Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

Multi-equilibrium property of metabolic networks: SSI module.

PubMed

Lei, Hong-Bo; Zhang, Ji-Feng; Chen, Luonan

2011-06-20

Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks

PubMed Central

Sha, Chao; Qiu, Jian-mei; Li, Shu-yan; Qiang, Meng-ye; Wang, Ru-chuan

2016-01-01

To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods. PMID:27338401

Interdependent Multi-Layer Networks: Modeling and Survivability Analysis with Applications to Space-Based Networks

PubMed Central

Castet, Jean-Francois; Saleh, Joseph H.

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the

Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

PubMed

Castet, Jean-Francois; Saleh, Joseph H

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the

Online Teaching in Networked Learning Communities: A Multi-Method Approach to Studying the Role of the Teacher

ERIC Educational Resources Information Center

De Laat, Maarten; Lally, Vic; Lipponen, Lasse; Simons, Robert-Jan

2007-01-01

The aim of this paper is to study the online teaching styles of two teachers who each tutor a networked learning community (NLC), within the same workshop. The study is undertaking empirical work using a multi-method approach in order to triangulate and contextualise our findings and enrich our understanding of the teacher participation in these…

Multi-equilibrium property of metabolic networks: SSI module

PubMed Central

2011-01-01

Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI) metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module. PMID:21689474

Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

NASA Astrophysics Data System (ADS)

Margitus, Michael R.; Tagliaferri, William A., Jr.; Sudit, Moises; LaMonica, Peter M.

2012-06-01

Understanding the structure and dynamics of networks are of vital importance to winning the global war on terror. To fully comprehend the network environment, analysts must be able to investigate interconnected relationships of many diverse network types simultaneously as they evolve both spatially and temporally. To remove the burden from the analyst of making mental correlations of observations and conclusions from multiple domains, we introduce the Dynamic Graph Analytic Framework (DYGRAF). DYGRAF provides the infrastructure which facilitates a layered multi-modal network analysis (LMMNA) approach that enables analysts to assemble previously disconnected, yet related, networks in a common battle space picture. In doing so, DYGRAF provides the analyst with timely situation awareness, understanding and anticipation of threats, and support for effective decision-making in diverse environments.

Predicting multicellular function through multi-layer tissue networks

PubMed Central

Zitnik, Marinka; Leskovec, Jure

2017-01-01

Abstract Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. Availability and implementation: Source code and datasets are available at http://snap.stanford.edu/ohmnet. Contact: jure@cs.stanford.edu PMID:28881986

Integration of multi-omics data for integrative gene regulatory network inference.

PubMed

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Integration of multi-omics data for integrative gene regulatory network inference

PubMed Central

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189

Next generation of network medicine: interdisciplinary signaling approaches.

PubMed

Korcsmaros, Tamas; Schneider, Maria Victoria; Superti-Furga, Giulio

2017-02-20

In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

Adding the 'heart' to hanging drop networks for microphysiological multi-tissue experiments.

PubMed

Rismani Yazdi, Saeed; Shadmani, Amir; Bürgel, Sebastian C; Misun, Patrick M; Hierlemann, Andreas; Frey, Olivier

2015-11-07

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid-air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip.

Multiple network alignment via multiMAGNA+.

PubMed

Vijayan, Vipin; Milenkovic, Tijana

2017-08-21

Network alignment (NA) aims to find a node mapping that identifies topologically or functionally similar network regions between molecular networks of different species. Analogous to genomic sequence alignment, NA can be used to transfer biological knowledge from well- to poorly-studied species between aligned network regions. Pairwise NA (PNA) finds similar regions between two networks while multiple NA (MNA) can align more than two networks. We focus on MNA. Existing MNA methods aim to maximize total similarity over all aligned nodes (node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Directly optimizing edge conservation during alignment construction in addition to node conservation may result in superior alignments. Thus, we present a novel MNA method called multiMAGNA++ that can achieve this. Indeed, multiMAGNA++ outperforms or is on par with existing MNA methods, while often completing faster than existing methods. That is, multiMAGNA++ scales well to larger network data and can be parallelized effectively. During method evaluation, we also introduce new MNA quality measures to allow for more fair MNA method comparison compared to the existing alignment quality measures. MultiMAGNA++ code is available on the method's web page at http://nd.edu/~cone/multiMAGNA++/.

Global multi-layer network of human mobility

PubMed Central

Belyi, Alexander; Bojic, Iva; Sobolevsky, Stanislav; Sitko, Izabela; Hawelka, Bartosz; Rudikova, Lada; Kurbatski, Alexander; Ratti, Carlo

2017-01-01

ABSTRACT Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility – while the first two highlight short-term visits of people from one country to another, the last one – migration – shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately. PMID:28553155

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Uncoordinated MAC for Adaptive Multi-Beam Directional Networks: Analysis and Evaluation

DTIC Science & Technology

2016-04-10

transmission times, hence traditional CSMA approaches are not appropriate. We first present our model of these multi-beamforming capa- bilities and the...resulting wireless interference. We then derive an upper bound on multi-access performance for an idealized version of this physical layer. We then present... transmissions and receptions in a mobile ad-hoc network has in practice led to very constrained topologies. As mentioned, one approach for system design is to de

Generating global network structures by triad types

PubMed Central

Ferligoj, Anuška; Žiberna, Aleš

2018-01-01

This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks’ blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account. PMID:29847563

Adding the ‘heart’ to hanging drop networks for microphysiological multi-tissue experiments†

PubMed Central

Yazdi, Saeed Rismani; Shadmani, Amir; Bürgel, Sebastian C.; Misun, Patrick M.; Hierlemann, Andreas; Frey, Olivier

2017-01-01

Microfluidic hanging-drop networks enable culturing and analysis of 3D microtissue spheroids derived from different cell types under controlled perfusion and investigating inter-tissue communication in multi-tissue formats. In this paper we introduce a compact on-chip pumping approach for flow control in hanging-drop networks. The pump includes one pneumatic chamber located directly above one of the hanging drops and uses the surface tension at the liquid–air-interface for flow actuation. Control of the pneumatic protocol provides a wide range of unidirectional pulsatile and continuous flow profiles. With the proposed concept several independent hanging-drop networks can be operated in parallel with only one single pneumatic actuation line at high fidelity. Closed-loop medium circulation between different organ models for multi-tissue formats and multiple simultaneous assays in parallel are possible. Finally, we implemented a real-time feedback control-loop of the pump actuation based on the beating of a human iPS-derived cardiac microtissue cultured in the same system. This configuration allows for simulating physiological effects on the heart and their impact on flow circulation between the organ models on chip. PMID:26401602

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

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

PubMed Central

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

2011-01-01

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

Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: A multi-level regression approach.

PubMed

Uddin, Shahadat

2016-02-04

A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments.

Topology Control in Aerial Multi-Beam Directional Networks

DTIC Science & Technology

2017-04-24

underlying challenges to topology control in multi -beam direction networks. Two topology control algorithms are developed: a centralized algorithm...main beam, the gain is negligible. Thus, for topology control in a multi -beam system, two nodes that are being simultaneously transmitted to or...the network. As the network size is larger than the communication range, even the original network will require some multi -hop traffic. The second two

Guaranteeing Spoof-Resilient Multi-Robot Networks

DTIC Science & Technology

2015-05-12

particularly challenging attack on this assumption is the so-called “Sybil attack.” In a Sybil attack a malicious agent can generate (or spoof) a large...cybersecurity in general multi-node networks (e.g. a wired LAN), the same is not true for multi- robot networks [14, 28], leaving them largely vulnerable...key passing or cryptographic authen- tication is difficult to maintain due to the highly dynamic and distributed nature of multi-robot teams where

Exponential stability of stochastic complex networks with multi-weights based on graph theory

NASA Astrophysics Data System (ADS)

Zhang, Chunmei; Chen, Tianrui

2018-04-01

In this paper, a novel approach to exponential stability of stochastic complex networks with multi-weights is investigated by means of the graph-theoretical method. New sufficient conditions are provided to ascertain the moment exponential stability and almost surely exponential stability of stochastic complex networks with multiple weights. It is noted that our stability results are closely related with multi-weights and the intensity of stochastic disturbance. Numerical simulations are also presented to substantiate the theoretical results.

A multi-scale convolutional neural network for phenotyping high-content cellular images.

PubMed

Godinez, William J; Hossain, Imtiaz; Lazic, Stanley E; Davies, John W; Zhang, Xian

2017-07-01

Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. This study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via CNNs. The network specifications and solver definitions are provided in Supplementary Software 1. william_jose.godinez_navarro@novartis.com or xian-1.zhang@novartis.com. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A New Method for Setting Calculation Sequence of Directional Relay Protection in Multi-Loop Networks

NASA Astrophysics Data System (ADS)

Haijun, Xiong; Qi, Zhang

2016-08-01

Workload of relay protection setting calculation in multi-loop networks may be reduced effectively by optimization setting calculation sequences. A new method of setting calculation sequences of directional distance relay protection in multi-loop networks based on minimum broken nodes cost vector (MBNCV) was proposed to solve the problem experienced in current methods. Existing methods based on minimum breakpoint set (MBPS) lead to more break edges when untying the loops in dependent relationships of relays leading to possibly more iterative calculation workloads in setting calculations. A model driven approach based on behavior trees (BT) was presented to improve adaptability of similar problems. After extending the BT model by adding real-time system characters, timed BT was derived and the dependency relationship in multi-loop networks was then modeled. The model was translated into communication sequence process (CSP) models and an optimization setting calculation sequence in multi-loop networks was finally calculated by tools. A 5-nodes multi-loop network was applied as an example to demonstrate effectiveness of the modeling and calculation method. Several examples were then calculated with results indicating the method effectively reduces the number of forced broken edges for protection setting calculation in multi-loop networks.

Multi-channel distributed coordinated function over single radio in wireless sensor networks.

PubMed

Campbell, Carlene E-A; Loo, Kok-Keong Jonathan; Gemikonakli, Orhan; Khan, Shafiullah; Singh, Dhananjay

2011-01-01

Multi-channel assignments are becoming the solution of choice to improve performance in single radio for wireless networks. Multi-channel allows wireless networks to assign different channels to different nodes in real-time transmission. In this paper, we propose a new approach, Multi-channel Distributed Coordinated Function (MC-DCF) which takes advantage of multi-channel assignment. The backoff algorithm of the IEEE 802.11 distributed coordination function (DCF) was modified to invoke channel switching, based on threshold criteria in order to improve the overall throughput for wireless sensor networks (WSNs) over 802.11 networks. We presented simulation experiments in order to investigate the characteristics of multi-channel communication in wireless sensor networks using an NS2 platform. Nodes only use a single radio and perform channel switching only after specified threshold is reached. Single radio can only work on one channel at any given time. All nodes initiate constant bit rate streams towards the receiving nodes. In this work, we studied the impact of non-overlapping channels in the 2.4 frequency band on: constant bit rate (CBR) streams, node density, source nodes sending data directly to sink and signal strength by varying distances between the sensor nodes and operating frequencies of the radios with different data rates. We showed that multi-channel enhancement using our proposed algorithm provides significant improvement in terms of throughput, packet delivery ratio and delay. This technique can be considered for WSNs future use in 802.11 networks especially when the IEEE 802.11n becomes popular thereby may prevent the 802.15.4 network from operating effectively in the 2.4 GHz frequency band.

Multi-Channel Distributed Coordinated Function over Single Radio in Wireless Sensor Networks

PubMed Central

Campbell, Carlene E.-A.; Loo, Kok-Keong (Jonathan); Gemikonakli, Orhan; Khan, Shafiullah; Singh, Dhananjay

2011-01-01

Multi-channel assignments are becoming the solution of choice to improve performance in single radio for wireless networks. Multi-channel allows wireless networks to assign different channels to different nodes in real-time transmission. In this paper, we propose a new approach, Multi-channel Distributed Coordinated Function (MC-DCF) which takes advantage of multi-channel assignment. The backoff algorithm of the IEEE 802.11 distributed coordination function (DCF) was modified to invoke channel switching, based on threshold criteria in order to improve the overall throughput for wireless sensor networks (WSNs) over 802.11 networks. We presented simulation experiments in order to investigate the characteristics of multi-channel communication in wireless sensor networks using an NS2 platform. Nodes only use a single radio and perform channel switching only after specified threshold is reached. Single radio can only work on one channel at any given time. All nodes initiate constant bit rate streams towards the receiving nodes. In this work, we studied the impact of non-overlapping channels in the 2.4 frequency band on: constant bit rate (CBR) streams, node density, source nodes sending data directly to sink and signal strength by varying distances between the sensor nodes and operating frequencies of the radios with different data rates. We showed that multi-channel enhancement using our proposed algorithm provides significant improvement in terms of throughput, packet delivery ratio and delay. This technique can be considered for WSNs future use in 802.11 networks especially when the IEEE 802.11n becomes popular thereby may prevent the 802.15.4 network from operating effectively in the 2.4 GHz frequency band. PMID:22346614

Approaching human language with complex networks.

PubMed

Cong, Jin; Liu, Haitao

2014-12-01

The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics). Copyright © 2014 Elsevier B.V. All rights reserved.

Approaching human language with complex networks

NASA Astrophysics Data System (ADS)

Cong, Jin; Liu, Haitao

2014-12-01

The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

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.

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.

PubMed

Zou, Cunlu; Ladroue, Christophe; Guo, Shuixia; Feng, Jianfeng

2010-06-21

Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

A Composite Network Approach for Assessing Multi-Species Connectivity: An Application to Road Defragmentation Prioritisation

PubMed Central

Saura, Santiago; Rondinini, Carlo

2016-01-01

One of the biggest challenges in large-scale conservation is quantifying connectivity at broad geographic scales and for a large set of species. Because connectivity analyses can be computationally intensive, and the planning process quite complex when multiple taxa are involved, assessing connectivity at large spatial extents for many species turns to be often intractable. Such limitation results in that conducted assessments are often partial by focusing on a few key species only, or are generic by considering a range of dispersal distances and a fixed set of areas to connect that are not directly linked to the actual spatial distribution or mobility of particular species. By using a graph theory framework, here we propose an approach to reduce computational effort and effectively consider large assemblages of species in obtaining multi-species connectivity priorities. We demonstrate the potential of the approach by identifying defragmentation priorities in the Italian road network focusing on medium and large terrestrial mammals. We show that by combining probabilistic species graphs prior to conducting the network analysis (i) it is possible to analyse connectivity once for all species simultaneously, obtaining conservation or restoration priorities that apply for the entire species assemblage; and that (ii) those priorities are well aligned with the ones that would be obtained by aggregating the results of separate connectivity analysis for each of the individual species. This approach offers great opportunities to extend connectivity assessments to large assemblages of species and broad geographic scales. PMID:27768718

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

PubMed

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach

PubMed Central

Tong, Xin; Zeng, Yao

2016-01-01

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people’s incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make “good” inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows. PMID:27076691

An Intrusion Detection System Based on Multi-Level Clustering for Hierarchical Wireless Sensor Networks

PubMed Central

Butun, Ismail; Ra, In-Ho; Sankar, Ravi

2015-01-01

In this work, an intrusion detection system (IDS) framework based on multi-level clustering for hierarchical wireless sensor networks is proposed. The framework employs two types of intrusion detection approaches: (1) “downward-IDS (D-IDS)” to detect the abnormal behavior (intrusion) of the subordinate (member) nodes; and (2) “upward-IDS (U-IDS)” to detect the abnormal behavior of the cluster heads. By using analytical calculations, the optimum parameters for the D-IDS (number of maximum hops) and U-IDS (monitoring group size) of the framework are evaluated and presented. PMID:26593915

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

PubMed

Taboun, Mohammed S; Brennan, Robert W

2017-09-14

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

Convolutional Neural Network for Multi-Source Deep Learning Crop Classification in Ukraine

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.

