local (grid-scale) impacts that do not correlate with local changes in BOD loading, which illustrates: (1) the significance of accounting for the spatial heterogeneity of hydrological processes along river networks, and (2) the limited value of looking at country-level or global averages when estimating the actual impacts of trade on the environment.

Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with acoustic Doppler current profiler and global positioning system technology, 1996

USGS Publications Warehouse

Wilson, J.T.; Morlock, S.E.; Baker, N.T.

1997-01-01

Acoustic Doppler current profiler, global positioning system, and geographic information system technology were used to map the bathymetry of Morse and Geist Reservoirs, two artificial lakes used for public water supply in central Indiana. The project was a pilot study to evaluate the use of the technologies for bathymetric surveys. Bathymetric surveys were last conducted in 1978 on Morse Reservoir and in 1980 on Geist Reservoir; those surveys were done with conventional methods using networks of fathometer transects. The 1996 bathymetric surveys produced updated estimates of reservoir volumes that will serve as base-line data for future estimates of storage capacity and sedimentation rates.An acoustic Doppler current profiler and global positioning system receiver were used to collect water-depth and position data from April 1996 through October 1996. All water-depth and position data were imported to a geographic information system to create a data base. The geographic information system then was used to generate water-depth contour maps and to compute the volumes for each reservoir.The computed volume of Morse Reservoir was 22,820 acre-feet (7.44 billion gallons), with a surface area of 1,484 acres. The computed volume of Geist Reservoir was 19,280 acre-feet (6.29 billion gallons), with a surface area of 1,848 acres. The computed 1996 reservoir volumes are less than the design volumes and indicate that sedimentation has occurred in both reservoirs. Cross sections were constructed from the computer-generated surfaces for 1996 and compared to the fathometer profiles from the 1978 and 1980 surveys; analysis of these cross sections also indicates that some sedimentation has occurred in both reservoirs.The acoustic Doppler current profiler, global positioning system, and geographic information system technologies described in this report produced bathymetric maps and volume estimates more efficiently and with comparable or greater resolution than conventional bathymetry methods.

On multigrid methods for the Navier-Stokes Computer

NASA Technical Reports Server (NTRS)

Nosenchuck, D. M.; Krist, S. E.; Zang, T. A.

1988-01-01

The overall architecture of the multipurpose parallel-processing Navier-Stokes Computer (NSC) being developed by Princeton and NASA Langley (Nosenchuck et al., 1986) is described and illustrated with extensive diagrams, and the NSC implementation of an elementary multigrid algorithm for simulating isotropic turbulence (based on solution of the incompressible time-dependent Navier-Stokes equations with constant viscosity) is characterized in detail. The present NSC design concept calls for 64 nodes, each with the performance of a class VI supercomputer, linked together by a fiber-optic hypercube network and joined to a front-end computer by a global bus. In this configuration, the NSC would have a storage capacity of over 32 Gword and a peak speed of over 40 Gflops. The multigrid Navier-Stokes code discussed would give sustained operation rates of about 25 Gflops.

Perfusion network shift during seizures in medial temporal lobe epilepsy.

PubMed

Sequeira, Karen M; Tabesh, Ali; Sainju, Rup K; DeSantis, Stacia M; Naselaris, Thomas; Joseph, Jane E; Ahlman, Mark A; Spicer, Kenneth M; Glazier, Steve S; Edwards, Jonathan C; Bonilha, Leonardo

2013-01-01

Medial temporal lobe epilepsy (MTLE) is associated with limbic atrophy involving the hippocampus, peri-hippocampal and extra-temporal structures. While MTLE is related to static structural limbic compromise, it is unknown whether the limbic system undergoes dynamic regional perfusion network alterations during seizures. In this study, we aimed to investigate state specific (i.e. ictal versus interictal) perfusional limbic networks in patients with MTLE. We studied clinical information and single photon emission computed tomography (SPECT) images obtained with intravenous infusion of the radioactive tracer Technetium- Tc 99 m Hexamethylpropyleneamine Oxime (Tc-99 m HMPAO) during ictal and interictal state confirmed by video-electroencephalography (VEEG) in 20 patients with unilateral MTLE (12 left and 8 right MTLE). Pair-wise voxel-based analyses were used to define global changes in tracer between states. Regional tracer uptake was calculated and state specific adjacency matrices were constructed based on regional correlation of uptake across subjects. Graph theoretical measures were applied to investigate global and regional state specific network reconfigurations. A significant increase in tracer uptake was observed during the ictal state in the medial temporal region, cerebellum, thalamus, insula and putamen. From network analyses, we observed a relative decreased correlation between the epileptogenic temporal region and remaining cortex during the interictal state, followed by a surge of cross-correlated perfusion in epileptogenic temporal-limbic structures during a seizure, corresponding to local network integration. These results suggest that MTLE is associated with a state specific perfusion and possibly functional organization consisting of a surge of limbic cross-correlated tracer uptake during a seizure, with a relative disconnection of the epileptogenic temporal lobe in the interictal period. This pattern of state specific shift in metabolic networks in MTLE may improve the understanding of epileptogenesis and neuropsychological impairments associated with MTLE.

Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk.

PubMed

Li, Yongsheng; Jin, Xiyun; Wang, Zishan; Li, Lili; Chen, Hong; Lin, Xiaoyu; Yi, Song; Zhang, Yunpeng; Xu, Juan

2017-10-25

Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA-target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA-target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

State Support: A Prerequisite for Global Health Network Effectiveness

PubMed Central

Marten, Robert; Smith, Richard D.

2018-01-01

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. PMID:29524958

Global Electricity Trade Network: Structures and Implications

PubMed Central

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

Global Electricity Trade Network: Structures and Implications.

PubMed

Ji, Ling; Jia, Xiaoping; Chiu, Anthony S F; Xu, Ming

2016-01-01

Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions.

Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study

PubMed Central

2014-01-01

Background Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers’ careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. Methods A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Results Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Conclusions Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world. PMID:24460819

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

PubMed

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-08-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

PubMed Central

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-01-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. PMID:26317784

Privacy Management and Networked PPD Systems - Challenges Solutions.

PubMed

Ruotsalainen, Pekka; Pharow, Peter; Petersen, Francoise

2015-01-01

Modern personal portable health devices (PPDs) become increasingly part of a larger, inhomogeneous information system. Information collected by sensors are stored and processed in global clouds. Services are often free of charge, but at the same time service providers' business model is based on the disclosure of users' intimate health information. Health data processed in PPD networks is not regulated by health care specific legislation. In PPD networks, there is no guarantee that stakeholders share same ethical principles with the user. Often service providers have own security and privacy policies and they rarely offer to the user possibilities to define own, or adapt existing privacy policies. This all raises huge ethical and privacy concerns. In this paper, the authors have analyzed privacy challenges in PPD networks from users' viewpoint using system modeling method and propose the principle "Personal Health Data under Personal Control" must generally be accepted at global level. Among possible implementation of this principle, the authors propose encryption, computer understandable privacy policies, and privacy labels or trust based privacy management methods. The latter can be realized using infrastructural trust calculation and monitoring service. A first step is to require the protection of personal health information and the principle proposed being internationally mandatory. This requires both regulatory and standardization activities, and the availability of open and certified software application which all service providers can implement. One of those applications should be the independent Trust verifier.