2016-12-01

Land cover and crop type maps are one of the most essential inputs when dealing with environmental and agriculture monitoring tasks [1]. During long time neural network (NN) approach was one of the most efficient and popular approach for most applications, including crop classification using remote sensing data, with high an overall accuracy (OA) [2]. In the last years the most popular and efficient method for multi-sensor and multi-temporal land cover classification is convolution neural networks (CNNs). Taking into account presence clouds in optical data, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of optical imagery from Landsat-8 satellite. After missing data restoration, optical data from Landsat-8 was merged with Sentinel-1A radar data for better crop types discrimination [3]. An ensemble of CNNs is proposed for multi-temporal satellite images supervised classification. Each CNN in the corresponding ensemble is a 1-d CNN with 4 layers implemented using the Google's library TensorFlow. The efficiency of the proposed approach was tested on a time-series of Landsat-8 and Sentinel-1A images over the JECAM test site (Kyiv region) in Ukraine in 2015. Overall classification accuracy for ensemble of CNNs was 93.5% that outperformed an ensemble of multi-layer perceptrons (MLPs) by +0.8% and allowed us to better discriminate summer crops, in particular maize and soybeans. For 2016 we would like to validate this method using Sentinel-1 and Sentinel-2 data for Ukraine territory within ESA project on country level demonstration Sen2Agri. 1. A. Kolotii et al., "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine," The Int. Arch. of Photogram., Rem. Sens. and Spatial Inform. Scie., vol. 40, no. 7, pp. 39-44, 2015. 2. F. Waldner et al., "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity," Int. Journal of Rem. Sens. vol. 37, no. 14, pp

Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis.

PubMed

Frey, Olivier; Misun, Patrick M; Fluri, David A; Hengstler, Jan G; Hierlemann, Andreas

2014-06-30

Integration of multiple three-dimensional microtissues into microfluidic networks enables new insights in how different organs or tissues of an organism interact. Here, we present a platform that extends the hanging-drop technology, used for multi-cellular spheroid formation, to multifunctional complex microfluidic networks. Engineered as completely open, 'hanging' microfluidic system at the bottom of a substrate, the platform features high flexibility in microtissue arrangements and interconnections, while fabrication is simple and operation robust. Multiple spheroids of different cell types are formed in parallel on the same platform; the different tissues are then connected in physiological order for multi-tissue experiments through reconfiguration of the fluidic network. Liquid flow is precisely controlled through the hanging drops, which enable nutrient supply, substance dosage and inter-organ metabolic communication. The possibility to perform parallelized microtissue formation on the same chip that is subsequently used for complex multi-tissue experiments renders the developed platform a promising technology for 'body-on-a-chip'-related research.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

PubMed

Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing

2017-08-01

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Optimizing Airborne Networking Performance with Cross-Layer Design Approach

DTIC Science & Technology

2009-06-01

Schiavone , L.J.; “Airborne Networking –Approaches and Challenges,” Military Communications Conference IEEE, Oct 31 – Nov 3, 2004, Vol. 1, pp. 404...www.ccny.cuny.edu/cint/ [5] John Seguí and Esther Jennings,’’ Delay Tolerant Networking – Bundle Protocol Simulation’’ [6] DTNRG website...throughput route selection in multi-rate ad hoc wireless networks,” Technical report, Johns Hopkins CS Dept, March 2003. v 2. [15] R. Draves, J

A heuristic method for consumable resource allocation in multi-class dynamic PERT networks

NASA Astrophysics Data System (ADS)

Yaghoubi, Saeed; Noori, Siamak; Mazdeh, Mohammad Mahdavi

2013-06-01

This investigation presents a heuristic method for consumable resource allocation problem in multi-class dynamic Project Evaluation and Review Technique (PERT) networks, where new projects from different classes (types) arrive to system according to independent Poisson processes with different arrival rates. Each activity of any project is operated at a devoted service station located in a node of the network with exponential distribution according to its class. Indeed, each project arrives to the first service station and continues its routing according to precedence network of its class. Such system can be represented as a queuing network, while the discipline of queues is first come, first served. On the basis of presented method, a multi-class system is decomposed into several single-class dynamic PERT networks, whereas each class is considered separately as a minisystem. In modeling of single-class dynamic PERT network, we use Markov process and a multi-objective model investigated by Azaron and Tavakkoli-Moghaddam in 2007. Then, after obtaining the resources allocated to service stations in every minisystem, the final resources allocated to activities are calculated by the proposed method.

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

PubMed

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

2016-02-01

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

Multi-modal Social Networks: A MRF Learning Approach

DTIC Science & Technology

2016-06-20

number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. University of Texas at Austin 101 East 27th Street Suite 5.300 Austin , TX 78712 -1532...Proceedings of ACM Sigmetrics, Austin , TX June 2014. (17% acceptance) “Topic Modeling from Network Spread,” A. Ray, S. Sanghavi and S. Shakkottai...Proceedings of ACM Sigmetrics (poster paper), Austin , TX June 2014. Conclusions: Our approach based on hypothesis testing on graphs provides a

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

PubMed Central

Brennan, Robert W.

2017-01-01

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

Hop Optimization and Relay Node Selection in Multi-hop Wireless Ad-Hoc Networks

NASA Astrophysics Data System (ADS)

Li, Xiaohua(Edward)

In this paper we propose an efficient approach to determine the optimal hops for multi-hop ad hoc wireless networks. Based on the assumption that nodes use successive interference cancellation (SIC) and maximal ratio combining (MRC) to deal with mutual interference and to utilize all the received signal energy, we show that the signal-to-interference-plus-noise ratio (SINR) of a node is determined only by the nodes before it, not the nodes after it, along a packet forwarding path. Based on this observation, we propose an iterative procedure to select the relay nodes and to calculate the path SINR as well as capacity of an arbitrary multi-hop packet forwarding path. The complexity of the algorithm is extremely low, and scaling well with network size. The algorithm is applicable in arbitrarily large networks. Its performance is demonstrated as desirable by simulations. The algorithm can be helpful in analyzing the performance of multi-hop wireless networks.

Networked Guidance and Control for Mobile Multi-Agent Systems: A Multi-terminal (Network) Information Theoretic Approach

DTIC Science & Technology

2012-01-19

time , i.e., the state of the system is the input delayed by one time unit. In contrast with classical approaches, here the control action must be a...Transactions on Automatic Control , Vol. 56, No. 9, September 2011, Pages 2013-2025 Consider a first order linear time -invariant discrete time system driven by...1, January 2010, Pages 175-179 Consider a discrete- time networked control system , in which the controller has direct access to noisy

Approach jamming effectiveness evaluation for surface-type infrared decoy in network centric warship formation

NASA Astrophysics Data System (ADS)

Lv, Mingshan

2015-10-01

The passive and photoelectrical jamming to anti-ship missile in the condition of network centric warship formation is an important research issue of fleet EW operation. An approach jamming method of shipborne surface-type infrared decoy countering the infrared image guided anti-ship missile is put forward. By analyzing the countering process the jamming effectiveness evaluation model is constructed. By simulation the method is proved t reasonable and effective. This method breaks through the traditional restrict that the passive and photoelectricity jamming measure can only be used in the end self-defence and provides a new method for network centric worship formation to support each other.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity.

PubMed

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-08

The increasing number of recording electrodes enhances the capability of capturing the network's cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks.

PubMed

Lautenschlager, Karin; Hwang, Chiachi; Liu, Wen-Tso; Boon, Nico; Köster, Oliver; Vrouwenvelder, Hans; Egli, Thomas; Hammes, Frederik

2013-06-01

Biological stability of drinking water implies that the concentration of bacterial cells and composition of the microbial community should not change during distribution. In this study, we used a multi-parametric approach that encompasses different aspects of microbial water quality including microbial growth potential, microbial abundance, and microbial community composition, to monitor biological stability in drinking water of the non-chlorinated distribution system of Zürich. Drinking water was collected directly after treatment from the reservoir and in the network at several locations with varied average hydraulic retention times (6-52 h) over a period of four months, with a single repetition two years later. Total cell concentrations (TCC) measured with flow cytometry remained remarkably stable at 9.5 (± 0.6) × 10(4) cells/ml from water in the reservoir throughout most of the distribution network, and during the whole time period. Conventional microbial methods like heterotrophic plate counts, the concentration of adenosine tri-phosphate, total organic carbon and assimilable organic carbon remained also constant. Samples taken two years apart showed more than 80% similarity for the microbial communities analysed with denaturing gradient gel electrophoresis and 454 pyrosequencing. Only the two sampling locations with the longest water retention times were the exceptions and, so far for unknown reasons, recorded a slight but significantly higher TCC (1.3 (± 0.1) × 10(5) cells/ml) compared to the other locations. This small change in microbial abundance detected by flow cytometry was also clearly observed in a shift in the microbial community profiles to a higher abundance of members from the Comamonadaceae (60% vs. 2% at other locations). Conventional microbial detection methods were not able to detect changes as observed with flow cytometric cell counts and microbial community analysis. Our findings demonstrate that the multi-parametric approach used

Network Approach to Disease Diagnosis

NASA Astrophysics Data System (ADS)

Sharma, Amitabh; Bashan, Amir; Barabasi, Alber-Laszlo

2014-03-01

Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network (''interactome''). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. Support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health.

Optimal multi-community network modularity for information diffusion

NASA Astrophysics Data System (ADS)

Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

2016-02-01

Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

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

Na, Seungjin; Payne, Samuel H.; Bandeira, Nuno

The spectral networks approach enables the detection of pairs of spectra from related peptides and thus allows for the propagation of annotations from identified peptides to unidentified spectra. Beyond allowing for unbiased discovery of unexpected post-translational modifications, spectral networks are also applicable to multi-species comparative proteomics or metaproteomics to identify numerous orthologous versions of a protein. We present algorithmic and statistical advances in spectral networks that have made it possible to rigorously assess the statistical significance of spectral pairs and accurately estimate the error rate of identifications via propagation. In the analysis of three related Cyanothece species, a model organismmore » for biohydrogen production, spectral networks identified peptides with highly divergent sequences with up to dozens of variants per peptide, including many novel peptides in species that lack a sequenced genome. Furthermore, spectral networks strongly suggested the presence of novel peptides even in genomically characterized species (i.e. missing from databases) in that a significant portion of unidentified multi-species networks included at least two polymorphic peptide variants.« less

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

NASA Astrophysics Data System (ADS)

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-01

The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

Assessing the challenges of multi-scope clinical research sites: an example from NIH HIV/AIDS clinical trials networks.

PubMed

Rosas, Scott R; Cope, Marie T; Villa, Christie; Motevalli, Mahnaz; Utech, Jill; Schouten, Jeffrey T

2014-04-01

Large-scale, multi-network clinical trials are seen as a means for efficient and effective utilization of resources with greater responsiveness to new discoveries. Formal structures instituted within the National Institutes of Health (NIH) HIV/AIDS Clinical Trials facilitate collaboration and coordination across networks and emphasize an integrated approach to HIV/AIDS vaccine, prevention and therapeutics clinical trials. This study examines the joint usage of clinical research sites as means of gaining efficiency, extending capacity, and adding scientific value to the networks. A semi-structured questionnaire covering eight clinical management domains was administered to 74 (62% of sites) clinical site coordinators at single- and multi-network sites to identify challenges and efficiencies related to clinical trials management activities and coordination with multi-network units. Overall, respondents at multi-network sites did not report more challenges than single-network sites, but did report unique challenges to overcome including in the areas of study prioritization, community engagement, staff education and training, and policies and procedures. The majority of multi-network sites reported that such affiliations do allow for the consolidation and cost-sharing of research functions. Suggestions for increasing the efficiency or performance of multi-network sites included streamlining standards and requirements, consolidating protocol activation methods, using a single cross-network coordinating centre, and creating common budget and payment mechanisms. The results of this assessment provide important information to consider in the design and management of multi-network configurations for the NIH HIV/AIDS Clinical Trials Networks, as well as others contemplating and promoting the concept of multi-network settings. © 2013 John Wiley & Sons Ltd.

A multi-agent system architecture for sensor networks.

PubMed

Fuentes-Fernández, Rubén; Guijarro, María; Pajares, Gonzalo

2009-01-01

The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work.

Enhanced disease characterization through multi network functional normalization in fMRI.

PubMed

Çetin, Mustafa S; Khullar, Siddharth; Damaraju, Eswar; Michael, Andrew M; Baum, Stefi A; Calhoun, Vince D

2015-01-01

Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

Low Power Multi-Hop Networking Analysis in Intelligent Environments.

PubMed

Etxaniz, Josu; Aranguren, Gerardo

2017-05-19

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide.

Low Power Multi-Hop Networking Analysis in Intelligent Environments

PubMed Central

Etxaniz, Josu; Aranguren, Gerardo

2017-01-01

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide. PMID:28534847

Network module detection: Affinity search technique with the multi-node topological overlap measure

PubMed Central

Li, Ai; Horvath, Steve

2009-01-01

Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: PMID:19619323

Network module detection: Affinity search technique with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2009-07-20

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

Effects of multi-stakeholder platforms on multi-stakeholder innovation networks: Implications for research for development interventions targeting innovations at scale

PubMed Central

Schut, Marc; Hermans, Frans; van Asten, Piet; Leeuwis, Cees

2018-01-01

Multi-stakeholder platforms (MSPs) have been playing an increasing role in interventions aiming to generate and scale innovations in agricultural systems. However, the contribution of MSPs in achieving innovations and scaling has been varied, and many factors have been reported to be important for their performance. This paper aims to provide evidence on the contribution of MSPs to innovation and scaling by focusing on three developing country cases in Burundi, Democratic Republic of Congo, and Rwanda. Through social network analysis and logistic models, the paper studies the changes in the characteristics of multi-stakeholder innovation networks targeted by MSPs and identifies factors that play significant roles in triggering these changes. The results demonstrate that MSPs do not necessarily expand and decentralize innovation networks but can lead to contraction and centralization in the initial years of implementation. They show that some of the intended next users of interventions with MSPs–local-level actors–left the innovation networks, whereas the lead organization controlling resource allocation in the MSPs substantially increased its centrality. They also indicate that not all the factors of change in innovation networks are country specific. Initial conditions of innovation networks and funding provided by the MSPs are common factors explaining changes in innovation networks across countries and across different network functions. The study argues that investigating multi-stakeholder innovation network characteristics targeted by the MSP using a network approach in early implementation can contribute to better performance in generating and scaling innovations, and that funding can be an effective implementation tool in developing country contexts. PMID:29870559

Effects of multi-stakeholder platforms on multi-stakeholder innovation networks: Implications for research for development interventions targeting innovations at scale.

PubMed

Sartas, Murat; Schut, Marc; Hermans, Frans; Asten, Piet van; Leeuwis, Cees

2018-01-01

Multi-stakeholder platforms (MSPs) have been playing an increasing role in interventions aiming to generate and scale innovations in agricultural systems. However, the contribution of MSPs in achieving innovations and scaling has been varied, and many factors have been reported to be important for their performance. This paper aims to provide evidence on the contribution of MSPs to innovation and scaling by focusing on three developing country cases in Burundi, Democratic Republic of Congo, and Rwanda. Through social network analysis and logistic models, the paper studies the changes in the characteristics of multi-stakeholder innovation networks targeted by MSPs and identifies factors that play significant roles in triggering these changes. The results demonstrate that MSPs do not necessarily expand and decentralize innovation networks but can lead to contraction and centralization in the initial years of implementation. They show that some of the intended next users of interventions with MSPs-local-level actors-left the innovation networks, whereas the lead organization controlling resource allocation in the MSPs substantially increased its centrality. They also indicate that not all the factors of change in innovation networks are country specific. Initial conditions of innovation networks and funding provided by the MSPs are common factors explaining changes in innovation networks across countries and across different network functions. The study argues that investigating multi-stakeholder innovation network characteristics targeted by the MSP using a network approach in early implementation can contribute to better performance in generating and scaling innovations, and that funding can be an effective implementation tool in developing country contexts.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

PubMed Central

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-01-01

The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity. PMID:27824075

Multi-Hop Link Capacity of Multi-Route Multi-Hop MRC Diversity for a Virtual Cellular Network

NASA Astrophysics Data System (ADS)

Daou, Imane; Kudoh, Eisuke; Adachi, Fumiyuki

In virtual cellular network (VCN), proposed for high-speed mobile communications, the signal transmitted from a mobile terminal is received by some wireless ports distributed in each virtual cell and relayed to the central port that acts as a gateway to the core network. In this paper, we apply the multi-route MHMRC diversity in order to decrease the transmit power and increase the multi-hop link capacity. The transmit power, the interference power and the link capacity are evaluated for DS-CDMA multi-hop VCN by computer simulation. The multi-route MHMRC diversity can be applied to not only DS-CDMA but also other access schemes (i. e. MC-CDMA, OFDM, etc.).

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

NASA Astrophysics Data System (ADS)

Yin, Xi; Liu, Xiaoming

2018-02-01

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

Sampling Approaches for Multi-Domain Internet Performance Measurement Infrastructures

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

Calyam, Prasad

2014-09-15

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

A Multi-Agent System Architecture for Sensor Networks

PubMed Central

Fuentes-Fernández, Rubén; Guijarro, María; Pajares, Gonzalo

2009-01-01

The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work. PMID:22303172

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

NASA Astrophysics Data System (ADS)

Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

2013-01-01

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

Considerations for Software Defined Networking (SDN): Approaches and use cases

NASA Astrophysics Data System (ADS)

Bakshi, K.

Software Defined Networking (SDN) is an evolutionary approach to network design and functionality based on the ability to programmatically modify the behavior of network devices. SDN uses user-customizable and configurable software that's independent of hardware to enable networked systems to expand data flow control. SDN is in large part about understanding and managing a network as a unified abstraction. It will make networks more flexible, dynamic, and cost-efficient, while greatly simplifying operational complexity. And this advanced solution provides several benefits including network and service customizability, configurability, improved operations, and increased performance. There are several approaches to SDN and its practical implementation. Among them, two have risen to prominence with differences in pedigree and implementation. This paper's main focus will be to define, review, and evaluate salient approaches and use cases of the OpenFlow and Virtual Network Overlay approaches to SDN. OpenFlow is a communication protocol that gives access to the forwarding plane of a network's switches and routers. The Virtual Network Overlay relies on a completely virtualized network infrastructure and services to abstract the underlying physical network, which allows the overlay to be mobile to other physical networks. This is an important requirement for cloud computing, where applications and associated network services are migrated to cloud service providers and remote data centers on the fly as resource demands dictate. The paper will discuss how and where SDN can be applied and implemented, including research and academia, virtual multitenant data center, and cloud computing applications. Specific attention will be given to the cloud computing use case, where automated provisioning and programmable overlay for scalable multi-tenancy is leveraged via the SDN approach.

Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

PubMed Central

Lin, Kai; Wang, Di; Hu, Long

2016-01-01

With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods. PMID:27376302

Neural networks within multi-core optic fibers.

PubMed

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

2016-07-07

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

Optimization of Multiple Related Negotiation through Multi-Negotiation Network

NASA Astrophysics Data System (ADS)

Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi

In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a

Neural network for interpretation of multi-meaning Chinese words

NASA Astrophysics Data System (ADS)

He, Qianhua; Xu, Bingzheng

1994-03-01

We proposed a neural network that can interpret multi-meaning Chinese words correctly by using context information. The self-organized network, designed for translating Chinese to English, builds a context according to key words of the processed text and utilizes it to interpret multi-meaning words correctly. The network is generated automatically basing on a Chinese-English dictionary and a knowledge-base of weights, and can adapt to the change of contexts. Simulation experiments have proved that the network worked as expected.

Multi-level deep supervised networks for retinal vessel segmentation.

PubMed

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Multi-Scale Approach to Understanding Source-Sink Dynamics of Amphibians

DTIC Science & Technology

2015-12-01

spotted salamander, A. maculatum) at Fort Leonard Wood (FLW), Missouri. We used a multi-faceted approach in which we combined ecological , genetic...spotted salamander, A. maculatum) at Fort Leonard Wood , Missouri through a combination of intensive ecological field studies, genetic analyses, and...spatial demographic networks to identify optimal locations for wetland construction and restoration. Ecological Applications. Walls, S. C., Ball, L. C

A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis.

PubMed

Taroni, Jaclyn N; Greene, Casey S; Martyanov, Viktor; Wood, Tammara A; Christmann, Romy B; Farber, Harrison W; Lafyatis, Robert A; Denton, Christopher P; Hinchcliff, Monique E; Pioli, Patricia A; Mahoney, J Matthew; Whitfield, Michael L

2017-03-23

Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology. We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast the skin and lung microenvironments and to assess the functional role of the inflammatory and fibrotic genes in each organ. Lastly, we tested the expression of macrophage activation state-associated gene sets for enrichment in skin and lung using a Wilcoxon rank sum test. We identified a common pathogenic gene expression signature-an immune-fibrotic axis-indicative of pro-fibrotic macrophages (MØs) in multiple tissues (skin, lung, esophagus, and peripheral blood mononuclear cells) affected by SSc. While the co-expression of these genes is common to all tissues, the functional consequences of this upregulation differ by organ. We used this disease-associated signature to query tissue-specific functional genomic networks to identify common and tissue-specific pathologies of SSc and related conditions. In contrast to skin, in the lung-specific functional network we identify a distinct lung-resident MØ signature associated with lipid stimulation and alternative activation. In keeping with our network results, we find distinct MØ alternative activation transcriptional programs in SSc-associated PF lung and in the skin of patients with an "inflammatory" SSc gene expression signature. Our results suggest that the innate immune

Problems With Deployment of Multi-Domained, Multi-Homed Mobile Networks

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2008-01-01

This document describes numerous problems associated with deployment of multi-homed mobile platforms consisting of multiple networks and traversing large geographical areas. The purpose of this document is to provide insight to real-world deployment issues and provide information to groups that are addressing many issues related to multi-homing, policy-base routing, route optimization and mobile security - particularly those groups within the Internet Engineering Task Force.

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

Neural networks within multi-core optic fibers

PubMed Central

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

2016-01-01

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks. PMID:27383911

Detection of gene communities in multi-networks reveals cancer drivers

NASA Astrophysics Data System (ADS)

Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

2015-12-01

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Multi-GNSS PPP-RTK: From Large- to Small-Scale Networks.

PubMed

Nadarajah, Nandakumaran; Khodabandeh, Amir; Wang, Kan; Choudhury, Mazher; Teunissen, Peter J G

2018-04-03

Precise point positioning (PPP) and its integer ambiguity resolution-enabled variant, PPP-RTK (real-time kinematic), can benefit enormously from the integration of multiple global navigation satellite systems (GNSS). In such a multi-GNSS landscape, the positioning convergence time is expected to be reduced considerably as compared to the one obtained by a single-GNSS setup. It is therefore the goal of the present contribution to provide numerical insights into the role taken by the multi-GNSS integration in delivering fast and high-precision positioning solutions (sub-decimeter and centimeter levels) using PPP-RTK. To that end, we employ the Curtin PPP-RTK platform and process data-sets of GPS, BeiDou Navigation Satellite System (BDS) and Galileo in stand-alone and combined forms. The data-sets are collected by various receiver types, ranging from high-end multi-frequency geodetic receivers to low-cost single-frequency mass-market receivers. The corresponding stations form a large-scale (Australia-wide) network as well as a small-scale network with inter-station distances less than 30 km. In case of the Australia-wide GPS-only ambiguity-float setup, 90% of the horizontal positioning errors (kinematic mode) are shown to become less than five centimeters after 103 min. The stated required time is reduced to 66 min for the corresponding GPS + BDS + Galieo setup. The time is further reduced to 15 min by applying single-receiver ambiguity resolution. The outcomes are supported by the positioning results of the small-scale network.

Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Ji, Yuefeng; Zhang, Jie; Li, Hui; Xiong, Qianjin; Qiu, Shaofeng

2014-08-01

Ultrahigh throughout capacity requirement is challenging the current optical switching nodes with the fast development of data center networks. Pbit/s level all optical switching networks need to be deployed soon, which will cause the high complexity of node architecture. How to control the future network and node equipment together will become a new problem. An enhanced Software Defined Networking (eSDN) control architecture is proposed in the paper, which consists of Provider NOX (P-NOX) and Node NOX (N-NOX). With the cooperation of P-NOX and N-NOX, the flexible control of the entire network can be achieved. All optical switching network testbed has been experimentally demonstrated with efficient control of enhanced Software Defined Networking (eSDN). Pbit/s level all optical switching nodes in the testbed are implemented based on multi-dimensional switching architecture, i.e. multi-level and multi-planar. Due to the space and cost limitation, each optical switching node is only equipped with four input line boxes and four output line boxes respectively. Experimental results are given to verify the performance of our proposed control and switching architecture.

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

ERIC Educational Resources Information Center

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

A new multi-scale method to reveal hierarchical modular structures in biological networks.

PubMed

Jiao, Qing-Ju; Huang, Yan; Shen, Hong-Bin

2016-11-15

Biological networks are effective tools for studying molecular interactions. Modular structure, in which genes or proteins may tend to be associated with functional modules or protein complexes, is a remarkable feature of biological networks. Mining modular structure from biological networks enables us to focus on a set of potentially important nodes, which provides a reliable guide to future biological experiments. The first fundamental challenge in mining modular structure from biological networks is that the quality of the observed network data is usually low owing to noise and incompleteness in the obtained networks. The second problem that poses a challenge to existing approaches to the mining of modular structure is that the organization of both functional modules and protein complexes in networks is far more complicated than was ever thought. For instance, the sizes of different modules vary considerably from each other and they often form multi-scale hierarchical structures. To solve these problems, we propose a new multi-scale protocol for mining modular structure (named ISIMB) driven by a node similarity metric, which works in an iteratively converged space to reduce the effects of the low data quality of the observed network data. The multi-scale node similarity metric couples both the local and the global topology of the network with a resolution regulator. By varying this resolution regulator to give different weightings to the local and global terms in the metric, the ISIMB method is able to fit the shape of modules and to detect them on different scales. Experiments on protein-protein interaction and genetic interaction networks show that our method can not only mine functional modules and protein complexes successfully, but can also predict functional modules from specific to general and reveal the hierarchical organization of protein complexes.

Multi-enzyme logic network architectures for assessing injuries: digital processing of biomarkers.

PubMed

Halámek, Jan; Bocharova, Vera; Chinnapareddy, Soujanya; Windmiller, Joshua Ray; Strack, Guinevere; Chuang, Min-Chieh; Zhou, Jian; Santhosh, Padmanabhan; Ramirez, Gabriela V; Arugula, Mary A; Wang, Joseph; Katz, Evgeny

2010-12-01

A multi-enzyme biocatalytic cascade processing simultaneously five biomarkers characteristic of traumatic brain injury (TBI) and soft tissue injury (STI) was developed. The system operates as a digital biosensor based on concerted function of 8 Boolean AND logic gates, resulting in the decision about the physiological conditions based on the logic analysis of complex patterns of the biomarkers. The system represents the first example of a multi-step/multi-enzyme biosensor with the built-in logic for the analysis of complex combinations of biochemical inputs. The approach is based on recent advances in enzyme-based biocomputing systems and the present paper demonstrates the potential applicability of biocomputing for developing novel digital biosensor networks.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

PubMed

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-09-25

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

Social network types and well-being among South Korean older adults.

PubMed

Park, Sojung; Smith, Jacqui; Dunkle, Ruth E

2014-01-01

The social networks of older individuals reflect personal life history and cultural factors. Despite these two sources of variation, four similar network types have been identified in Europe, North America, Japan, and China: namely 'restricted', 'family', 'friend', and 'diverse'. This study identified the social network types of Korean older adults and examined differential associations of the network types with well-being. The analysis used data from the 2008 wave of the Korean Longitudinal Study of Aging (KLoSA: N = 4251, age range 65-108). We used a two-step cluster analytical approach to identify network types from seven indicators of network structure and function. Regression models determined associations between network types and well-being outcomes, including life satisfaction and depressive symptomatology. Cluster analysis of indicators of network structure and function revealed four types, including the restricted, friend, and diverse types. Instead of a family type, we found a couple-focused type. The young-old (age 65-74) were more likely to be in the couple-focused type and more of the oldest old (age 85+) belonged to the restricted type. Compared with the restricted network, older adults in all other networks were more likely to report higher life satisfaction and lower depressive symptomatology. Life course and cohort-related factors contribute to similarities across societies in network types and their associations with well-being. Korean-specific life course and socio-historical factors, however, may contribute to our unique findings about network types.

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

Hybrid protection algorithms based on game theory in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming

2011-12-01

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.

Efficiently Scheduling Multi-core Guest Virtual Machines on Multi-core Hosts in Network Simulation

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

Yoginath, Srikanth B; Perumalla, Kalyan S

2011-01-01

Virtual machine (VM)-based simulation is a method used by network simulators to incorporate realistic application behaviors by executing actual VMs as high-fidelity surrogates for simulated end-hosts. A critical requirement in such a method is the simulation time-ordered scheduling and execution of the VMs. Prior approaches such as time dilation are less efficient due to the high degree of multiplexing possible when multiple multi-core VMs are simulated on multi-core host systems. We present a new simulation time-ordered scheduler to efficiently schedule multi-core VMs on multi-core real hosts, with a virtual clock realized on each virtual core. The distinguishing features of ourmore » approach are: (1) customizable granularity of the VM scheduling time unit on the simulation time axis, (2) ability to take arbitrary leaps in virtual time by VMs to maximize the utilization of host (real) cores when guest virtual cores idle, and (3) empirically determinable optimality in the tradeoff between total execution (real) time and time-ordering accuracy levels. Experiments show that it is possible to get nearly perfect time-ordered execution, with a slight cost in total run time, relative to optimized non-simulation VM schedulers. Interestingly, with our time-ordered scheduler, it is also possible to reduce the time-ordering error from over 50% of non-simulation scheduler to less than 1% realized by our scheduler, with almost the same run time efficiency as that of the highly efficient non-simulation VM schedulers.« less

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Deterministic Teleportation of Multi-qudit States in a Network via Various Probabilistic Channels

NASA Astrophysics Data System (ADS)

Zhang, Ti-Hang; Jiang, Min; Huang, Xu; Wan, Min

2014-04-01

In this paper, we present a generalized approach to faithfully teleport an unknown state of a multi-qudit system involving multi spatially remote agents via various probabilistic channels. In a quantum teleportation network, there are generally multi spatially remote relay agents between a sender and a distant receiver. With the assistance of the relay agents, it is possible to directly construct a deterministic channel between the sender and the distant receiver. In our scheme, different from previous probabilistic teleportation protocols, the integrity of the unknown multi-qudit state could be maintained even when the construction of faithful channel fails. Our results also show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

Multipoint to multipoint routing and wavelength assignment in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Qin, Panke; Wu, Jingru; Li, Xudong; Tang, Yongli

2018-01-01

In multi-point to multi-point (MP2MP) routing and wavelength assignment (RWA) problems, researchers usually assume the optical networks to be a single domain. However, the optical networks develop toward to multi-domain and larger scale in practice. In this context, multi-core shared tree (MST)-based MP2MP RWA are introduced problems including optimal multicast domain sequence selection, core nodes belonging in which domains and so on. In this letter, we focus on MST-based MP2MP RWA problems in multi-domain optical networks, mixed integer linear programming (MILP) formulations to optimally construct MP2MP multicast trees is presented. A heuristic algorithm base on network virtualization and weighted clustering algorithm (NV-WCA) is proposed. Simulation results show that, under different traffic patterns, the proposed algorithm achieves significant improvement on network resources occupation and multicast trees setup latency in contrast with the conventional algorithms which were proposed base on a single domain network environment.

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

PubMed

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

3D multi-view convolutional neural networks for lung nodule classification

PubMed Central

Kang, Guixia; Hou, Beibei; Zhang, Ningbo

2017-01-01

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy. PMID:29145492

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

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

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

Corpus callosum segmentation using deep neural networks with prior information from multi-atlas images

NASA Astrophysics Data System (ADS)

Park, Gilsoon; Hong, Jinwoo; Lee, Jong-Min

2018-03-01

In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer's disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.