Global Seismic Monitoring: Past, Present, and Future

NASA Astrophysics Data System (ADS)

Zoback, M.; Benz, H.; Oppenheimer, D.

2007-12-01

Global seismological observations began in April 1889 when an earthquake in Tokyo, Japan was accurately recorded in Germany on two different horizontal pendulum instruments. However, modern global observational seismology really began 46 years ago when the 120-station World Wide Standard Seismograph Network was installed by the US to monitor underground nuclear tests and earthquakes using well-calibrated short- and long- period stations. At the same time rapid advances in computing technology enabled researchers to begin sophisticated analysis of the increasing amount of seismic data, which led to better understanding of earthquake source properties and their use in establishing plate tectonics. Today, global seismic networks are operated by German (Geophon), France (Geoscope), the United States (Global Seismograph Network) and the International Monitoring System. Presently, the Federation of Digital Seismograph Networks registers more than 1,000 broadband stations world-wide, a small percentage of the total number of digital seismic stations around the world. Following the devastating Kobe, Japan and Northridge, California earthquakes, Japan and the US have led the world in the integration of existing seismic sensor systems (weak and strong motion) into development of near-real-time, post-earthquake response products like ShakeMap, detailing the spatial distribution of strong shaking. Future challenges include expanding real-time integration of both seismic and geodetic sensor systems to produce early warning of strong shaking, rapid source determination, as well as near-realtime post- earthquake damage assessment. Seismic network data, hydro-acoustic arrays, deep water tide gauges, and satellite imagery of wave propagation should be integrated in real-time to provide input for hydrodynamic modeling yielding the distribution, timing and size of tsunamis runup--which would then be available instantly on the web, e.g. in a Google Earth format. Dense arrays of strong motion sensors together with deployment of MEMS-type accelerometers in buildings and equipment routinely connected to the Web could potentially provide thousands of measurements of damaging strong ground motion. This technology could ultimately become part of smart building design enabling critical facilities to change their structural response to imminent strong shaking. Looking further forward, it is likely that a continuously observing spaceborne system could image the occurrence of "silent" or "slow" earthquakes as well as the propagation of ground displacement by surface waves at scales of continents.

Maximally Permissive Composition of Actors in Ptolemy II

DTIC Science & Technology

2013-03-20

into our physical world by means of sensors and actuators . This global network of Cyber-Physical Systems (i.e., integrations of computation with...physical processes [Lee, 2008]), is often referred to as the “Internet of Things” ( IoT ). This term was coined by Kevin Ashton [Ashton, 2009] in 1999 to...processing capabilities. A newly emerging outer- most peripheral layer of the Cloud that is key to the full realization of the IoT , is identified as “The

Implementation of GAMMON - An efficient load balancing strategy for a local computer system

NASA Technical Reports Server (NTRS)

Baumgartner, Katherine M.; Kling, Ralph M.; Wah, Benjamin W.

1989-01-01

GAMMON (Global Allocation from Maximum to Minimum in cONstant time), an efficient load-balancing algorithm, is described. GAMMON uses the available broadcast capability of multiaccess networks to implement an efficient search technique for finding hosts with maximal and minimal loads. The search technique has an average overhead which is independent of the number of participating stations. The transition from the theoretical concept to a practical, reliable, and efficient implementation is described.

Georgia’s Cyber Left Hook

DTIC Science & Technology

2009-01-01

Relations for the Joint Task Force- Global Network Operations (JTF-GNO/ J5 ). He assists in development of cyber policy and strategy for operations and...History (Manchester, U.K: Manchester Univ. Press, 2000), 1. 10. See The Steamship Appam, 243 U.S. 124 (1917). 11. Jeffrey T. G. Kelsey, “ Hacking into...Arrest for Computer Hacking ,” news release, 1 October 2007, http://www.cybercrime.gov/kingIndict.pdf. 39. Grant Gross, “FBI: Several Nations Eyeing U.S

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

2015-09-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

Modular representation of layered neural networks.

PubMed

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

PubMed

Durstewitz, Daniel

2017-06-01

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

Job Scheduling in a Heterogeneous Grid Environment

NASA Technical Reports Server (NTRS)

Shan, Hong-Zhang; Smith, Warren; Oliker, Leonid; Biswas, Rupak

2004-01-01

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.

Converting differential-equation models of biological systems to membrane computing.

PubMed

Muniyandi, Ravie Chandren; Zin, Abdullah Mohd; Sanders, J W

2013-12-01

This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand-receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Non-harmful insertion of data mimicking computer network attacks

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

Neil, Joshua Charles; Kent, Alexander; Hash, Jr, Curtis Lee

Non-harmful data mimicking computer network attacks may be inserted in a computer network. Anomalous real network connections may be generated between a plurality of computing systems in the network. Data mimicking an attack may also be generated. The generated data may be transmitted between the plurality of computing systems using the real network connections and measured to determine whether an attack is detected.

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

PubMed

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2016-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.

Impact of the Topology of Global Macroeconomic Network on the Spreading of Economic Crises

PubMed Central

Lee, Kyu-Min; Yang, Jae-Suk; Kim, Gunn; Lee, Jaesung; Goh, Kwang-Il; Kim, In-mook

2011-01-01

Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of a network of interacting macroeconomic agents. In this regard, from the perspective of collective network dynamics, here we explore how the topology of the global macroeconomic network affects the patterns of spreading of economic crises. Using a simple toy model of crisis spreading, we demonstrate that an individual country's role in crisis spreading is not only dependent on its gross macroeconomic capacities, but also on its local and global connectivity profile in the context of the world economic network. We find that on one hand clustering of weak links at the regional scale can significantly aggravate the spread of crises, but on the other hand the current network structure at the global scale harbors higher tolerance of extreme crises compared to more “globalized” random networks. These results suggest that there can be a potential hidden cost in the ongoing globalization movement towards establishing less-constrained, trans-regional economic links between countries, by increasing vulnerability of the global economic system to extreme crises. PMID:21483794

WetNet: Using SSM/I data interactively for global distribution of tropical rainfall and precipitable water

NASA Technical Reports Server (NTRS)

Zipser, Edward J.; Mcguirk, James P.

1993-01-01

The research objectives were the following: (1) to use SSM/I to categorize, measure, and parameterize effects of rainfall systems around the globe, especially mesoscale convective systems; (2) to use SSM/I to monitor key components of the global hydrologic cycle, including tropical rainfall and precipitable water, and links to increasing sea surface temperatures; and (3) to assist in the development of efficient methods of exchange of massive satellite data bases and of analysis techniques, especially their use at a university. Numerous tasks have been initiated. First and foremost has been the integration and startup of the WetNet computer system into the TAMU computer network. Scientific activity was infeasible before completion of this activity. Final hardware delivery was not completed until October 1991, after which followed a period of identification and solution of several hardware and software and software problems. Accomplishments representing approximately four months work with the WetNEt system are presented.

DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

NASA Astrophysics Data System (ADS)

Mokhtari, Aryan; Shi, Wei; Ling, Qing; Ribeiro, Alejandro

2016-10-01

This paper considers decentralized consensus optimization problems where nodes of a network have access to different summands of a global objective function. Nodes cooperate to minimize the global objective by exchanging information with neighbors only. A decentralized version of the alternating directions method of multipliers (DADMM) is a common method for solving this category of problems. DADMM exhibits linear convergence rate to the optimal objective but its implementation requires solving a convex optimization problem at each iteration. This can be computationally costly and may result in large overall convergence times. The decentralized quadratically approximated ADMM algorithm (DQM), which minimizes a quadratic approximation of the objective function that DADMM minimizes at each iteration, is proposed here. The consequent reduction in computational time is shown to have minimal effect on convergence properties. Convergence still proceeds at a linear rate with a guaranteed constant that is asymptotically equivalent to the DADMM linear convergence rate constant. Numerical results demonstrate advantages of DQM relative to DADMM and other alternatives in a logistic regression problem.

Sibsonian and non-Sibsonian natural neighbour interpolation of the total electron content value

NASA Astrophysics Data System (ADS)

Kotulak, Kacper; Froń, Adam; Krankowski, Andrzej; Pulido, German Olivares; Henrandez-Pajares, Manuel

2017-03-01

In radioastronomy the interferometric measurement between radiotelescopes located relatively close to each other helps removing ionospheric effects. Unfortunately, in case of networks such as LOw Frequency ARray (LOFAR), due to long baselines (currently up to 1500 km), interferometric methods fail to provide sufficiently accurate ionosphere delay corrections. Practically it means that systems such as LOFAR need external ionosphere information, coming from Global or Regional Ionospheric Maps (GIMs or RIMs, respectively). Thanks to the technology based on Global Navigation Satellite Systems (GNSS), the scientific community is provided with ionosphere sounding virtually worldwide. In this paper we compare several interpolation methods for RIMs computation based on scattered Vertical Total Electron Content measurements located on one thin ionospheric layer (Ionospheric Pierce Points—IPPs). The results of this work show that methods that take into account the topology of the data distribution (e.g., natural neighbour interpolation) perform better than those based on geometric computation only (e.g., distance-weighted methods).

Decreased Metabolism in the Posterior Medial Network with Concomitantly Increased Metabolism in the Anterior Temporal Network During Transient Global Amnesia.

PubMed

Yi, SangHak; Park, Young Ho; Jang, Jae-Won; Lim, Jae-Sung; Chun, In Kook; Kim, SangYun

2018-05-01

Perturbation of corticohippocampal circuits is a key step in the pathogenesis of transient global amnesia. We evaluated the spatial distribution of altered cerebral metabolism to determine the location of the corticohippocampal circuits perturbed during the acute stage of transient global amnesia. A consecutive series of 12 patients with transient global amnesia who underwent 18 F-fluorodeoxyglucose positron emission tomography within 3 days after symptom onset was identified. We used statistical parametric mapping with two contrasts to identify regions of decreased and increased brain metabolism in transient global amnesia patients compared with 25 age-matched controls. Transient global amnesia patients showed hypometabolic clusters in the left temporal and bilateral parieto-occipital regions that belong to the posterior medial network as well as, hypermetabolic clusters in the bilateral inferior frontal regions that belong to the anterior temporal network. The posterior medial and anterior temporal networks are the two main corticohippocampal circuits involved in memory-guided behavior. Decreased metabolism in the posterior medial network might explain the impairment of episodic memory observed during the acute stage of transient global amnesia. Concomitant increased metabolism within the anterior temporal network might occur as a compensatory mechanism.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

PubMed Central

Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing

2016-01-01

Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371

ESCAP/POPIN Expert Working Group on Development of Population Information Centres and Networks, 20-23 June 1984, Bangkok, Thailand.

PubMed

1984-07-01

An overview of current population information programs at the regional, national, and global level was presented at a meeting of the Expert Working Group on Development of Population Information Centres and Networks. On the global level, the decentralized Population Information Network (POPIN) was established, consisting of population libraries, clearinghouses, information systems, and documentation centers. The Economic and Social Commission for Asia and the Pacific (ESCAP) Regional Population Information Centre (PIC) has actively promoted the standardization of methodologies for the collection and processing of data, the use of compatible terminology, adoption of classification systems, computer-assisted data and information handling, and improved programs of publication and infomration dissemination, within and among national centers. Among the national PICs, 83% are attached to the primary national family planning/fertility control unit and 17% are attached to demographic data, research, and analysis units. Lack of access to specialized information handling equipment such as microcomputers, word processors, and computer terminals remains a problem for PICs. Recommendations were made by the Expert Working Group to improve the functions of PICs: 1) the mandate and resoponsibilities of the PIC should be explicilty stated; 2) PICs should collect, process, and disseminate population information in the most effective format to workers in the population feild; 3) PICs should be given flexibility in the performance of activitites by their governing bodies; 4) short-term training should be provided in computerization and dissemination of information; 5) research and evaluation mechanisms for PIC activities should be developed; 6) PIC staff should prepare policy briefs for decision makers; 7) access to parent organizations should be given to nongovernment PICs; 8) study tours to foreign PICs should be organized for PIC staff; and 9) on-the-job training in indexing and abstracting should be provided. Networking among PICs can be further facilitated by written acquisition policies, automation of bibliographic information, common classification systems, and exchange of ideas and experience between various systems.

Designing allostery-inspired response in mechanical networks

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

Rocks, Jason W.; Pashine, Nidhi; Bischofberger, Irmgard

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are then able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ~1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individualmore » response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.« less

Designing allostery-inspired response in mechanical networks

DOE PAGES

Rocks, Jason W.; Pashine, Nidhi; Bischofberger, Irmgard; ...

2017-02-21

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are then able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ~1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individualmore » response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.« less

Cellular network entropy as the energy potential in Waddington's differentiation landscape

PubMed Central

Banerji, Christopher R. S.; Miranda-Saavedra, Diego; Severini, Simone; Widschwendter, Martin; Enver, Tariq; Zhou, Joseph X.; Teschendorff, Andrew E.

2013-01-01

Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape. PMID:24154593

Designing allostery-inspired response in mechanical networks

PubMed Central

Rocks, Jason W.; Pashine, Nidhi; Bischofberger, Irmgard; Goodrich, Carl P.; Liu, Andrea J.; Nagel, Sidney R.