Multi-GNSS PPP-RTK: From Large- to Small-Scale Networks

PubMed Central

Nadarajah, Nandakumaran; Wang, Kan; Choudhury, Mazher

2018-01-01

Precise point positioning (PPP) and its integer ambiguity resolution-enabled variant, PPP-RTK (real-time kinematic), can benefit enormously from the integration of multiple global navigation satellite systems (GNSS). In such a multi-GNSS landscape, the positioning convergence time is expected to be reduced considerably as compared to the one obtained by a single-GNSS setup. It is therefore the goal of the present contribution to provide numerical insights into the role taken by the multi-GNSS integration in delivering fast and high-precision positioning solutions (sub-decimeter and centimeter levels) using PPP-RTK. To that end, we employ the Curtin PPP-RTK platform and process data-sets of GPS, BeiDou Navigation Satellite System (BDS) and Galileo in stand-alone and combined forms. The data-sets are collected by various receiver types, ranging from high-end multi-frequency geodetic receivers to low-cost single-frequency mass-market receivers. The corresponding stations form a large-scale (Australia-wide) network as well as a small-scale network with inter-station distances less than 30 km. In case of the Australia-wide GPS-only ambiguity-float setup, 90% of the horizontal positioning errors (kinematic mode) are shown to become less than five centimeters after 103 min. The stated required time is reduced to 66 min for the corresponding GPS + BDS + Galieo setup. The time is further reduced to 15 min by applying single-receiver ambiguity resolution. The outcomes are supported by the positioning results of the small-scale network. PMID:29614040

Probabilistic Approaches for Multi-Hazard Risk Assessment of Structures and Systems

NASA Astrophysics Data System (ADS)

Kwag, Shinyoung

Performance assessment of structures, systems, and components for multi-hazard scenarios has received significant attention in recent years. However, the concept of multi-hazard analysis is quite broad in nature and the focus of existing literature varies across a wide range of problems. In some cases, such studies focus on hazards that either occur simultaneously or are closely correlated with each other. For example, seismically induced flooding or seismically induced fires. In other cases, multi-hazard studies relate to hazards that are not dependent or correlated but have strong likelihood of occurrence at different times during the lifetime of a structure. The current approaches for risk assessment need enhancement to account for multi-hazard risks. It must be able to account for uncertainty propagation in a systems-level analysis, consider correlation among events or failure modes, and allow integration of newly available information from continually evolving simulation models, experimental observations, and field measurements. This dissertation presents a detailed study that proposes enhancements by incorporating Bayesian networks and Bayesian updating within a performance-based probabilistic framework. The performance-based framework allows propagation of risk as well as uncertainties in the risk estimates within a systems analysis. Unlike conventional risk assessment techniques such as a fault-tree analysis, a Bayesian network can account for statistical dependencies and correlations among events/hazards. The proposed approach is extended to develop a risk-informed framework for quantitative validation and verification of high fidelity system-level simulation tools. Validation of such simulations can be quite formidable within the context of a multi-hazard risk assessment in nuclear power plants. The efficiency of this approach lies in identification of critical events, components, and systems that contribute to the overall risk. Validation of any event or

Prediction of protein-protein interaction network using a multi-objective optimization approach.

PubMed

Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

2016-06-01

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).

PubMed

Borchani, Hanen; Bielza, Concha; Martı Nez-Martı N, Pablo; Larrañaga, Pedro

2012-12-01

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables. Copyright © 2012 Elsevier Inc. All rights reserved.

Identifying the Community Structure of the Food-Trade International Multi-Network

NASA Technical Reports Server (NTRS)

Torreggiani, S.; Mangioni, G.

2018-01-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network's community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001-2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors-such as geographical proximity and trade-agreement co-membership-than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential 'shocks' to global food trade.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

PubMed

Chen Peng; Ao Li

2017-01-01

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

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

PubMed Central

Qu, Xiaobo; He, Yifan

2018-01-01

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods. PMID:29509666

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

PubMed

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

2018-03-06

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

Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

PubMed

Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

2014-01-01

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

PubMed Central

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-01-01

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

A neural network approach to job-shop scheduling.

PubMed

Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

1991-01-01

A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.

Multi-Topic Tracking Model for dynamic social network

NASA Astrophysics Data System (ADS)

Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun

2016-07-01

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network.

PubMed

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-07-08

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods.

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network

PubMed Central

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-01-01

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods. PMID:28698466

Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

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

Gearhart, Jared Lee; Kurtz, Nolan Scot

2014-09-01

The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance aremore » investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.« less

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

PubMed

Cruz-Piris, Luis; Rivera, Diego; Fernandez, Susel; Marsa-Maestre, Ivan

2018-02-02

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

PubMed Central

2018-01-01

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network. PMID:29393884

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

PubMed

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Deep multi-scale convolutional neural network for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

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

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

NASA Astrophysics Data System (ADS)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Identifying the community structure of the food-trade international multi-network

NASA Astrophysics Data System (ADS)

Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

2018-05-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

Multi-casting approach for vascular networks in cellularized hydrogels.

PubMed

Justin, Alexander W; Brooks, Roger A; Markaki, Athina E

2016-12-01

Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200-250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel. © 2016 The Authors.

Multi-parametric centrality method for graph network models

NASA Astrophysics Data System (ADS)

Ivanov, Sergei Evgenievich; Gorlushkina, Natalia Nikolaevna; Ivanova, Lubov Nikolaevna

2018-04-01

The graph model networks are investigated to determine centrality, weights and the significance of vertices. For centrality analysis appliesa typical method that includesany one of the properties of graph vertices. In graph theory, methods of analyzing centrality are used: in terms by degree, closeness, betweenness, radiality, eccentricity, page-rank, status, Katz and eigenvector. We have proposed a new method of multi-parametric centrality, which includes a number of basic properties of the network member. The mathematical model of multi-parametric centrality method is developed. Comparison of results for the presented method with the centrality methods is carried out. For evaluate the results for the multi-parametric centrality methodthe graph model with hundreds of vertices is analyzed. The comparative analysis showed the accuracy of presented method, includes simultaneously a number of basic properties of vertices.

FACETS: multi-faceted functional decomposition of protein interaction networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2012-10-15

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Supplementary data are available at the Bioinformatics online. Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

Prior knowledge guided active modules identification: an integrated multi-objective approach.

PubMed

Chen, Weiqi; Liu, Jing; He, Shan

2017-03-14

Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

Using a multi-scale approach to identify and quantify oil and gas emissions: a case study for GHG emissions verification

NASA Astrophysics Data System (ADS)

Sweeney, C.; Kort, E. A.; Rella, C.; Conley, S. A.; Karion, A.; Lauvaux, T.; Frankenberg, C.

2015-12-01

Along with a boom in oil and natural gas production in the US, there has been a substantial effort to understand the true environmental impact of these operations on air and water quality, as well asnet radiation balance. This multi-institution effort funded by both governmental and non-governmental agencies has provided a case study for identification and verification of emissions using a multi-scale, top-down approach. This approach leverages a combination of remote sensing to identify areas that need specific focus and airborne in-situ measurements to quantify both regional and large- to mid-size single-point emitters. Ground-based networks of mobile and stationary measurements provide the bottom tier of measurements from which process-level information can be gathered to better understand the specific sources and temporal distribution of the emitters. The motivation for this type of approach is largely driven by recent work in the Barnett Shale region in Texas as well as the San Juan Basin in New Mexico and Colorado; these studies suggest that relatively few single-point emitters dominate the regional emissions of CH4.

How can the functioning and effectiveness of networks in the settings approach of health promotion be understood, achieved and researched?

PubMed

Dietscher, Christina

2017-02-01

Networks in health promotion (HP) have, after the launch of WHO's Ottawa Charter [(World Health Organization (WHO) (eds). (1986) Ottawa Charter on Health Promotion. Towards A New Public Health. World Health Organization, Geneva], become a widespread tool to disseminate HP especially in conjunction with the settings approach. Despite their allegedly high importance for HP practice and more than two decades of experiences with networking so far, a sound theoretical basis to support effective planning, formation, coordination and strategy development for networks in the settings approach of HP (HPSN) is still widely missing. Brößkamp-Stone's multi-facetted interorganizational network assessment framework (2004) provides a starting point but falls short of specifying the outcomes that can be reasonably expected from the specific network type of HPSN, and the specific processes/strategies and structures that are needed to achieve them. Based on outcome models in HP, on social, managerial and health science theories of networks, settings and organizations, a sociological systems theory approach and the capacity approach in HP, this article points out why existing approaches to studying networks are insufficient for HPSN, what can be understood by their functioning and effectiveness, what preconditions there are for HPSN effectiveness and how an HPSN functioning and effectiveness framework proposed on these grounds can be used for researching networks in practice, drawing on experiences from the ‘Project on an Internationally Comparative Evaluation Study of the International Network of Health Promoting Hospitals and Health Services’ (PRICES-HPH), which was coordinated by the WHO Collaborating Centre for Health Promotion in Hospitals and Health Services (Vienna WHO-CC) from 2008 to 2012.

An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation.

PubMed

Hayati, Elyas; Majnounian, Baris; Abdi, Ehsan; Sessions, John; Makhdoum, Majid

2013-02-01

Changes in forest landscapes resulting from road construction have increased remarkably in the last few years. On the other hand, the sustainable management of forest resources can only be achieved through a well-organized road network. In order to minimize the environmental impacts of forest roads, forest road managers must design the road network efficiently and environmentally as well. Efficient planning methodologies can assist forest road managers in considering the technical, economic, and environmental factors that affect forest road planning. This paper describes a three-stage methodology using the Delphi method for selecting the important criteria, the Analytic Hierarchy Process for obtaining the relative importance of the criteria, and finally, a spatial multi-criteria evaluation in a geographic information system (GIS) environment for identifying the lowest-impact road network alternative. Results of the Delphi method revealed that ground slope, lithology, distance from stream network, distance from faults, landslide susceptibility, erosion susceptibility, geology, and soil texture are the most important criteria for forest road planning in the study area. The suitability map for road planning was then obtained by combining the fuzzy map layers of these criteria with respect to their weights. Nine road network alternatives were designed using PEGGER, an ArcView GIS extension, and finally, their values were extracted from the suitability map. Results showed that the methodology was useful for identifying road that met environmental and cost considerations. Based on this work, we suggest future work in forest road planning using multi-criteria evaluation and decision making be considered in other regions and that the road planning criteria identified in this study may be useful.

Multi-Layer Approach for the Detection of Selective Forwarding Attacks

PubMed Central

Alajmi, Naser; Elleithy, Khaled

2015-01-01

Security breaches are a major threat in wireless sensor networks (WSNs). WSNs are increasingly used due to their broad range of important applications in both military and civilian domains. WSNs are prone to several types of security attacks. Sensor nodes have limited capacities and are often deployed in dangerous locations; therefore, they are vulnerable to different types of attacks, including wormhole, sinkhole, and selective forwarding attacks. Security attacks are classified as data traffic and routing attacks. These security attacks could affect the most significant applications of WSNs, namely, military surveillance, traffic monitoring, and healthcare. Therefore, there are different approaches to detecting security attacks on the network layer in WSNs. Reliability, energy efficiency, and scalability are strong constraints on sensor nodes that affect the security of WSNs. Because sensor nodes have limited capabilities in most of these areas, selective forwarding attacks cannot be easily detected in networks. In this paper, we propose an approach to selective forwarding detection (SFD). The approach has three layers: MAC pool IDs, rule-based processing, and anomaly detection. It maintains the safety of data transmission between a source node and base station while detecting selective forwarding attacks. Furthermore, the approach is reliable, energy efficient, and scalable. PMID:26610499

Multi-Layer Approach for the Detection of Selective Forwarding Attacks.

PubMed

Alajmi, Naser; Elleithy, Khaled

2015-11-19

Security breaches are a major threat in wireless sensor networks (WSNs). WSNs are increasingly used due to their broad range of important applications in both military and civilian domains. WSNs are prone to several types of security attacks. Sensor nodes have limited capacities and are often deployed in dangerous locations; therefore, they are vulnerable to different types of attacks, including wormhole, sinkhole, and selective forwarding attacks. Security attacks are classified as data traffic and routing attacks. These security attacks could affect the most significant applications of WSNs, namely, military surveillance, traffic monitoring, and healthcare. Therefore, there are different approaches to detecting security attacks on the network layer in WSNs. Reliability, energy efficiency, and scalability are strong constraints on sensor nodes that affect the security of WSNs. Because sensor nodes have limited capabilities in most of these areas, selective forwarding attacks cannot be easily detected in networks. In this paper, we propose an approach to selective forwarding detection (SFD). The approach has three layers: MAC pool IDs, rule-based processing, and anomaly detection. It maintains the safety of data transmission between a source node and base station while detecting selective forwarding attacks. Furthermore, the approach is reliable, energy efficient, and scalable.

Dynamic clustering scheme based on the coordination of management and control in multi-layer and multi-region intelligent optical network

NASA Astrophysics Data System (ADS)

Niu, Xiaoliang; Yuan, Fen; Huang, Shanguo; Guo, Bingli; Gu, Wanyi

2011-12-01

A Dynamic clustering scheme based on coordination of management and control is proposed to reduce network congestion rate and improve the blocking performance of hierarchical routing in Multi-layer and Multi-region intelligent optical network. Its implement relies on mobile agent (MA) technology, which has the advantages of efficiency, flexibility, functional and scalability. The paper's major contribution is to adjust dynamically domain when the performance of working network isn't in ideal status. And the incorporation of centralized NMS and distributed MA control technology migrate computing process to control plane node which releases the burden of NMS and improves process efficiently. Experiments are conducted on Multi-layer and multi-region Simulation Platform for Optical Network (MSPON) to assess the performance of the scheme.

Integration of multi-interface conversion channel using FPGA for modular photonic network

NASA Astrophysics Data System (ADS)

Janicki, Tomasz; Pozniak, Krzysztof T.; Romaniuk, Ryszard S.

2010-09-01

The article discusses the integration of different types of interfaces with FPGA circuits using a reconfigurable communication platform. The solution has been implemented in practice in a single node of a distributed measurement system. Construction of communication platform has been presented with its selected hardware modules, described in VHDL and implemented in FPGA circuits. The graphical user interface (GUI) has been described that allows a user to control the operation of the system. In the final part of the article selected practical solutions have been introduced. The whole measurement system resides on multi-gigabit optical network. The optical network construction is highly modular, reconfigurable and scalable.

Semi-Supervised Multi-View Learning for Gene Network Reconstruction

PubMed Central

Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Džeroski, Sašo

2015-01-01

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. PMID:26641091

Multi-Cohort Stand Structural Classification: Ground- and LiDAR-based Approaches for Boreal Mixedwood and Black Spruce Forest Types of Northeastern Ontario

NASA Astrophysics Data System (ADS)

Kuttner, Benjamin George

Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed "cohorts", and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground-based classifications and to extend structural cohort typing from plot- to stand-levels. In both mixedwood and black spruce forest types, stand age and age-related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age-emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using Li

The Lifecycle of Bayesian Network Models Developed for Multi-Source Signature Assessment of Nuclear Programs

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

Gastelum, Zoe N.; White, Amanda M.; Whitney, Paul D.

2013-06-04

The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory’s (PNNL) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information such as text, sensor output, imagery, or audio/video files, to assess nuclear activities through a series of Bayesian network (BN) models. These models incorporate knowledge from a diverse range of information sources in order to help assess a country’s nuclear activities. The models span engineering topic areas, state-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country’s likelihoodmore » to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model assists non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.« less

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool.

PubMed

Amoroso, N; Errico, R; Bruno, S; Chincarini, A; Garuccio, E; Sensi, F; Tangaro, S; Tateo, A; Bellotti, R

2015-11-21

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice[Formula: see text] and Dice[Formula: see text]). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

NASA Astrophysics Data System (ADS)

Amoroso, N.; Errico, R.; Bruno, S.; Chincarini, A.; Garuccio, E.; Sensi, F.; Tangaro, S.; Tateo, A.; Bellotti, R.; Alzheimers Disease Neuroimaging Initiative,the

2015-11-01

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer’s Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice{{}\\text{ADNI}} =0.929+/- 0.003 and Dice{{}\\text{OASIS}} =0.869+/- 0.002 ). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

Distributed Grooming in Multi-Domain IP/MPLS-DWDM Networks

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

Liu, Qing

2009-12-01

This paper studies distributed multi-domain, multilayer provisioning (grooming) in IP/MPLS-DWDM networks. Although many multi-domain studies have emerged over the years, these have primarily considered 'homogeneous' network layers. Meanwhile, most grooming studies have assumed idealized settings with 'global' link state across all layers. Hence there is a critical need to develop practical distributed grooming schemes for real-world networks consisting of multiple domains and technology layers. Along these lines, a detailed hierarchical framework is proposed to implement inter-layer routing, distributed grooming, and setup signaling. The performance of this solution is analyzed in detail using simulation studies and future work directions are alsomore » high-lighted.« less

A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

NASA Astrophysics Data System (ADS)

Bashiri, Mahdi; Farshbaf-Geranmayeh, Amir; Mogouie, Hamed

2013-11-01

In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB)-type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors' levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach.

High-precision multi-node clock network distribution.

PubMed

Chen, Xing; Cui, Yifan; Lu, Xing; Ci, Cheng; Zhang, Xuesong; Liu, Bo; Wu, Hong; Tang, Tingsong; Shi, Kebin; Zhang, Zhigang

2017-10-01

A high precision multi-node clock network for multiple users was built following the precise frequency transmission and time synchronization of 120 km fiber. The network topology adopts a simple star-shaped network structure. The clock signal of a hydrogen maser (synchronized with UTC) was recovered from a 120 km telecommunication fiber link and then was distributed to 4 sub-stations. The fractional frequency instability of all substations is in the level of 10 -15 in a second and the clock offset instability is in sub-ps in root-mean-square average.