2017-01-01

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ∼1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individual response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks. PMID:28223534

Designing allostery-inspired response in mechanical networks.

PubMed

Rocks, Jason W; Pashine, Nidhi; Bischofberger, Irmgard; Goodrich, Carl P; Liu, Andrea J; Nagel, Sidney R

2017-03-07

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ∼1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individual response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.

A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

PubMed

Shen, Lili; Guo, Jiming; Wang, Lei

2018-06-06

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

Performance analysis of PPP ambiguity resolution with UPD products estimated from different scales of reference station networks

NASA Astrophysics Data System (ADS)

Wang, Siyao; Li, Bofeng; Li, Xingxing; Zang, Nan

2018-01-01

Integer ambiguity fixing with uncalibrated phase delay (UPD) products can significantly shorten the initialization time and improve the accuracy of precise point positioning (PPP). Since the tracking arcs of satellites and the behavior of atmospheric biases can be very different for the reference networks with different scales, the qualities of corresponding UPD products may be also various. The purpose of this paper is to comparatively investigate the influence of different scales of reference station networks on UPD estimation and user ambiguity resolution. Three reference station networks with global, wide-area and local scales are used to compute the UPD products and analyze their impact on the PPP-AR. The time-to-first-fix, the unfix rate and the incorrect fix rate of PPP-AR are analyzed. Moreover, in order to further shorten the convergence time for obtaining precise positioning, a modified partial ambiguity resolution (PAR) and corresponding validation strategy are presented. In this PAR method, the ambiguity subset is determined by removing the ambiguity one by one in the order of ascending elevations. Besides, for static positioning mode, a coordinate validation strategy is employed to enhance the reliability of the fixed coordinate. The experiment results show that UPD products computed by smaller station network are more accurate and lead to a better coordinate solution; the PAR method used in this paper can shorten the convergence time and the coordinate validation strategy can improve the availability of high precision positioning.

The Social Network of Tracer Variations and O(100) Uncertain Photochemical Parameters in the Community Atmosphere Model

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Labute, M.; Chowdhary, K.; Debusschere, B.; Cameron-Smith, P. J.

2014-12-01

Simulating the atmospheric cycles of ozone, methane, and other radiatively important trace gases in global climate models is computationally demanding and requires the use of 100's of photochemical parameters with uncertain values. Quantitative analysis of the effects of these uncertainties on tracer distributions, radiative forcing, and other model responses is hindered by the "curse of dimensionality." We describe efforts to overcome this curse using ensemble simulations and advanced statistical methods. Uncertainties from 95 photochemical parameters in the trop-MOZART scheme were sampled using a Monte Carlo method and propagated through 10,000 simulations of the single column version of the Community Atmosphere Model (CAM). The variance of the ensemble was represented as a network with nodes and edges, and the topology and connections in the network were analyzed using lasso regression, Bayesian compressive sensing, and centrality measures from the field of social network theory. Despite the limited sample size for this high dimensional problem, our methods determined the key sources of variation and co-variation in the ensemble and identified important clusters in the network topology. Our results can be used to better understand the flow of photochemical uncertainty in simulations using CAM and other climate models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported by the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC).

Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit.

PubMed

Eriksson, O; Brinne, B; Zhou, Y; Björkegren, J; Tegnér, J

2009-03-01

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein-protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a 'tearing-and-zooming' approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between state-variables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits. [Includes supplementary material].

From WSN towards WoT: Open API Scheme Based on oneM2M Platforms.

PubMed

Kim, Jaeho; Choi, Sung-Chan; Ahn, Il-Yeup; Sung, Nak-Myoung; Yun, Jaeseok

2016-10-06

Conventional computing systems have been able to be integrated into daily objects and connected to each other due to advances in computing and network technologies, such as wireless sensor networks (WSNs), forming a global network infrastructure, called the Internet of Things (IoT). To support the interconnection and interoperability between heterogeneous IoT systems, the availability of standardized, open application programming interfaces (APIs) is one of the key features of common software platforms for IoT devices, gateways, and servers. In this paper, we present a standardized way of extending previously-existing WSNs towards IoT systems, building the world of the Web of Things (WoT). Based on the oneM2M software platforms developed in the previous project, we introduce a well-designed open API scheme and device-specific thing adaptation software (TAS) enabling WSN elements, such as a wireless sensor node, to be accessed in a standardized way on a global scale. Three pilot services are implemented (i.e., a WiFi-enabled smart flowerpot, voice-based control for ZigBee-connected home appliances, and WiFi-connected AR.Drone control) to demonstrate the practical usability of the open API scheme and TAS modules. Full details on the method of integrating WSN elements into three example systems are described at the programming code level, which is expected to help future researchers in integrating their WSN systems in IoT platforms, such as oneM2M. We hope that the flexibly-deployable, easily-reusable common open API scheme and TAS-based integration method working with the oneM2M platforms will help the conventional WSNs in diverse industries evolve into the emerging WoT solutions.

From WSN towards WoT: Open API Scheme Based on oneM2M Platforms

PubMed Central

Kim, Jaeho; Choi, Sung-Chan; Ahn, Il-Yeup; Sung, Nak-Myoung; Yun, Jaeseok

2016-01-01

Conventional computing systems have been able to be integrated into daily objects and connected to each other due to advances in computing and network technologies, such as wireless sensor networks (WSNs), forming a global network infrastructure, called the Internet of Things (IoT). To support the interconnection and interoperability between heterogeneous IoT systems, the availability of standardized, open application programming interfaces (APIs) is one of the key features of common software platforms for IoT devices, gateways, and servers. In this paper, we present a standardized way of extending previously-existing WSNs towards IoT systems, building the world of the Web of Things (WoT). Based on the oneM2M software platforms developed in the previous project, we introduce a well-designed open API scheme and device-specific thing adaptation software (TAS) enabling WSN elements, such as a wireless sensor node, to be accessed in a standardized way on a global scale. Three pilot services are implemented (i.e., a WiFi-enabled smart flowerpot, voice-based control for ZigBee-connected home appliances, and WiFi-connected AR.Drone control) to demonstrate the practical usability of the open API scheme and TAS modules. Full details on the method of integrating WSN elements into three example systems are described at the programming code level, which is expected to help future researchers in integrating their WSN systems in IoT platforms, such as oneM2M. We hope that the flexibly-deployable, easily-reusable common open API scheme and TAS-based integration method working with the oneM2M platforms will help the conventional WSNs in diverse industries evolve into the emerging WoT solutions. PMID:27782058

Energy-efficient STDP-based learning circuits with memristor synapses

NASA Astrophysics Data System (ADS)

Wu, Xinyu; Saxena, Vishal; Campbell, Kristy A.

2014-05-01

It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-andfire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

Algebraic and adaptive learning in neural control systems

NASA Astrophysics Data System (ADS)

Ferrari, Silvia

A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

Organization of the secure distributed computing based on multi-agent system

NASA Astrophysics Data System (ADS)

Khovanskov, Sergey; Rumyantsev, Konstantin; Khovanskova, Vera

2018-04-01

Nowadays developing methods for distributed computing is received much attention. One of the methods of distributed computing is using of multi-agent systems. The organization of distributed computing based on the conventional network computers can experience security threats performed by computational processes. Authors have developed the unified agent algorithm of control system of computing network nodes operation. Network PCs is used as computing nodes. The proposed multi-agent control system for the implementation of distributed computing allows in a short time to organize using of the processing power of computers any existing network to solve large-task by creating a distributed computing. Agents based on a computer network can: configure a distributed computing system; to distribute the computational load among computers operated agents; perform optimization distributed computing system according to the computing power of computers on the network. The number of computers connected to the network can be increased by connecting computers to the new computer system, which leads to an increase in overall processing power. Adding multi-agent system in the central agent increases the security of distributed computing. This organization of the distributed computing system reduces the problem solving time and increase fault tolerance (vitality) of computing processes in a changing computing environment (dynamic change of the number of computers on the network). Developed a multi-agent system detects cases of falsification of the results of a distributed system, which may lead to wrong decisions. In addition, the system checks and corrects wrong results.

Emerging CAE technologies and their role in Future Ambient Intelligence Environments

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2011-03-01

Dramatic improvements are on the horizon in Computer Aided Engineering (CAE) and various simulation technologies. The improvements are due, in part, to the developments in a number of leading-edge technologies and their synergistic combinations/convergence. The technologies include ubiquitous, cloud, and petascale computing; ultra high-bandwidth networks, pervasive wireless communication; knowledge based engineering; networked immersive virtual environments and virtual worlds; novel human-computer interfaces; and powerful game engines and facilities. This paper describes the frontiers and emerging simulation technologies, and their role in the future virtual product creation and learning/training environments. The environments will be ambient intelligence environments, incorporating a synergistic combination of novel agent-supported visual simulations (with cognitive learning and understanding abilities); immersive 3D virtual world facilities; development chain management systems and facilities (incorporating a synergistic combination of intelligent engineering and management tools); nontraditional methods; intelligent, multimodal and human-like interfaces; and mobile wireless devices. The Virtual product creation environment will significantly enhance the productivity and will stimulate creativity and innovation in future global virtual collaborative enterprises. The facilities in the learning/training environment will provide timely, engaging, personalized/collaborative and tailored visual learning.

The UNESCO Global Network of National Geoparks

NASA Astrophysics Data System (ADS)

Mc Keever1, P.; Zouros, N.; Patzak, M.; Missotten, R.

2009-12-01

The UNESCO Global Network of National Geoparks was founded in 2004, following the model successfully established by the European Geoparks Network in 2000. It now comprises 63 members in 19 nations across the world. A Global Geopark is an area with geological heritage of international value but where that heritage is being used for the sustainable economic benefit if the local inhabitants, primarily through education and tourism. Supported by IUGS and IUCN, the aim of the Global Geoparks Network is to facilitate exchange and sharing between members to assist in the protection and conservation of the geological heritage of our planet but to do so in way where local communities can take ownership of these special places and where they can get some sustainable economic benefit from them. While allowing for the sustainable economic development of geoparks, the network explicitly forbids the destruction or sale of the geological value of a geopark. This paper outlines the ethos of the Global Geoparks Network and describes the typical activities of geoparks and how the network functions. Using two examples it also illustrates how members of the Global Geoparks Network provide good examples as tools not only for holistic nature conservation but also for economic development.

Industrial application for global quantum communication

NASA Astrophysics Data System (ADS)

Mirza, A.; Petruccione, F.

2012-09-01

In the last decade the quantum communication community has witnessed great advances in photonic quantum cryptography technology with the research, development and commercialization of automated Quantum Key Distribution (QKD) devices. These first generation devices are however bottlenecked by the achievable spatial coverage. This is due to the intrinsic absorption of the quantum particle into the communication medium. As QKD is of paramount importance in the future ICT landscape, various innovative solutions have been developed and tested to expand the spatial coverage of these networks such as the Quantum City initiative in Durban, South Africa. To expand this further into a global QKD-secured network, recent efforts have focussed on high-altitude free-space techniques through the use of satellites. This couples the QKD-secured Metropolitan Area Networks (MANs) with secured ground-tosatellite links as access points to a global network. Such a solution, however, has critical limitations that reduce its commercial feasibility. As parallel step to the development of satellitebased global QKD networks, we investigate the use of the commercial aircrafts' network as secure transport mechanisms in a global QKD network. This QKD-secured global network will provide a robust infrastructure to create, distribute and manage encryption keys between the MANs of the participating cities.

Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

PubMed Central

Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

2014-01-01

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

The challenge of sustaining effectiveness over time: the case of the global network to stop tuberculosis

PubMed Central

Quissell, Kathryn; Walt, Gill

2016-01-01

Where once global health decisions were largely the domain of national governments and the World Health Organization, today networks of international organizations, governments, private philanthropies and other entities are actively shaping public policy. However, there is still limited understanding of how global networks form, how they create institutions, how they promote and sustain collective action, and how they adapt to changes in the policy environment. Understanding these processes is crucial to understanding their effectiveness: whether and how global networks influence policy and public health outcomes. This study seeks to address these gaps through the examination of the global network to stop tuberculosis (TB) and the factors influencing its effectiveness over time. Drawing from ∼200 document sources and 16 interviews with key informants, we trace the development of the Global Partnership to Stop TB and its work over the past decade. We find that having a centralized core group and a strategic brand helped the network to coalesce around a primary intervention strategy, directly observed treatment short course. This strategy was created before the network was formalized, and helped bring in donors, ministries of health and other organizations committed to fighting TB—growing the network. Adaptations to this strategy, the creation of a consensus-based Global Plan, and the creation of a variety of participatory venues for discussion, helped to expand and sustain the network. Presently, however, tensions have become more apparent within the network as it struggles with changing internal political dynamics and the evolution of the disease. While centralization and stability helped to launch and grow the network, the institutionalization of governance and strategy may have constrained adaptation. Institutionalization and centralization may, therefore, facilitate short-term success for networks, but may end up complicating longer-term effectiveness. PMID:26282859

The "side" matters: how configurality is reflected in completion.

PubMed

Kogo, Naoki; Wagemans, Johan

2013-01-01

The perception of figure-ground organization is a highly context-sensitive phenomenon. Accumulating evidence suggests that the so-called completion phenomenon is tightly linked to this figure-ground organization. While many computational models have applied borderline completion algorithms based on the detection of boundary alignments, we point out the problems of this approach. We hypothesize that completion is a result of computing the figure-ground organization. Specifically, the global interactions in the neural network activate the "border-ownership" sensitive neurons at the location where no luminance contrast is given and this activation corresponds to the perception of illusory contours. The implications of this result to the general property of emerging Gestalt percepts are discussed.