Satellite rainfall monitoring over Africa using multi-spectral MSG data in an artificial neural network approach

NASA Astrophysics Data System (ADS)

Chadwick, Robin; Grimes, David

2010-05-01

Rainfall monitoring over Africa is crucial for a variety of humanitarian and agricultural purposes, and satellites have been used for some time to provide real-time rainfall estimates over the region. Several recent applications of satellite rainfall estimates, such as flash-flood warning systems and crop-yield models, require accurate rainfall totals at daily timescales or below. Multi-spectral Meteosat Second Generation (MSG) data provide information on cloud properties such as optical depth and cloud particle size and phase. These parameters are all relevant to the probability of rainfall occurring from a cloud and the likely intensity of that rainfall, so the use of MSG data should lead to improved satellite rainfall estimates. An artificial neural network (ANN) using multi-spectral inputs from MSG has been trained to provide daily rainfall estimates over Ethiopia, using daily rain-gauge data for calibration. Although ANN methods have previously been applied to the problem of producing rainfall estimates from multi-spectral satellite data, in general precipitation radar data have been used for calibration. The advantage of using rain-gauge data is that gauges are far more widespread over Africa than radar networks, so this method can be easily transferred and if necessary re-calibrated in different climatological regions of the continent. The ANN estimates have been validated against independent Ethiopian gauge data at a variety of time and space scales. The ANN shows an improvement in accuracy at daily timescale when compared to rainfall estimates from the TAMSAT algorithm, which uses only single channel MSG data.

Constructing Robust Cooperative Networks using a Multi-Objective Evolutionary Algorithm

PubMed Central

Wang, Shuai; Liu, Jing

2017-01-01

The design and construction of network structures oriented towards different applications has attracted much attention recently. The existing studies indicated that structural heterogeneity plays different roles in promoting cooperation and robustness. Compared with rewiring a predefined network, it is more flexible and practical to construct new networks that satisfy the desired properties. Therefore, in this paper, we study a method for constructing robust cooperative networks where the only constraint is that the number of nodes and links is predefined. We model this network construction problem as a multi-objective optimization problem and propose a multi-objective evolutionary algorithm, named MOEA-Netrc, to generate the desired networks from arbitrary initializations. The performance of MOEA-Netrc is validated on several synthetic and real-world networks. The results show that MOEA-Netrc can construct balanced candidates and is insensitive to the initializations. MOEA-Netrc can find the Pareto fronts for networks with different levels of cooperation and robustness. In addition, further investigation of the robustness of the constructed networks revealed the impact on other aspects of robustness during the construction process. PMID:28134314

FACETS: multi-faceted functional decomposition of protein interaction networks

PubMed Central

Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

2012-01-01

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

Participatory flood vulnerability assessment: a multi-criteria approach

NASA Astrophysics Data System (ADS)

Madruga de Brito, Mariana; Evers, Mariele; Delos Santos Almoradie, Adrian

2018-01-01

This paper presents a participatory multi-criteria decision-making (MCDM) approach for flood vulnerability assessment while considering the relationships between vulnerability criteria. The applicability of the proposed framework is demonstrated in the municipalities of Lajeado and Estrela, Brazil. The model was co-constructed by 101 experts from governmental organizations, universities, research institutes, NGOs, and private companies. Participatory methods such as the Delphi survey, focus groups, and workshops were applied. A participatory problem structuration, in which the modellers work closely with end users, was used to establish the structure of the vulnerability index. The preferences of each participant regarding the criteria importance were spatially modelled through the analytical hierarchy process (AHP) and analytical network process (ANP) multi-criteria methods. Experts were also involved at the end of the modelling exercise for validation. The final product is a set of individual and group flood vulnerability maps. Both AHP and ANP proved to be effective for flood vulnerability assessment; however, ANP is preferred as it considers the dependences among criteria. The participatory approach enabled experts to learn from each other and acknowledge different perspectives towards social learning. The findings highlight that to enhance the credibility and deployment of model results, multiple viewpoints should be integrated without forcing consensus.

Analysis of electrical tomography sensitive field based on multi-terminal network and electric field

NASA Astrophysics Data System (ADS)

He, Yongbo; Su, Xingguo; Xu, Meng; Wang, Huaxiang

2010-08-01

Electrical tomography (ET) aims at the study of the conductivity/permittivity distribution of the interested field non-intrusively via the boundary voltage/current. The sensor is usually regarded as an electric field, and finite element method (FEM) is commonly used to calculate the sensitivity matrix and to optimize the sensor architecture. However, only the lumped circuit parameters can be measured by the data acquisition electronics, it's very meaningful to treat the sensor as a multi terminal network. Two types of multi terminal network with common node and common loop topologies are introduced. Getting more independent measurements and making more uniform current distribution are the two main ways to minimize the inherent ill-posed effect. By exploring the relationships of network matrixes, a general formula is proposed for the first time to calculate the number of the independent measurements. Additionally, the sensitivity distribution is analyzed with FEM. As a result, quasi opposite mode, an optimal single source excitation mode, that has the advantages of more uniform sensitivity distribution and more independent measurements, is proposed.

An individual-based approach to SIR epidemics in contact networks.

PubMed

Youssef, Mina; Scoglio, Caterina

2011-08-21

Many approaches have recently been proposed to model the spread of epidemics on networks. For instance, the Susceptible/Infected/Recovered (SIR) compartmental model has successfully been applied to different types of diseases that spread out among humans and animals. When this model is applied on a contact network, the centrality characteristics of the network plays an important role in the spreading process. However, current approaches only consider an aggregate representation of the network structure, which can result in inaccurate analysis. In this paper, we propose a new individual-based SIR approach, which considers the whole description of the network structure. The individual-based approach is built on a continuous time Markov chain, and it is capable of evaluating the state probability for every individual in the network. Through mathematical analysis, we rigorously confirm the existence of an epidemic threshold below which an epidemic does not propagate in the network. We also show that the epidemic threshold is inversely proportional to the maximum eigenvalue of the network. Additionally, we study the role of the whole spectrum of the network, and determine the relationship between the maximum number of infected individuals and the set of eigenvalues and eigenvectors. To validate our approach, we analytically study the deviation with respect to the continuous time Markov chain model, and we show that the new approach is accurate for a large range of infection strength. Furthermore, we compare the new approach with the well-known heterogeneous mean field approach in the literature. Ultimately, we support our theoretical results through extensive numerical evaluations and Monte Carlo simulations. Published by Elsevier Ltd.

Analysis and Relative Evaluation of Connectivity of a Mobile Multi-Hop Network

NASA Astrophysics Data System (ADS)

Nakano, Keisuke; Miyakita, Kazuyuki; Sengoku, Masakazu; Shinoda, Shoji

In mobile multi-hop networks, a source node S and a destination node D sometimes encounter a situation where there is no multi-hop path between them when a message M, destined for D, arrives at S. In this situation, we cannot send M from S to D immediately; however, we can deliver M to D after waiting some time with the help of two capabilities of mobility. One of the capabilities is to construct a connected multi-hop path by changing the topology of the network during the waiting time (Capability 1), and the other is to move M closer to D during the waiting time (Capability 2). In this paper, we consider three methods to deliver M from S to D by using these capabilities in different ways. Method 1 uses Capability 1 and sends M from S to D after waiting until a connected multi-hop path appears between S and D. Method 2 uses Capability 2 and delivers M to D by allowing a mobile node to carry M from S to D. Method 3 is a combination of Methods 1 and 2 and minimizes the waiting time. We evaluate and compare these three methods in terms of the mean waiting time, from the time when M arrives at S to the time when D starts receiving M, as a new approach to connectivity evaluation. We consider a one-dimensional mobile multi-hop network consisting of mobile nodes flowing in opposite directions along a street. First, we derive some approximate equations and propose an estimation method to compute the mean waiting time of Method 1. Second, we theoretically analyze the mean waiting time of Method 2, and compute a lower bound of that of Method 3. By comparing the three methods under the same assumptions using results of the analyses and some simulation results, we show relations between the mean waiting times of these methods and show how Capabilities 1 and 2 differently affect the mean waiting time.

A multi-user real time inventorying system for radioactive materials: a networking approach.

PubMed

Mehta, S; Bandyopadhyay, D; Hoory, S

1998-01-01

A computerized system for radioisotope management and real time inventory coordinated across a large organization is reported. It handles hundreds of individual users and their separate inventory records. Use of highly efficient computer network and database technologies makes it possible to accept, maintain, and furnish all records related to receipt, usage, and disposal of the radioactive materials for the users separately and collectively. The system's central processor is an HP-9000/800 G60 RISC server and users from across the organization use their personal computers to login to this server using the TCP/IP networking protocol, which makes distributed use of the system possible. Radioisotope decay is automatically calculated by the program, so that it can make the up-to-date radioisotope inventory data of an entire institution available immediately. The system is specifically designed to allow use by large numbers of users (about 300) and accommodates high volumes of data input and retrieval without compromising simplicity and accuracy. Overall, it is an example of a true multi-user, on-line, relational database information system that makes the functioning of a radiation safety department efficient.

Economic and environmental optimization of a multi-site utility network for an industrial complex.

PubMed

Kim, Sang Hun; Yoon, Sung-Geun; Chae, Song Hwa; Park, Sunwon

2010-01-01

Most chemical companies consume a lot of steam, water and electrical resources in the production process. Given recent record fuel costs, utility networks must be optimized to reduce the overall cost of production. Environmental concerns must also be considered when preparing modifications to satisfy the requirements for industrial utilities, since wastes discharged from the utility networks are restricted by environmental regulations. Construction of Eco-Industrial Parks (EIPs) has drawn attention as a promising approach for retrofitting existing industrial parks to improve energy efficiency. The optimization of the utility network within an industrial complex is one of the most important undertakings to minimize energy consumption and waste loads in the EIP. In this work, a systematic approach to optimize the utility network of an industrial complex is presented. An important issue in the optimization of a utility network is the desire of the companies to achieve high profits while complying with the environmental regulations. Therefore, the proposed optimization was performed with consideration of both economic and environmental factors. The proposed approach consists of unit modeling using thermodynamic principles, mass and energy balances, development of a multi-period Mixed Integer Linear Programming (MILP) model for the integration of utility systems in an industrial complex, and an economic/environmental analysis of the results. This approach is applied to the Yeosu Industrial Complex, considering seasonal utility demands. The results show that both the total utility cost and waste load are reduced by optimizing the utility network of an industrial complex. 2009 Elsevier Ltd. All rights reserved.

Decoding the spatial signatures of multi-scale climate variability - a climate network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Jajcay, N.; Wiedermann, M.; Ekhtiari, N.; Palus, M.

2017-12-01

During the last years, the application of complex networks as a versatile tool for analyzing complex spatio-temporal data has gained increasing interest. Establishing this approach as a new paradigm in climatology has already provided valuable insights into key spatio-temporal climate variability patterns across scales, including novel perspectives on the dynamics of the El Nino Southern Oscillation or the emergence of extreme precipitation patterns in monsoonal regions. In this work, we report first attempts to employ network analysis for disentangling multi-scale climate variability. Specifically, we introduce the concept of scale-specific climate networks, which comprises a sequence of networks representing the statistical association structure between variations at distinct time scales. For this purpose, we consider global surface air temperature reanalysis data and subject the corresponding time series at each grid point to a complex-valued continuous wavelet transform. From this time-scale decomposition, we obtain three types of signals per grid point and scale - amplitude, phase and reconstructed signal, the statistical similarity of which is then represented by three complex networks associated with each scale. We provide a detailed analysis of the resulting connectivity patterns reflecting the spatial organization of climate variability at each chosen time-scale. Global network characteristics like transitivity or network entropy are shown to provide a new view on the (global average) relevance of different time scales in climate dynamics. Beyond expected trends originating from the increasing smoothness of fluctuations at longer scales, network-based statistics reveal different degrees of fragmentation of spatial co-variability patterns at different scales and zonal shifts among the key players of climate variability from tropically to extra-tropically dominated patterns when moving from inter-annual to decadal scales and beyond. The obtained results

Research of negotiation in network trade system based on multi-agent

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Guozheng; Wu, Haiyan

2009-07-01

A construction and implementation technology of network trade based on multi-agent is described in this paper. First, we researched the technology of multi-agent, then we discussed the consumer's behaviors and the negotiation between purchaser and bargainer which emerges in the traditional business mode and analysed the key technology to implement the network trade system. Finally, we implement the system.

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

NASA Astrophysics Data System (ADS)

Ahmadi, Bahman; Nariman-zadeh, Nader; Jamali, Ali

2017-06-01

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

NASA Astrophysics Data System (ADS)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Towards a Usability and Error "Safety Net": A Multi-Phased Multi-Method Approach to Ensuring System Usability and Safety.

PubMed

Kushniruk, Andre; Senathirajah, Yalini; Borycki, Elizabeth

2017-01-01

The usability and safety of health information systems have become major issues in the design and implementation of useful healthcare IT. In this paper we describe a multi-phased multi-method approach to integrating usability engineering methods into system testing to ensure both usability and safety of healthcare IT upon widespread deployment. The approach involves usability testing followed by clinical simulation (conducted in-situ) and "near-live" recording of user interactions with systems. At key stages in this process, usability problems are identified and rectified forming a usability and technology-induced error "safety net" that catches different types of usability and safety problems prior to releasing systems widely in healthcare settings.

Similarity network fusion for aggregating data types on a genomic scale.

PubMed

Wang, Bo; Mezlini, Aziz M; Demir, Feyyaz; Fiume, Marc; Tu, Zhuowen; Brudno, Michael; Haibe-Kains, Benjamin; Goldenberg, Anna

2014-03-01

Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.

Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

NASA Astrophysics Data System (ADS)

Nouiri, Issam

2017-11-01

This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.

Modeling of a production system using the multi-agent approach

NASA Astrophysics Data System (ADS)

Gwiazda, A.; Sękala, A.; Banaś, W.

2017-08-01

The method that allows for the analysis of complex systems is a multi-agent simulation. The multi-agent simulation (Agent-based modeling and simulation - ABMS) is modeling of complex systems consisting of independent agents. In the case of the model of the production system agents may be manufactured pieces set apart from other types of agents like machine tools, conveyors or replacements stands. Agents are magazines and buffers. More generally speaking, the agents in the model can be single individuals, but you can also be defined as agents of collective entities. They are allowed hierarchical structures. It means that a single agent could belong to a certain class. Depending on the needs of the agent may also be a natural or physical resource. From a technical point of view, the agent is a bundle of data and rules describing its behavior in different situations. Agents can be autonomous or non-autonomous in making the decision about the types of classes of agents, class sizes and types of connections between elements of the system. Multi-agent modeling is a very flexible technique for modeling and model creating in the convention that could be adapted to any research problem analyzed from different points of views. One of the major problems associated with the organization of production is the spatial organization of the production process. Secondly, it is important to include the optimal scheduling. For this purpose use can approach multi-purposeful. In this regard, the model of the production process will refer to the design and scheduling of production space for four different elements. The program system was developed in the environment NetLogo. It was also used elements of artificial intelligence. The main agent represents the manufactured pieces that, according to previously assumed rules, generate the technological route and allow preprint the schedule of that line. Machine lines, reorientation stands, conveyors and transport devices also represent the

Bidirectional Teleportation Protocol in Quantum Wireless Multi-hop Network

NASA Astrophysics Data System (ADS)

Cai, Rui; Yu, Xu-Tao; Zhang, Zai-Chen

2018-06-01

We propose a bidirectional quantum teleportation protocol based on a composite GHZ-Bell state. In this protocol, the composite GHZ-Bell state channel is transformed into two-Bell state channel through gate operations and single qubit measurements. The channel transformation will lead to different kinds of quantum channel states, so a method is proposed to help determine the unitary matrices effectively under different quantum channels. Furthermore, we discuss the bidirectional teleportation protocol in the quantum wireless multi-hop network. This paper is aimed to provide a bidirectional teleportation protocol and study the bidirectional multi-hop teleportation in the quantum wireless communication network.

Bidirectional Teleportation Protocol in Quantum Wireless Multi-hop Network

NASA Astrophysics Data System (ADS)

Cai, Rui; Yu, Xu-Tao; Zhang, Zai-Chen

2018-02-01

We propose a bidirectional quantum teleportation protocol based on a composite GHZ-Bell state. In this protocol, the composite GHZ-Bell state channel is transformed into two-Bell state channel through gate operations and single qubit measurements. The channel transformation will lead to different kinds of quantum channel states, so a method is proposed to help determine the unitary matrices effectively under different quantum channels. Furthermore, we discuss the bidirectional teleportation protocol in the quantum wireless multi-hop network. This paper is aimed to provide a bidirectional teleportation protocol and study the bidirectional multi-hop teleportation in the quantum wireless communication network.

LinkedOmics: analyzing multi-omics data within and across 32 cancer types.

PubMed

Vasaikar, Suhas V; Straub, Peter; Wang, Jing; Zhang, Bing

2018-01-04

The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Detecting anomalous traders using multi-slice network analysis

NASA Astrophysics Data System (ADS)

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2017-05-01

Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock market. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying anomalous traders using the transaction data of 8 manipulated stocks and 42 non-manipulated stocks during a one-year period. For each stock, we construct a multi-slice trading network to characterize the daily trading behavior and the cross-day participation of each trader. Comparing the multi-slice trading network of manipulated stocks and non-manipulated stocks with their randomized version, we find that manipulated stocks exhibit high number of trader pairs that trade with each other in multiple days and high deviation from randomized network at correlation between trading frequency and trading activity. These findings are effective at distinguishing manipulated stocks from non-manipulated ones and at identifying anomalous traders.

Benchmark data for identifying multi-functional types of membrane proteins.

PubMed

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2016-09-01

Identifying membrane proteins and their multi-functional types is an indispensable yet challenging topic in proteomics and bioinformatics. In this article, we provide data that are used for training and testing Mem-ADSVM (Wan et al., 2016. "Mem-ADSVM: a two-layer multi-label predictor for identifying multi-functional types of membrane proteins" [1]), a two-layer multi-label predictor for predicting multi-functional types of membrane proteins.

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

PubMed Central

Sun, Baoliang; Jiang, Chunlan; Li, Ming

2016-01-01

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

Load-adaptive practical multi-channel communications in wireless sensor networks.

PubMed

Islam, Md Shariful; Alam, Muhammad Mahbub; Hong, Choong Seon; Lee, Sungwon

2010-01-01

In recent years, a significant number of sensor node prototypes have been designed that provide communications in multiple channels. This multi-channel feature can be effectively exploited to increase the overall capacity and performance of wireless sensor networks (WSNs). In this paper, we present a multi-channel communications system for WSNs that is referred to as load-adaptive practical multi-channel communications (LPMC). LPMC estimates the active load of a channel at the sink since it has a more comprehensive view of the network behavior, and dynamically adds or removes channels based on the estimated load. LPMC updates the routing path to balance the loads of the channels. The nodes in a path use the same channel; therefore, they do not need to switch channels to receive or forward packets. LPMC has been evaluated through extensive simulations, and the results demonstrate that it can effectively increase the delivery ratio, network throughput, and channel utilization, and that it can decrease the end-to-end delay and energy consumption.

A scoring mechanism for the rank aggregation of network robustness

NASA Astrophysics Data System (ADS)

Yazdani, Alireza; Dueñas-Osorio, Leonardo; Li, Qilin

2013-10-01

To date, a number of metrics have been proposed to quantify inherent robustness of network topology against failures. However, each single metric usually only offers a limited view of network vulnerability to different types of random failures and targeted attacks. When applied to certain network configurations, different metrics rank network topology robustness in different orders which is rather inconsistent, and no single metric fully characterizes network robustness against different modes of failure. To overcome such inconsistency, this work proposes a multi-metric approach as the basis of evaluating aggregate ranking of network topology robustness. This is based on simultaneous utilization of a minimal set of distinct robustness metrics that are standardized so to give way to a direct comparison of vulnerability across networks with different sizes and configurations, hence leading to an initial scoring of inherent topology robustness. Subsequently, based on the inputs of initial scoring a rank aggregation method is employed to allocate an overall ranking of robustness to each network topology. A discussion is presented in support of the presented multi-metric approach and its applications to more realistically assess and rank network topology robustness.

A multi-period distribution network design model under demand uncertainty

NASA Astrophysics Data System (ADS)

Tabrizi, Babak H.; Razmi, Jafar

2013-05-01

Supply chain management is taken into account as an inseparable component in satisfying customers' requirements. This paper deals with the distribution network design (DND) problem which is a critical issue in achieving supply chain accomplishments. A capable DND can guarantee the success of the entire network performance. However, there are many factors that can cause fluctuations in input data determining market treatment, with respect to short-term planning, on the one hand. On the other hand, network performance may be threatened by the changes that take place within practicing periods, with respect to long-term planning. Thus, in order to bring both kinds of changes under control, we considered a new multi-period, multi-commodity, multi-source DND problem in circumstances where the network encounters uncertain demands. The fuzzy logic is applied here as an efficient tool for controlling the potential customers' demand risk. The defuzzifying framework leads the practitioners and decision-makers to interact with the solution procedure continuously. The fuzzy model is then validated by a sensitivity analysis test, and a typical problem is solved in order to illustrate the implementation steps. Finally, the formulation is tested by some different-sized problems to show its total performance.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

PubMed

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

PubMed

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

2015-12-26

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

PubMed Central

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X.

2015-01-01

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols. PMID:26712764

Multi-Criteria Optimization of Regulation in Metabolic Networks

PubMed Central

Higuera, Clara; Villaverde, Alejandro F.; Banga, Julio R.; Ross, John; Morán, Federico

2012-01-01

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks. PMID:22848435

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

PubMed

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate

A Gradient Optimization Approach to Adaptive Multi-Robot Control

DTIC Science & Technology

2009-09-01

implemented for deploying a group of three flying robots with downward facing cameras to monitor an environment on the ground. Thirdly, the multi-robot...theoretically proven, and implemented on multi-robot platforms. Thesis Supervisor: Daniela Rus Title: Professor of Electrical Engineering and Computer...often nonlinear, and they are coupled through a network which changes over time. Thirdly, implementing multi-robot controllers requires maintaining mul

Multi-criteria anomaly detection in urban noise sensor networks.

PubMed

Dauwe, Samuel; Oldoni, Damiano; De Baets, Bernard; Van Renterghem, Timothy; Botteldooren, Dick; Dhoedt, Bart

2014-01-01

The growing concern of citizens about the quality of their living environment and the emergence of low-cost microphones and data acquisition systems triggered the deployment of numerous noise monitoring networks spread over large geographical areas. Due to the local character of noise pollution in an urban environment, a dense measurement network is needed in order to accurately assess the spatial and temporal variations. The use of consumer grade microphones in this context appears to be very cost-efficient compared to the use of measurement microphones. However, the lower reliability of these sensing units requires a strong quality control of the measured data. To automatically validate sensor (microphone) data, prior to their use in further processing, a multi-criteria measurement quality assessment model for detecting anomalies such as microphone breakdowns, drifts and critical outliers was developed. Each of the criteria results in a quality score between 0 and 1. An ordered weighted average (OWA) operator combines these individual scores into a global quality score. The model is validated on datasets acquired from a real-world, extensive noise monitoring network consisting of more than 50 microphones. Over a period of more than a year, the proposed approach successfully detected several microphone faults and anomalies.

Implementation and Outcomes of a Collaborative Multi-Center Network Aimed at Web-Based Cognitive Training - COGWEB Network.

PubMed

Tedim Cruz, Vítor; Pais, Joana; Ruano, Luis; Mateus, Cátia; Colunas, Márcio; Alves, Ivânia; Barreto, Rui; Conde, Eduardo; Sousa, Andreia; Araújo, Isabel; Bento, Virgílio; Coutinho, Paula; Rocha, Nelson

2014-01-01

Cognitive care for the most prevalent neurologic and psychiatric conditions will only improve through the implementation of new sustainable approaches. Innovative cognitive training methodologies and collaborative professional networks are necessary evolutions in the mental health sector. The objective of the study was to describe the implementation process and early outcomes of a nationwide multi-organizational network supported on a Web-based cognitive training system (COGWEB). The setting for network implementation was the Portuguese mental health system and the hospital-, academic-, community-based institutions and professionals providing cognitive training. The network started in August 2012, with 16 centers, and was monitored until September 2013 (inclusions were open). After onsite training, all were allowed to use COGWEB in their clinical or research activities. For supervision and maintenance were implemented newsletters, questionnaires, visits and webinars. The following outcomes were prospectively measured: (1) number, (2) type, (3) time to start, and (4) activity state of centers; age, gender, level of education, and medical diagnosis of patients enrolled. The network included 68 professionals from 41 centers, (33/41) 80% clinical, (8/41) 19% nonclinical. A total of 298 patients received cognitive training; 45.3% (n=135) female, mean age 54.4 years (SD 18.7), mean educational level 9.8 years (SD 4.8). The number enrolled each month increased significantly (r=0.6; P=.031). At 12 months, 205 remained on treatment. The major causes of cognitive impairment were: (1) neurodegenerative (115/298, 38.6%), (2) structural brain lesions (63/298, 21.1%), (3) autoimmune (40/298, 13.4%), (4) schizophrenia (30/298, 10.1%), and (5) others (50/298, 16.8%). The comparison of the patient profiles, promoter versus all other clinical centers, showed significant increases in the diversity of causes and spectrums of ages and education. Over its first year, there was a major

Simultaneous observations of electromagnetically induced transparency (EIT) and absorption (EIA) in a multi-level V-type system of 87Rb and theoretical simulation of the observed spectra using a multi-mode approach

NASA Astrophysics Data System (ADS)

Das, Bankim Chandra; Bhattacharyya, Dipankar; Das, Arpita; Chakrabarti, Shrabana; De, Sankar

2016-12-01

We report here simultaneous experimental observation of Electromagnetically Induced Transparency (EIT) and Electromagnetically Induced Absorption (EIA) in a multi-level V-type system in D2 transition of 87Rb, i.e., F =2 →F' with a strong pump and a weak probe beam. We studied the probe spectrum by locking the probe beam to the transition F =2 →F'=2 while the pump is scanned from F =2 →F' . EIA is observed for the open transition (F =2 →F'=2 ) whereas EIT is observed in the closed transition (F =2 →F'=3 ). Sub natural line-width is observed for the EIA. To simulate the observed spectra theoretically, Liouville equation for the three-level V-type system is solved analytically with a multi-mode approach for the density matrix elements. We assumed both the pump and the probe beams can couple the excited states. A multi-mode approach for the coherence terms facilitates the study of all the frequency contributions due to the pump and the probe fields. Since the terms contain higher harmonics of the pump and the probe frequencies, we expressed them in Fourier transformed forms. To simulate the probe spectrum, we have solved inhomogeneous difference equations for the coherence terms using the Green's function technique and continued fraction theory. The experimental line-widths of the EIT and the EIA are compared with our theoretical model. Our system can be useful in optical switching applications as it can be precisely tuned to render the medium opaque and transparent simultaneously.

Simultaneous observations of electromagnetically induced transparency (EIT) and absorption (EIA) in a multi-level V-type system of 87Rb and theoretical simulation of the observed spectra using a multi-mode approach.

PubMed

Das, Bankim Chandra; Bhattacharyya, Dipankar; Das, Arpita; Chakrabarti, Shrabana; De, Sankar

2016-12-14

We report here simultaneous experimental observation of Electromagnetically Induced Transparency (EIT) and Electromagnetically Induced Absorption (EIA) in a multi-level V-type system in D 2 transition of Rb87, i.e., F=2→F ' with a strong pump and a weak probe beam. We studied the probe spectrum by locking the probe beam to the transition F=2→F ' =2 while the pump is scanned from F=2→F ' . EIA is observed for the open transition (F=2→F ' =2) whereas EIT is observed in the closed transition (F=2→F ' =3). Sub natural line-width is observed for the EIA. To simulate the observed spectra theoretically, Liouville equation for the three-level V-type system is solved analytically with a multi-mode approach for the density matrix elements. We assumed both the pump and the probe beams can couple the excited states. A multi-mode approach for the coherence terms facilitates the study of all the frequency contributions due to the pump and the probe fields. Since the terms contain higher harmonics of the pump and the probe frequencies, we expressed them in Fourier transformed forms. To simulate the probe spectrum, we have solved inhomogeneous difference equations for the coherence terms using the Green's function technique and continued fraction theory. The experimental line-widths of the EIT and the EIA are compared with our theoretical model. Our system can be useful in optical switching applications as it can be precisely tuned to render the medium opaque and transparent simultaneously.

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

Programmable multi-node quantum network design and simulation

NASA Astrophysics Data System (ADS)

Dasari, Venkat R.; Sadlier, Ronald J.; Prout, Ryan; Williams, Brian P.; Humble, Travis S.

2016-05-01

Software-defined networking offers a device-agnostic programmable framework to encode new network functions. Externally centralized control plane intelligence allows programmers to write network applications and to build functional network designs. OpenFlow is a key protocol widely adopted to build programmable networks because of its programmability, flexibility and ability to interconnect heterogeneous network devices. We simulate the functional topology of a multi-node quantum network that uses programmable network principles to manage quantum metadata for protocols such as teleportation, superdense coding, and quantum key distribution. We first show how the OpenFlow protocol can manage the quantum metadata needed to control the quantum channel. We then use numerical simulation to demonstrate robust programmability of a quantum switch via the OpenFlow network controller while executing an application of superdense coding. We describe the software framework implemented to carry out these simulations and we discuss near-term efforts to realize these applications.

(n, N) type maintenance policy for multi-component systems with failure interactions

NASA Astrophysics Data System (ADS)

Zhang, Zhuoqi; Wu, Su; Li, Binfeng; Lee, Seungchul

2015-04-01

This paper studies maintenance policies for multi-component systems in which failure interactions and opportunistic maintenance (OM) involve. This maintenance problem can be formulated as a Markov decision process (MDP). However, since an action set and state space in MDP exponentially expand as the number of components increase, traditional approaches are computationally intractable. To deal with curse of dimensionality, we decompose such a multi-component system into mutually influential single-component systems. Each single-component system is formulated as an MDP with the objective of minimising its long-run average maintenance cost. Under some reasonable assumptions, we prove the existence of the optimal (n, N) type policy for a single-component system. An algorithm to obtain the optimal (n, N) type policy is also proposed. Based on the proposed algorithm, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that failure interactions and OM pose significant effects on a maintenance policy.

First field trial of Virtual Network Operator oriented network on demand (NoD) service provisioning over software defined multi-vendor OTN networks

NASA Astrophysics Data System (ADS)

Li, Yajie; Zhao, Yongli; Zhang, Jie; Yu, Xiaosong; Chen, Haoran; Zhu, Ruijie; Zhou, Quanwei; Yu, Chenbei; Cui, Rui

2017-01-01

A Virtual Network Operator (VNO) is a provider and reseller of network services from other telecommunications suppliers. These network providers are categorized as virtual because they do not own the underlying telecommunication infrastructure. In terms of business operation, VNO can provide customers with personalized services by leasing network infrastructure from traditional network providers. The unique business modes of VNO lead to the emergence of network on demand (NoD) services. The conventional network provisioning involves a series of manual operation and configuration, which leads to high cost in time. Considering the advantages of Software Defined Networking (SDN), this paper proposes a novel NoD service provisioning solution to satisfy the private network need of VNOs. The solution is first verified in the real software defined multi-domain optical networks with multi-vendor OTN equipment. With the proposed solution, NoD service can be deployed via online web portals in near-real time. It reinvents the customer experience and redefines how network services are delivered to customers via an online self-service portal. Ultimately, this means a customer will be able to simply go online, click a few buttons and have new services almost instantaneously.

DReAM: Demand Response Architecture for Multi-level District Heating and Cooling Networks

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

Bhattacharya, Saptarshi; Chandan, Vikas; Arya, Vijay

In this paper, we exploit the inherent hierarchy of heat exchangers in District Heating and Cooling (DHC) networks and propose DReAM, a novel Demand Response (DR) architecture for Multi-level DHC networks. DReAM serves to economize system operation while still respecting comfort requirements of individual consumers. Contrary to many present day DR schemes that work on a consumer level granularity, DReAM works at a level of hierarchy above buildings, i.e. substations that supply heat to a group of buildings. This improves the overall DR scalability and reduce the computational complexity. In the first step of the proposed approach, mathematical models ofmore » individual substations and their downstream networks are abstracted into appropriately constructed low-complexity structural forms. In the second step, this abstracted information is employed by the utility to perform DR optimization that determines the optimal heat inflow to individual substations rather than buildings, in order to achieve the targeted objectives across the network. We validate the proposed DReAM framework through experimental results under different scenarios on a test network.« less

Parameter estimation in spiking neural networks: a reverse-engineering approach.