An efficient solution of real-time data processing for multi-GNSS network

NASA Astrophysics Data System (ADS)

Gong, Xiaopeng; Gu, Shengfeng; Lou, Yidong; Zheng, Fu; Ge, Maorong; Liu, Jingnan

2017-12-01

Global navigation satellite systems (GNSS) are acting as an indispensable tool for geodetic research and global monitoring of the Earth, and they have been rapidly developed over the past few years with abundant GNSS networks, modern constellations, and significant improvement in mathematic models of data processing. However, due to the increasing number of satellites and stations, the computational efficiency becomes a key issue and it could hamper the further development of GNSS applications. In this contribution, this problem is overcome from the aspects of both dense linear algebra algorithms and GNSS processing strategy. First, in order to fully explore the power of modern microprocessors, the square root information filter solution based on the blocked QR factorization employing as many matrix-matrix operations as possible is introduced. In addition, the algorithm complexity of GNSS data processing is further decreased by centralizing the carrier-phase observations and ambiguity parameters, as well as performing the real-time ambiguity resolution and elimination. Based on the QR factorization of the simulated matrix, we can conclude that compared to unblocked QR factorization, the blocked QR factorization can greatly improve processing efficiency with a magnitude of nearly two orders on a personal computer with four 3.30 GHz cores. Then, with 82 globally distributed stations, the processing efficiency is further validated in multi-GNSS (GPS/BDS/Galileo) satellite clock estimation. The results suggest that it will take about 31.38 s per epoch for the unblocked method. While, without any loss of accuracy, it only takes 0.50 and 0.31 s for our new algorithm per epoch for float and fixed clock solutions, respectively.

Macroscopic and microscopic spectral properties of brain networks during local and global synchronization

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir A.; Lüttjohann, Annika; Makarov, Vladimir V.; Goremyko, Mikhail V.; Koronovskii, Alexey A.; Nedaivozov, Vladimir; Runnova, Anastasia E.; van Luijtelaar, Gilles; Hramov, Alexander E.; Boccaletti, Stefano

2017-07-01

We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators, which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal. Our methodology is then applied to getting a glance into the microscopic interactions occurring in a neurophysiological system, namely, in the thalamocortical neural network of an epileptic brain of a rat, where the group electrical activity is registered by means of multichannel EEG. We demonstrate that it is possible to infer the degree of interaction between the interconnected regions of the brain during different types of brain activities and to estimate the regions' participation in the generation of the different levels of consciousness.

Neural model of gene regulatory network: a survey on supportive meta-heuristics.

PubMed

Biswas, Surama; Acharyya, Sriyankar

2016-06-01

Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation.

PubMed

Du, Tingsong; Hu, Yang; Ke, Xianting

2015-01-01

An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

PubMed

Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh

2017-09-01

Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

PubMed Central

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior. PMID:22496771

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation

PubMed Central

Hu, Yang; Ke, Xianting

2015-01-01

An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA. PMID:26447713

The QAP weighted network analysis method and its application in international services trade

NASA Astrophysics Data System (ADS)

Xu, Helian; Cheng, Long

2016-04-01

Based on QAP (Quadratic Assignment Procedure) correlation and complex network theory, this paper puts forward a new method named QAP Weighted Network Analysis Method. The core idea of the method is to analyze influences among relations in a social or economic group by building a QAP weighted network of networks of relations. In the QAP weighted network, a node depicts a relation and an undirect edge exists between any pair of nodes if there is significant correlation between relations. As an application of the QAP weighted network, we study international services trade by using the QAP weighted network, in which nodes depict 10 kinds of services trade relations. After the analysis of international services trade by QAP weighted network, and by using distance indicators, hierarchy tree and minimum spanning tree, the conclusion shows that: Firstly, significant correlation exists in all services trade, and the development of any one service trade will stimulate the other nine. Secondly, as the economic globalization goes deeper, correlations in all services trade have been strengthened continually, and clustering effects exist in those services trade. Thirdly, transportation services trade, computer and information services trade and communication services trade have the most influence and are at the core in all services trade.

Neocortical dynamics due to axon propagation delays in cortico-cortical fibers: EEG traveling and standing waves with implications for top-down influences on local networks and white matter disease

PubMed Central

Nunez, Paul L.; Srinivasan, Ramesh

2013-01-01

The brain is treated as a nested hierarchical complex system with substantial interactions across spatial scales. Local networks are pictured as embedded within global fields of synaptic action and action potentials. Global fields may act top-down on multiple networks, acting to bind remote networks. Because of scale-dependent properties, experimental electrophysiology requires both local and global models that match observational scales. Multiple local alpha rhythms are embedded in a global alpha rhythm. Global models are outlined in which cm-scale dynamic behaviors result largely from propagation delays in cortico-cortical axons and cortical background excitation level, controlled by neuromodulators on long time scales. The idealized global models ignore the bottom-up influences of local networks on global fields so as to employ relatively simple mathematics. The resulting models are transparently related to several EEG and steady state visually evoked potentials correlated with cognitive states, including estimates of neocortical coherence structure, traveling waves, and standing waves. The global models suggest that global oscillatory behavior of self-sustained (limit-cycle) modes lower than about 20 Hz may easily occur in neocortical/white matter systems provided: Background cortical excitability is sufficiently high; the strength of long cortico-cortical axon systems is sufficiently high; and the bottom-up influence of local networks on the global dynamic field is sufficiently weak. The global models provide "entry points" to more detailed studies of global top-down influences, including binding of weakly connected networks, modulation of gamma oscillations by theta or alpha rhythms, and the effects of white matter deficits. PMID:24505628

Structure and evolution of the global seafood trade network

NASA Astrophysics Data System (ADS)

Gephart, Jessica A.; Pace, Michael L.

2015-12-01

The food production system is increasingly global and seafood is among the most highly traded commodities. Global trade can improve food security by providing access to a greater variety of foods, increasing wealth, buffering against local supply shocks, and benefit the environment by increasing overall use efficiency for some resources. However, global trade can also expose countries to external supply shocks and degrade the environment by increasing resource demand and loosening feedbacks between consumers and the impacts of food production. As a result, changes in global food trade can have important implications for both food security and the environmental impacts of production. Measurements of globalization and the environmental impacts of food production require data on both total trade and the origin and destination of traded goods (the network structure). While the global trade network of agricultural and livestock products has previously been studied, seafood products have been excluded. This study describes the structure and evolution of the global seafood trade network, including metrics quantifying the globalization of seafood, shifts in bilateral trade flows, changes in centrality and comparisons of seafood to agricultural and industrial trade networks. From 1994 to 2012 the number of countries trading in the network remained relatively constant, while the number of trade partnerships increased by over 65%. Over this same period, the total quantity of seafood traded increased by 58% and the value increased 85% in real terms. These changes signify the increasing globalization of seafood products. Additionally, the trade patterns in the network indicate: increased influence of Thailand and China, strengthened intraregional trade, and increased exports from South America and Asia. In addition to characterizing these network changes, this study identifies data needs in order to connect seafood trade with environmental impacts and food security outcomes.