PubMed

Rostro-Gonzalez, H; Cessac, B; Vieville, T

2012-04-01

This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

PubMed

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimizing decentralized production-distribution planning problem in a multi-period supply chain network under uncertainty

NASA Astrophysics Data System (ADS)

Nourifar, Raheleh; Mahdavi, Iraj; Mahdavi-Amiri, Nezam; Paydar, Mohammad Mahdi

2017-09-01

Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Multi-channel multi-radio using 802.11 based media access for sink nodes in wireless sensor networks.

PubMed

Campbell, Carlene E-A; Khan, Shafiullah; Singh, Dhananjay; Loo, Kok-Keong

2011-01-01

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

Multi-Channel Multi-Radio Using 802.11 Based Media Access for Sink Nodes in Wireless Sensor Networks

PubMed Central

Campbell, Carlene E.-A.; Khan, Shafiullah; Singh, Dhananjay; Loo, Kok-Keong

2011-01-01

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives. PMID:22163883

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

Convolutional neural network with transfer learning for rice type classification

NASA Astrophysics Data System (ADS)

Patel, Vaibhav Amit; Joshi, Manjunath V.

2018-04-01

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

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

PubMed Central

Jin, Yichao; Vural, Serdar; Gluhak, Alexander; Moessner, Klaus

2013-01-01

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines. PMID:24135992

Social network type and morale in old age.

PubMed

Litwin, H

2001-08-01

The aim of this research was to derive network types among an elderly population and to examine the relationship of network type to morale. Secondary analysis of data compiled by the Israeli Central Bureau of Statistics (n = 2,079) was employed, and network types were derived through K-means cluster analysis. Respondents' morale scores were regressed on network types, controlling for background and health variables. Five network types were derived. Respondents in diverse or friends networks reported the highest morale; those in exclusively family or restricted networks had the lowest. Multivariate regression analysis underscored that certain network types were second among the study variables in predicting respondents' morale, preceded only by disability level (Adjusted R(2) =.41). Classification of network types allows consideration of the interpersonal environments of older people in relation to outcomes of interest. The relative effects on morale of elective versus obligated social ties, evident in the current analysis, is a case in point.

A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks

PubMed Central

Ye, Dayon; Zhang, Minji; Yang, Yu

2015-01-01

Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage. PMID:25928063

Multi-factor challenge/response approach for remote biometric authentication

NASA Astrophysics Data System (ADS)

Al-Assam, Hisham; Jassim, Sabah A.

2011-06-01

Although biometric authentication is perceived to be more reliable than traditional authentication schemes, it becomes vulnerable to many attacks when it comes to remote authentication over open networks and raises serious privacy concerns. This paper proposes a biometric-based challenge-response approach to be used for remote authentication between two parties A and B over open networks. In the proposed approach, a remote authenticator system B (e.g. a bank) challenges its client A who wants to authenticate his/her self to the system by sending a one-time public random challenge. The client A responds by employing the random challenge along with secret information obtained from a password and a token to produce a one-time cancellable representation of his freshly captured biometric sample. The one-time biometric representation, which is based on multi-factor, is then sent back to B for matching. Here, we argue that eavesdropping of the one-time random challenge and/or the resulting one-time biometric representation does not compromise the security of the system, and no information about the original biometric data is leaked. In addition to securing biometric templates, the proposed protocol offers a practical solution for the replay attack on biometric systems. Moreover, we propose a new scheme for generating a password-based pseudo random numbers/permutation to be used as a building block in the proposed approach. The proposed scheme is also designed to provide protection against repudiation. We illustrate the viability and effectiveness of the proposed approach by experimental results based on two biometric modalities: fingerprint and face biometrics.

Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans

NASA Astrophysics Data System (ADS)

Efrain Humpire-Mamani, Gabriel; Arindra Adiyoso Setio, Arnaud; van Ginneken, Bram; Jacobs, Colin

2018-04-01

Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view. Each network takes extra slices around the current slice as input to provide extra context. A sigmoid layer is used to perform multi-label classification. The output of the three networks is subsequently combined to compute a 3D bounding box for each structure. We used our approach to locate 11 structures of interest. The neural network was trained and evaluated on a large set of 1884 thorax-abdomen CT scans from patients undergoing oncological workup. Reference bounding boxes were annotated by human observers. The performance of our method was evaluated by computing the wall distance to the reference bounding boxes. The bounding boxes annotated by the first human observer were used as the reference standard for the test set. Using the best configuration, we obtained an average wall distance of 3.20~+/-~7.33 mm in the test set. The second human observer achieved 1.23~+/-~3.39 mm. For all structures, the results were better than those reported in previously published studies. In conclusion, we proposed an efficient method for the accurate localization of multiple organs. Our method uses multiple slices as input to provide more context around the slice under analysis, and we have shown that this improves performance. This method can easily be adapted to handle more organs.

A Network Pharmacology Approach to Understanding the Mechanisms of Action of Traditional Medicine: Bushenhuoxue Formula for Treatment of Chronic Kidney Disease

PubMed Central

Zheng, Xiao-yong; Cao, Dong-sheng; Ye, Fa-qing; Xiang, Zheng

2014-01-01

Traditional Chinese medicine (TCM) has unique therapeutic effects for complex chronic diseases. However, for the lack of an effective systematic approach, the research progress on the effective substances and pharmacological mechanism of action has been very slow. In this paper, by incorporating network biology, bioinformatics and chemoinformatics methods, an integrated approach was proposed to systematically investigate and explain the pharmacological mechanism of action and effective substances of TCM. This approach includes the following main steps: First, based on the known drug targets, network biology was used to screen out putative drug targets; Second, the molecular docking method was used to calculate whether the molecules from TCM and drug targets related to chronic kidney diseases (CKD) interact or not; Third, according to the result of molecular docking, natural product-target network, main component-target network and compound-target network were constructed; Finally, through analysis of network characteristics and literature mining, potential effective multi-components and their synergistic mechanism were putatively identified and uncovered. Bu-shen-Huo-xue formula (BSHX) which was frequently used for treating CKD, was used as the case to demonstrate reliability of our proposed approach. The results show that BSHX has the therapeutic effect by using multi-channel network regulation, such as regulating the coagulation and fibrinolytic balance, and the expression of inflammatory factors, inhibiting abnormal ECM accumulation. Tanshinone IIA, rhein, curcumin, calycosin and quercetin may be potential effective ingredients of BSHX. This research shows that the integration approach can be an effective means for discovering active substances and revealing their pharmacological mechanisms of TCM. PMID:24598793

Network design and analysis for multi-enzyme biocatalysis.

PubMed

Blaß, Lisa Katharina; Weyler, Christian; Heinzle, Elmar

2017-08-10

As more and more biological reaction data become available, the full exploration of the enzymatic potential for the synthesis of valuable products opens up exciting new opportunities but is becoming increasingly complex. The manual design of multi-step biosynthesis routes involving enzymes from different organisms is very challenging. To harness the full enzymatic potential, we developed a computational tool for the directed design of biosynthetic production pathways for multi-step catalysis with in vitro enzyme cascades, cell hydrolysates and permeabilized cells. We present a method which encompasses the reconstruction of a genome-scale pan-organism metabolic network, path-finding and the ranking of the resulting pathway candidates for proposing suitable synthesis pathways. The network is based on reaction and reaction pair data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the thermodynamics calculator eQuilibrator. The pan-organism network is especially useful for finding the most suitable pathway to a target metabolite from a thermodynamic or economic standpoint. However, our method can be used with any network reconstruction, e.g. for a specific organism. We implemented a path-finding algorithm based on a mixed-integer linear program (MILP) which takes into account both topology and stoichiometry of the underlying network. Unlike other methods we do not specify a single starting metabolite, but our algorithm searches for pathways starting from arbitrary start metabolites to a target product of interest. Using a set of biochemical ranking criteria including pathway length, thermodynamics and other biological characteristics such as number of heterologous enzymes or cofactor requirement, it is possible to obtain well-designed meaningful pathway alternatives. In addition, a thermodynamic profile, the overall reactant balance and potential side reactions as well as an SBML file for visualization are generated for each pathway alternative. We present

Analytical Study on Multi-Tier 5G Heterogeneous Small Cell Networks: Coverage Performance and Energy Efficiency.

PubMed

Xiao, Zhu; Liu, Hongjing; Havyarimana, Vincent; Li, Tong; Wang, Dong

2016-11-04

In this paper, we investigate the coverage performance and energy efficiency of multi-tier heterogeneous cellular networks (HetNets) which are composed of macrocells and different types of small cells, i.e., picocells and femtocells. By virtue of stochastic geometry tools, we model the multi-tier HetNets based on a Poisson point process (PPP) and analyze the Signal to Interference Ratio (SIR) via studying the cumulative interference from pico-tier and femto-tier. We then derive the analytical expressions of coverage probabilities in order to evaluate coverage performance in different tiers and investigate how it varies with the small cells' deployment density. By taking the fairness and user experience into consideration, we propose a disjoint channel allocation scheme and derive the system channel throughput for various tiers. Further, we formulate the energy efficiency optimization problem for multi-tier HetNets in terms of throughput performance and resource allocation fairness. To solve this problem, we devise a linear programming based approach to obtain the available area of the feasible solutions. System-level simulations demonstrate that the small cells' deployment density has a significant effect on the coverage performance and energy efficiency. Simulation results also reveal that there exits an optimal small cell base station (SBS) density ratio between pico-tier and femto-tier which can be applied to maximize the energy efficiency and at the same time enhance the system performance. Our findings provide guidance for the design of multi-tier HetNets for improving the coverage performance as well as the energy efficiency.

Analytical Study on Multi-Tier 5G Heterogeneous Small Cell Networks: Coverage Performance and Energy Efficiency

PubMed Central

Xiao, Zhu; Liu, Hongjing; Havyarimana, Vincent; Li, Tong; Wang, Dong

2016-01-01

In this paper, we investigate the coverage performance and energy efficiency of multi-tier heterogeneous cellular networks (HetNets) which are composed of macrocells and different types of small cells, i.e., picocells and femtocells. By virtue of stochastic geometry tools, we model the multi-tier HetNets based on a Poisson point process (PPP) and analyze the Signal to Interference Ratio (SIR) via studying the cumulative interference from pico-tier and femto-tier. We then derive the analytical expressions of coverage probabilities in order to evaluate coverage performance in different tiers and investigate how it varies with the small cells’ deployment density. By taking the fairness and user experience into consideration, we propose a disjoint channel allocation scheme and derive the system channel throughput for various tiers. Further, we formulate the energy efficiency optimization problem for multi-tier HetNets in terms of throughput performance and resource allocation fairness. To solve this problem, we devise a linear programming based approach to obtain the available area of the feasible solutions. System-level simulations demonstrate that the small cells’ deployment density has a significant effect on the coverage performance and energy efficiency. Simulation results also reveal that there exits an optimal small cell base station (SBS) density ratio between pico-tier and femto-tier which can be applied to maximize the energy efficiency and at the same time enhance the system performance. Our findings provide guidance for the design of multi-tier HetNets for improving the coverage performance as well as the energy efficiency. PMID:27827917

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

PubMed

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders

NASA Astrophysics Data System (ADS)

Abdi, Abdi M.; Szu, Harold H.

2003-04-01

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

Multi-Antenna Data Collector for Smart Metering Networks with Integrated Source Separation by Spatial Filtering

NASA Astrophysics Data System (ADS)

Quednau, Philipp; Trommer, Ralph; Schmidt, Lorenz-Peter

2016-03-01

Wireless transmission systems in smart metering networks share the advantage of lower installation costs due to the expandability of separate infrastructure but suffer from transmission problems. In this paper the issue of interference of wireless transmitted smart meter data with third party systems and data from other meters is investigated and an approach for solving the problem is presented. A multi-channel wireless m-bus receiver was developed to separate the desired data from unwanted interferers by spatial filtering. The according algorithms are presented and the influence of different antenna types on the spatial filtering is investigated. The performance of the spatial filtering is evaluated by extensive measurements in a realistic surrounding with several hundreds of active wireless m-bus transponders. These measurements correspond to the future environment for data-collectors as they took place in rural and urban areas with smart gas meters equipped with wireless m-bus transponders installed in almost all surrounding buildings.

Conjunctive management of multi-reservoir network system and groundwater system

NASA Astrophysics Data System (ADS)

Mani, A.; Tsai, F. T. C.

2015-12-01

This study develops a successive mixed-integer linear fractional programming (successive MILFP) method to conjunctively manage water resources provided by a multi-reservoir network system and a groundwater system. The conjunctive management objectives are to maximize groundwater withdrawals and maximize reservoir storages while satisfying water demands and raising groundwater level to a target level. The decision variables in the management problem are reservoir releases and spills, network flows and groundwater pumping rates. Using the fractional programming approach, the objective function is defined as a ratio of total groundwater withdraws to total reservoir storage deficits from the maximum storages. Maximizing this ratio function tends to maximizing groundwater use and minimizing surface water use. This study introduces a conditional constraint on groundwater head in order to sustain aquifers from overpumping: if current groundwater level is less than a target level, groundwater head at the next time period has to be raised; otherwise, it is allowed to decrease up to a certain extent. This conditional constraint is formulated into a set of mixed binary nonlinear constraints and results in a mixed-integer nonlinear fractional programming (MINLFP) problem. To solve the MINLFP problem, we first use the response matrix approach to linearize groundwater head with respect to pumping rate and reduce the problem to an MILFP problem. Using the Charnes-Cooper transformation, the MILFP is transformed to an equivalent mixed-integer linear programming (MILP). The solution of the MILP is successively updated by updating the response matrix in every iteration. The study uses IBM CPLEX to solve the MILP problem. The methodology is applied to water resources management in northern Louisiana. This conjunctive management approach aims to recover the declining groundwater level of the stressed Sparta aquifer by using surface water from a network of four reservoirs as an

A novel method for 3D measurement of RFID multi-tag network based on matching vision and wavelet

NASA Astrophysics Data System (ADS)

Zhuang, Xiao; Yu, Xiaolei; Zhao, Zhimin; Wang, Donghua; Zhang, Wenjie; Liu, Zhenlu; Lu, Dongsheng; Dong, Dingbang

2018-07-01

In the field of radio frequency identification (RFID), the three-dimensional (3D) distribution of RFID multi-tag networks has a significant impact on their reading performance. At the same time, in order to realize the anti-collision of RFID multi-tag networks in practical engineering applications, the 3D distribution of RFID multi-tag networks must be measured. In this paper, a novel method for the 3D measurement of RFID multi-tag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. Then, the wavelet threshold denoising method is used to remove noise in the obtained images. The template matching method is used to determine the two-dimensional coordinates and vertical coordinate of each tag. The 3D coordinates of each tag are obtained subsequently. Finally, a model of the nonlinear relation between the 3D coordinate distribution of the RFID multi-tag network and the corresponding reading distance is established using the wavelet neural network. The experiment results show that the average prediction relative error is 0.71% and the time cost is 2.17 s. The values of the average prediction relative error and time cost are smaller than those of the particle swarm optimization neural network and genetic algorithm–back propagation neural network. The time cost of the wavelet neural network is about 1% of that of the other two methods. The method proposed in this paper has a smaller relative error. The proposed method can improve the real-time performance of RFID multi-tag networks and the overall dynamic performance of multi-tag networks.

Multi-Hazard Interactions in Guatemala

NASA Astrophysics Data System (ADS)

Gill, Joel; Malamud, Bruce D.

2017-04-01

In this paper, we combine physical and social science approaches to develop a multi-scale regional framework for natural hazard interactions in Guatemala. The identification and characterisation of natural hazard interactions is an important input for comprehensive multi-hazard approaches to disaster risk reduction at a regional level. We use five transdisciplinary evidence sources to organise and populate our framework: (i) internationally-accessible literature; (ii) civil protection bulletins; (iii) field observations; (iv) stakeholder interviews (hazard and civil protection professionals); and (v) stakeholder workshop results. These five evidence sources are synthesised to determine an appropriate natural hazard classification scheme for Guatemala (6 hazard groups, 19 hazard types, and 37 hazard sub-types). For a national spatial extent (Guatemala), we construct and populate a "21×21" hazard interaction matrix, identifying 49 possible interactions between 21 hazard types. For a sub-national spatial extent (Southern Highlands, Guatemala), we construct and populate a "33×33" hazard interaction matrix, identifying 112 possible interactions between 33 hazard sub-types. Evidence sources are also used to constrain anthropogenic processes that could trigger natural hazards in Guatemala, and characterise possible networks of natural hazard interactions (cascades). The outcomes of this approach are among the most comprehensive interaction frameworks for national and sub-national spatial scales in the published literature. These can be used to support disaster risk reduction and civil protection professionals in better understanding natural hazards and potential disasters at a regional scale.