From network structure to network reorganization: implications for adult neurogenesis

NASA Astrophysics Data System (ADS)

Schneider-Mizell, Casey M.; Parent, Jack M.; Ben-Jacob, Eshel; Zochowski, Michal R.; Sander, Leonard M.

2010-12-01

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

Executing a gather operation on a parallel computer

DOEpatents

Archer, Charles J [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2012-03-20

Methods, apparatus, and computer program products are disclosed for executing a gather operation on a parallel computer according to embodiments of the present invention. Embodiments include configuring, by the logical root, a result buffer or the logical root, the result buffer having positions, each position corresponding to a ranked node in the operational group and for storing contribution data gathered from that ranked node. Embodiments also include repeatedly for each position in the result buffer: determining, by each compute node of an operational group, whether the current position in the result buffer corresponds with the rank of the compute node, if the current position in the result buffer corresponds with the rank of the compute node, contributing, by that compute node, the compute node's contribution data, if the current position in the result buffer does not correspond with the rank of the compute node, contributing, by that compute node, a value of zero for the contribution data, and storing, by the logical root in the current position in the result buffer, results of a bitwise OR operation of all the contribution data by all compute nodes of the operational group for the current position, the results received through the global combining network.

A General, Adaptive, Roadmap-Based Algorithm for Protein Motion Computation.

PubMed

Molloy, Kevin; Shehu, Amarda

2016-03-01

Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

The 1987 RIACS annual report

NASA Technical Reports Server (NTRS)

1987-01-01

The Research Institute for Advanced Computer Science (RIACS) was established at the NASA Ames Research Center in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 64 universities with graduate programs in the aerospace sciences, under several Cooperative Agreements with NASA. RIACS's goal is to provide preeminent leadership in basic and applied computer science research as partners in support of NASA's goals and missions. In pursuit of this goal, RIACS contributes to several of the grand challenges in science and engineering facing NASA: flying an airplane inside a computer; determining the chemical properties of materials under hostile conditions in the atmospheres of earth and the planets; sending intelligent machines on unmanned space missions; creating a one-world network that makes all scientific resources, including those in space, accessible to all the world's scientists; providing intelligent computational support to all stages of the process of scientific investigation from problem formulation to results dissemination; and developing accurate global models for climatic behavior throughout the world. In working with these challenges, we seek novel architectures, and novel ways to use them, that exploit the potential of parallel and distributed computation and make possible new functions that are beyond the current reach of computing machines. The investigation includes pattern computers as well as the more familiar numeric and symbolic computers, and it includes networked systems of resources distributed around the world. We believe that successful computer science research is interdisciplinary: it is driven by (and drives) important problems in other disciplines. We believe that research should be guided by a clear long-term vision with planned milestones. And we believe that our environment must foster and exploit innovation. Our activities and accomplishments for the calendar year 1987 and our plans for 1988 are reported.

A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks

PubMed Central

Khammash, Mustafa

2014-01-01

Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed. PMID:24968191

The big data-big model (BDBM) challenges in ecological research

NASA Astrophysics Data System (ADS)

Luo, Y.

2015-12-01

The field of ecology has become a big-data science in the past decades due to development of new sensors used in numerous studies in the ecological community. Many sensor networks have been established to collect data. For example, satellites, such as Terra and OCO-2 among others, have collected data relevant on global carbon cycle. Thousands of field manipulative experiments have been conducted to examine feedback of terrestrial carbon cycle to global changes. Networks of observations, such as FLUXNET, have measured land processes. In particular, the implementation of the National Ecological Observatory Network (NEON), which is designed to network different kinds of sensors at many locations over the nation, will generate large volumes of ecological data every day. The raw data from sensors from those networks offer an unprecedented opportunity for accelerating advances in our knowledge of ecological processes, educating teachers and students, supporting decision-making, testing ecological theory, and forecasting changes in ecosystem services. Currently, ecologists do not have the infrastructure in place to synthesize massive yet heterogeneous data into resources for decision support. It is urgent to develop an ecological forecasting system that can make the best use of multiple sources of data to assess long-term biosphere change and anticipate future states of ecosystem services at regional and continental scales. Forecasting relies on big models that describe major processes that underlie complex system dynamics. Ecological system models, despite great simplification of the real systems, are still complex in order to address real-world problems. For example, Community Land Model (CLM) incorporates thousands of processes related to energy balance, hydrology, and biogeochemistry. Integration of massive data from multiple big data sources with complex models has to tackle Big Data-Big Model (BDBM) challenges. Those challenges include interoperability of multiple, heterogeneous data sets; intractability of structural complexity of big models; equifinality of model structure selection and parameter estimation; and computational demand of global optimization with Big Models.

Line-plane broadcasting in a data communications network of a parallel computer

DOEpatents

Archer, Charles J.; Berg, Jeremy E.; Blocksome, Michael A.; Smith, Brian E.

2010-06-08

Methods, apparatus, and products are disclosed for line-plane broadcasting in a data communications network of a parallel computer, the parallel computer comprising a plurality of compute nodes connected together through the network, the network optimized for point to point data communications and characterized by at least a first dimension, a second dimension, and a third dimension, that include: initiating, by a broadcasting compute node, a broadcast operation, including sending a message to all of the compute nodes along an axis of the first dimension for the network; sending, by each compute node along the axis of the first dimension, the message to all of the compute nodes along an axis of the second dimension for the network; and sending, by each compute node along the axis of the second dimension, the message to all of the compute nodes along an axis of the third dimension for the network.

Line-plane broadcasting in a data communications network of a parallel computer

DOEpatents

Archer, Charles J.; Berg, Jeremy E.; Blocksome, Michael A.; Smith, Brian E.

2010-11-23

Methods, apparatus, and products are disclosed for line-plane broadcasting in a data communications network of a parallel computer, the parallel computer comprising a plurality of compute nodes connected together through the network, the network optimized for point to point data communications and characterized by at least a first dimension, a second dimension, and a third dimension, that include: initiating, by a broadcasting compute node, a broadcast operation, including sending a message to all of the compute nodes along an axis of the first dimension for the network; sending, by each compute node along the axis of the first dimension, the message to all of the compute nodes along an axis of the second dimension for the network; and sending, by each compute node along the axis of the second dimension, the message to all of the compute nodes along an axis of the third dimension for the network.

Development and Evaluation of an Integrated Hydrological Modeling Framework for Monitoring and Understanding Floods and Droughts

NASA Astrophysics Data System (ADS)

Yang, Z. L.; Wu, W. Y.; Lin, P.; Maidment, D. R.

2017-12-01

Extreme water events such as catastrophic floods and severe droughts have increased in recent decades. Mitigating the risk to lives, food security, infrastructure, energy supplies, as well as numerous other industries posed by these extreme events requires informed decision-making and planning based on sound science. We are developing a global water modeling capability by building models that will provide total operational water predictions (evapotranspiration, soil moisture, groundwater, channel flow, inundation, snow) at unprecedented spatial resolutions and updated frequencies. Toward this goal, this talk presents an integrated global hydrological modeling framework that takes advantage of gridded meteorological forcing, land surface modeling, channeled flow modeling, ground observations, and satellite remote sensing. Launched in August 2016, the National Water Model successfully incorporates weather forecasts to predict river flows for more than 2.7 million rivers across the continental United States, which transfers a "synoptic weather map" to a "synoptic river flow map" operationally. In this study, we apply a similar framework to a high-resolution global river network database, which is developed from a hierarchical Dominant River Tracing (DRT) algorithm, and runoff output from the Global Land Data Assimilation System (GLDAS) to a vector-based river routing model (The Routing Application for Parallel Computation of Discharge, RAPID) to produce river flows from 2001 to 2016 using Message Passing Interface (MPI) on Texas Advanced Computer Center's Stampede system. In this simulation, global river discharges for more than 177,000 rivers are computed every 30 minutes. The modeling framework's performance is evaluated with various observations including river flows at more than 400 gauge stations globally. Overall, the model exhibits a reasonably good performance in simulating the averaged patterns of terrestrial water storage, evapotranspiration and runoff. The system is appropriate for monitoring and studying floods and droughts. Directions for future research will be outlined and discussed.

Instantaneous global spatial interaction? Exploring the Gaussian inequality, distance and Internet pings in a global network

NASA Astrophysics Data System (ADS)

Baker, R. G. V.

2005-12-01

The Internet has been publicly portrayed as a new technological horizon yielding instantaneous interaction to a point where geography no longer matters. This research aims to dispel this impression by applying a dynamic form of trip modelling to investigate pings in a global computer network compiled by the Stanford Linear Accelerator Centre (SLAC) from 1998 to 2004. Internet flows have been predicted to have the same mathematical operators as trips to a supermarket, since they are both periodic and constrained by a distance metric. Both actual and virtual trips are part of a spectrum of origin-destination pairs in the time-space convergence of trip time-lines. Internet interaction is very near to the convergence of these time-lines (at a very small time scale in milliseconds, but with interactions over thousands of kilometres). There is a lag effect and this is formalised by the derivation of Gaussian and gravity inequalities between the time taken (Δ t) and the partitioning of distance (Δ x). This inequality seems to be robust for a regression of Δ t to Δ x in the SLAC data set for each year (1998 to 2004). There is a constant ‘forbidden zone’ in the interaction, underpinned by the fact that pings do not travel faster than the speed of light. Superimposed upon this zone is the network capacity where a linear regression of Δ t to Δ x is a proxy summarising global Internet connectivity for that year. The results suggest that there has been a substantial improvement in connectivity over the period with R 2 increasing steadily from 0.39 to 0.65 from less Gaussian spreading of the ping latencies. Further, the regression line shifts towards the inequality boundary from 1998 to 2004, where the increased slope shows a greater proportional rise in local connectivity over global connectivity. A conclusion is that national geography still does matter in spatial interaction modelling of the Internet.

Communication and computing technology in biocontainment laboratories using the NEIDL as a model.

PubMed

McCall, John; Hardcastle, Kath

2014-07-01

The National Emerging Infectious Diseases Laboratories (NEIDL), Boston University, is a globally unique biocontainment research facility housing biosafety level 2 (BSL-2), BSL-3, and BSL-4 laboratories. Located in the BioSquare area at the University's Medical Campus, it is part of a national network of secure facilities constructed to study infectious diseases of major public health concern. The NEIDL allows for basic, translational, and clinical phases of research to be carried out in a single facility with the overall goal of accelerating understanding, treatment, and prevention of infectious diseases. The NEIDL will also act as a center of excellence providing training and education in all aspects of biocontainment research. Within every detail of NEIDL operations is a primary emphasis on safety and security. The ultramodern NEIDL has required a new approach to communications technology solutions in order to ensure safety and security and meet the needs of investigators working in this complex building. This article discusses the implementation of secure wireless networks and private cloud computing to promote operational efficiency, biosecurity, and biosafety with additional energy-saving advantages. The utilization of a dedicated data center, virtualized servers, virtualized desktop integration, multichannel secure wireless networks, and a NEIDL-dedicated Voice over Internet Protocol (VoIP) network are all discussed. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

GLOBECOM '84 - Global Telecommunications Conference, Atlanta, GA, November 26-29, 1984, Conference Record. Volume 1

NASA Astrophysics Data System (ADS)

The subjects discussed are related to LSI/VLSI based subscriber transmission and customer access for the Integrated Services Digital Network (ISDN), special applications of fiber optics, ISDN and competitive telecommunication services, technical preparations for the Geostationary-Satellite Orbit Conference, high-capacity statistical switching fabrics, networking and distributed systems software, adaptive arrays and cancelers, synchronization and tracking, speech processing, advances in communication terminals, full-color videotex, and a performance analysis of protocols. Advances in data communications are considered along with transmission network plans and progress, direct broadcast satellite systems, packet radio system aspects, radio-new and developing technologies and applications, the management of software quality, and Open Systems Interconnection (OSI) aspects of telematic services. Attention is given to personal computers and OSI, the role of software reliability measurement in information systems, and an active array antenna for the next-generation direct broadcast satellite.

Inference of financial networks using the normalised mutual information rate.

PubMed

Goh, Yong Kheng; Hasim, Haslifah M; Antonopoulos, Chris G

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.

Inference of financial networks using the normalised mutual information rate

PubMed Central

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics. PMID:29420644

Optimisation of the usage of LHC and local computing resources in a multidisciplinary physics department hosting a WLCG Tier-2 centre

NASA Astrophysics Data System (ADS)

Barberis, Stefano; Carminati, Leonardo; Leveraro, Franco; Mazza, Simone Michele; Perini, Laura; Perlz, Francesco; Rebatto, David; Tura, Ruggero; Vaccarossa, Luca; Villaplana, Miguel

2015-12-01

We present the approach of the University of Milan Physics Department and the local unit of INFN to allow and encourage the sharing among different research areas of computing, storage and networking resources (the largest ones being those composing the Milan WLCG Tier-2 centre and tailored to the needs of the ATLAS experiment). Computing resources are organised as independent HTCondor pools, with a global master in charge of monitoring them and optimising their usage. The configuration has to provide satisfactory throughput for both serial and parallel (multicore, MPI) jobs. A combination of local, remote and cloud storage options are available. The experience of users from different research areas operating on this shared infrastructure is discussed. The promising direction of improving scientific computing throughput by federating access to distributed computing and storage also seems to fit very well with the objectives listed in the European Horizon 2020 framework for research and development.