Networked Airborne Communications Using Adaptive Multi Beam Directional Links

DTIC Science & Technology

2016-03-05

Networked Airborne Communications Using Adaptive Multi-Beam Directional Links R. Bruce MacLeod Member, IEEE, and Adam Margetts Member, IEEE MIT...provide new techniques for increasing throughput in airborne adaptive directional net- works. By adaptive directional linking, we mean systems that can...techniques can dramatically increase the capacity in airborne networks. Advances in digital array technology are beginning to put these gains within reach

Multi-voxel Patterns Reveal Functionally Differentiated Networks Underlying Auditory Feedback Processing of Speech

PubMed Central

Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.; Munhall, Kevin G.; Cusack, Rhodri; Johnsrude, Ingrid S.

2013-01-01

The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations within a multi-voxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was employed to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during vocalization, compared to during passive listening. One network of regions appears to encode an ‘error signal’ irrespective of acoustic features of the error: this network, including right angular gyrus, right supplementary motor area, and bilateral cerebellum, yielded consistent neural patterns across acoustically different, distorted feedback types, only during articulation (not during passive listening). In contrast, a fronto-temporal network appears sensitive to the speech features of auditory stimuli during passive listening; this preference for speech features was diminished when the same stimuli were presented as auditory concomitants of vocalization. A third network, showing a distinct functional pattern from the other two, appears to capture aspects of both neural response profiles. Taken together, our findings suggest that auditory feedback processing during speech motor control may rely on multiple, interactive, functionally differentiated neural systems. PMID:23467350

Network-based Approaches in Pharmacology.

PubMed

Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

2017-10-01

In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Extended multi-configuration quasi-degenerate perturbation theory: the new approach to multi-state multi-reference perturbation theory.

PubMed

Granovsky, Alexander A

2011-06-07

The distinctive desirable features, both mathematically and physically meaningful, for all partially contracted multi-state multi-reference perturbation theories (MS-MR-PT) are explicitly formulated. The original approach to MS-MR-PT theory, called extended multi-configuration quasi-degenerate perturbation theory (XMCQDPT), having most, if not all, of the desirable properties is introduced. The new method is applied at the second order of perturbation theory (XMCQDPT2) to the 1(1)A(')-2(1)A(') conical intersection in allene molecule, the avoided crossing in LiF molecule, and the 1(1)A(1) to 2(1)A(1) electronic transition in cis-1,3-butadiene. The new theory has several advantages compared to those of well-established approaches, such as second order multi-configuration quasi-degenerate perturbation theory and multi-state-second order complete active space perturbation theory. The analysis of the prevalent approaches to the MS-MR-PT theory performed within the framework of the XMCQDPT theory unveils the origin of their common inherent problems. We describe the efficient implementation strategy that makes XMCQDPT2 an especially useful general-purpose tool in the high-level modeling of small to large molecular systems. © 2011 American Institute of Physics

Assessment of spare reliability for multi-state computer networks within tolerable packet unreliability

NASA Astrophysics Data System (ADS)

Lin, Yi-Kuei; Huang, Cheng-Fu

2015-04-01

From a quality of service viewpoint, the transmission packet unreliability and transmission time are both critical performance indicators in a computer system when assessing the Internet quality for supervisors and customers. A computer system is usually modelled as a network topology where each branch denotes a transmission medium and each vertex represents a station of servers. Almost every branch has multiple capacities/states due to failure, partial failure, maintenance, etc. This type of network is known as a multi-state computer network (MSCN). This paper proposes an efficient algorithm that computes the system reliability, i.e., the probability that a specified amount of data can be sent through k (k ≥ 2) disjoint minimal paths within both the tolerable packet unreliability and time threshold. Furthermore, two routing schemes are established in advance to indicate the main and spare minimal paths to increase the system reliability (referred to as spare reliability). Thus, the spare reliability can be readily computed according to the routing scheme.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

PubMed Central

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Network neighborhood analysis with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2007-01-15

The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures. The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes. An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).

An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks

DOE PAGES

Ji, Haoran; Wang, Chengshan; Li, Peng; ...

2017-09-20

The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposedmore » in this paper. Furthermore, by regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. In order to tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.« less

An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks

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

Ji, Haoran; Wang, Chengshan; Li, Peng

The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposedmore » in this paper. Furthermore, by regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. In order to tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.« less

Interference Drop Scheme: Enhancing QoS Provision in Multi-Hop Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Luo, Chang-Yi; Komuro, Nobuyoshi; Takahashi, Kiyoshi; Kasai, Hiroyuki; Ueda, Hiromi; Tsuboi, Toshinori

Ad hoc networking uses wireless technologies to construct networks with no physical infrastructure and so are expected to provide instant networking in areas such as disaster recovery sites and inter-vehicle communication. Unlike conventional wired networks services, services in ad hoc networks are easily disrupted by the frequent changes in traffic and topology. Therefore, solutions to assure the Quality of Services (QoS) in ad hoc networks are different from the conventional ones used in wired networks. In this paper, we propose a new queue management scheme, Interference Drop Scheme (IDS) for ad hoc networks. In the conventional queue management approaches such as FIFO (First-in First-out) and RED (Random Early Detection), a queue is usually managed by a queue length limit. FIFO discards packets according to the queue limit, and RED discards packets in an early and random fashion. IDS, on the other hand, manages the queue according to wireless interference time, which increases as the number of contentions in the MAC layer increases. When there are many MAC contentions, IDS discards TCP data packets. By observing the interference time and discarding TCP data packets, our simulation results show that IDS improves TCP performance and reduces QoS violations in UDP in ad hoc networks with chain, grid, and random topologies. Our simulation results also demonstrate that wireless interference time is a better metric than queue length limit for queue management in multi-hop ad hoc networks.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

PubMed

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

PubMed

Poornima, Paramasivan; Kumar, Jothi Dinesh; Zhao, Qiaoli; Blunder, Martina; Efferth, Thomas

2016-09-01

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer. Copyright © 2016 Elsevier Ltd. All rights reserved.

Topology of molecular interaction networks.

PubMed

Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

2013-09-16

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Topology of molecular interaction networks

PubMed Central

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

A multi-objective approach to improve SWAT model calibration in alpine catchments

NASA Astrophysics Data System (ADS)

Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele

2018-04-01

Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

A multi-resolution approach to electromagnetic modelling

NASA Astrophysics Data System (ADS)

Cherevatova, M.; Egbert, G. D.; Smirnov, M. Yu

2018-07-01

We present a multi-resolution approach for 3-D magnetotelluric forward modelling. Our approach is motivated by the fact that fine-grid resolution is typically required at shallow levels to adequately represent near surface inhomogeneities, topography and bathymetry, while a much coarser grid may be adequate at depth where the diffusively propagating electromagnetic fields are much smoother. With a conventional structured finite difference grid, the fine discretization required to adequately represent rapid variations near the surface is continued to all depths, resulting in higher computational costs. Increasing the computational efficiency of the forward modelling is especially important for solving regularized inversion problems. We implement a multi-resolution finite difference scheme that allows us to decrease the horizontal grid resolution with depth, as is done with vertical discretization. In our implementation, the multi-resolution grid is represented as a vertical stack of subgrids, with each subgrid being a standard Cartesian tensor product staggered grid. Thus, our approach is similar to the octree discretization previously used for electromagnetic modelling, but simpler in that we allow refinement only with depth. The major difficulty arose in deriving the forward modelling operators on interfaces between adjacent subgrids. We considered three ways of handling the interface layers and suggest a preferable one, which results in similar accuracy as the staggered grid solution, while retaining the symmetry of coefficient matrix. A comparison between multi-resolution and staggered solvers for various models shows that multi-resolution approach improves on computational efficiency without compromising the accuracy of the solution.

A neural network approach for enhancing information extraction from multispectral image data

USGS Publications Warehouse

Liu, J.; Shao, G.; Zhu, H.; Liu, S.

2005-01-01

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

NASA Astrophysics Data System (ADS)

Shishebori, Davood; Babadi, Abolghasem Yousefi

2018-03-01

This study investigates the reliable multi-configuration capacitated logistics network design problem (RMCLNDP) under system disturbances, which relates to locating facilities, establishing transportation links, and also allocating their limited capacities to the customers conducive to provide their demand on the minimum expected total cost (including locating costs, link constructing costs, and also expected costs in normal and disturbance conditions). In addition, two types of risks are considered; (I) uncertain environment, (II) system disturbances. A two-level mathematical model is proposed for formulating of the mentioned problem. Also, because of the uncertain parameters of the model, an efficacious possibilistic robust optimization approach is utilized. To evaluate the model, a drug supply chain design (SCN) is studied. Finally, an extensive sensitivity analysis was done on the critical parameters. The obtained results show that the efficiency of the proposed approach is suitable and is worthwhile for analyzing the real practical problems.

Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

NASA Astrophysics Data System (ADS)

Lin, Yi-Kuei; Yeh, Cheng-Ta

2013-05-01

From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.

Design of shared unit-dose drug distribution network using multi-level particle swarm optimization.

PubMed

Chen, Linjie; Monteiro, Thibaud; Wang, Tao; Marcon, Eric

2018-03-01

Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.

A Multi-level Fuzzy Evaluation Method for Smart Distribution Network Based on Entropy Weight

NASA Astrophysics Data System (ADS)

Li, Jianfang; Song, Xiaohui; Gao, Fei; Zhang, Yu

2017-05-01

Smart distribution network is considered as the future trend of distribution network. In order to comprehensive evaluate smart distribution construction level and give guidance to the practice of smart distribution construction, a multi-level fuzzy evaluation method based on entropy weight is proposed. Firstly, focus on both the conventional characteristics of distribution network and new characteristics of smart distribution network such as self-healing and interaction, a multi-level evaluation index system which contains power supply capability, power quality, economy, reliability and interaction is established. Then, a combination weighting method based on Delphi method and entropy weight method is put forward, which take into account not only the importance of the evaluation index in the experts’ subjective view, but also the objective and different information from the index values. Thirdly, a multi-level evaluation method based on fuzzy theory is put forward. Lastly, an example is conducted based on the statistical data of some cites’ distribution network and the evaluation method is proved effective and rational.

Network cyberinfrastructure as a shared platform to support multi-site research

USDA-ARS?s Scientific Manuscript database

Multi-site research across the Long-term Agroecosystem Research (LTAR) network requires access to data and information. We present some existing examples where you can get data from across the network and summarize the rich inventory of measurements taken across LTAR sites. But data management suppo...

Dynamic configuration management of a multi-standard and multi-mode reconfigurable multi-ASIP architecture for turbo decoding

NASA Astrophysics Data System (ADS)

Lapotre, Vianney; Gogniat, Guy; Baghdadi, Amer; Diguet, Jean-Philippe

2017-12-01

The multiplication of connected devices goes along with a large variety of applications and traffic types needing diverse requirements. Accompanying this connectivity evolution, the last years have seen considerable evolutions of wireless communication standards in the domain of mobile telephone networks, local/wide wireless area networks, and Digital Video Broadcasting (DVB). In this context, intensive research has been conducted to provide flexible turbo decoder targeting high throughput, multi-mode, multi-standard, and power consumption efficiency. However, flexible turbo decoder implementations have not often considered dynamic reconfiguration issues in this context that requires high speed configuration switching. Starting from this assessment, this paper proposes the first solution that allows frame-by-frame run-time configuration management of a multi-processor turbo decoder without compromising the decoding performances.

Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

PubMed Central

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-01-01

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

PubMed

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-10-30

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

Synchronization of Lienard-Type Oscillators in Uniform Electrical Networks

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

Sinha, Mohit; Dorfler, Florian; Johnson, Brian B.

2016-08-01

This paper presents a condition for global asymptotic synchronization of Lienard-type nonlinear oscillators in uniform LTI electrical networks with series R-L circuits modeling interconnections. By uniform electrical networks, we mean that the per-unit-length impedances are identical for the interconnecting lines. We derive conditions for global asymptotic synchronization for a particular feedback architecture where the derivative of the oscillator output current supplements the innate current feedback induced by simply interconnecting the oscillator to the network. Our proof leverages a coordinate transformation to a set of differential coordinates that emphasizes signal differences and the particular form of feedback permits the formulation ofmore » a quadratic Lyapunov function for this class of networks. This approach is particularly interesting since synchronization conditions are difficult to obtain by means of quadratic Lyapunov functions when only current feedback is used and for networks composed of series R-L circuits. Our synchronization condition depends on the algebraic connectivity of the underlying network, and reiterates the conventional wisdom from Lyapunov- and passivity-based arguments that strong coupling is required to ensure synchronization.« less

A network approach for researching partnerships in health

PubMed Central

Lewis, Jenny M

2005-01-01

Background The last decade has witnessed a significant move towards new modes of governing that are based on coordination and collaboration. In particular, local level partnerships have been widely introduced around the world. There are few comprehensive approaches for researching the effects of these partnerships. The aim of this paper is to outline a network approach that combines structure and agency based explanations to research partnerships in health. Network research based on two Primary Care Partnerships (PCPs) in Victoria is used to demonstrate the utility of this approach. The paper examines multiple types of ties between people (structure), and the use and value of relationships to partners (agency), using interviews with the people involved in two PCPs – one in metropolitan Melbourne and one in a rural area. Results Network maps of ties based on work, strategic information and policy advice, show that there are many strong connections in both PCPs. Not surprisingly, PCP staff are central and highly connected. Of more interest are the ties that are dependent on these dedicated partnership staff, as they reveal which actors become weakly linked or disconnected without them. Network measures indicate that work ties are the most dispersed and strategic information ties are the most concentrated around fewer people. Divisions of general practice are weakly linked, while local government officials and Department of Human Services (DHS) regional staff appear to play important bridging roles. Finally, the relationships between partners have changed and improved, and most of those interviewed value their new or improved links with partners. Conclusion Improving service coordination and health promotion planning requires engaging people and building strong relationships. Mapping ties is a useful means for assessing the strengths and weaknesses of partnerships, and network analysis indicates concentration and dispersion, the importance of particular individuals

A network approach for researching partnerships in health.

PubMed

Lewis, Jenny M

2005-10-07

The last decade has witnessed a significant move towards new modes of governing that are based on coordination and collaboration. In particular, local level partnerships have been widely introduced around the world. There are few comprehensive approaches for researching the effects of these partnerships. The aim of this paper is to outline a network approach that combines structure and agency based explanations to research partnerships in health. Network research based on two Primary Care Partnerships (PCPs) in Victoria is used to demonstrate the utility of this approach. The paper examines multiple types of ties between people (structure), and the use and value of relationships to partners (agency), using interviews with the people involved in two PCPs--one in metropolitan Melbourne and one in a rural area. Network maps of ties based on work, strategic information and policy advice, show that there are many strong connections in both PCPs. Not surprisingly, PCP staff are central and highly connected. Of more interest are the ties that are dependent on these dedicated partnership staff, as they reveal which actors become weakly linked or disconnected without them. Network measures indicate that work ties are the most dispersed and strategic information ties are the most concentrated around fewer people. Divisions of general practice are weakly linked, while local government officials and Department of Human Services (DHS) regional staff appear to play important bridging roles. Finally, the relationships between partners have changed and improved, and most of those interviewed value their new or improved links with partners. Improving service coordination and health promotion planning requires engaging people and building strong relationships. Mapping ties is a useful means for assessing the strengths and weaknesses of partnerships, and network analysis indicates concentration and dispersion, the importance of particular individuals, and the points at which they

A multi-objective approach to solid waste management.

PubMed

Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

2010-01-01

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy). 2010 Elsevier Ltd. All rights reserved.

A multi-objective approach to solid waste management

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

Galante, Giacomo, E-mail: galante@dtpm.unipa.i; Aiello, Giuseppe; Enea, Mario

2010-08-15

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached inmore » a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy).« less

Competitive dynamics of lexical innovations in multi-layer networks

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto

2014-04-01

We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models.

PubMed

Pope, Ronald; Wu, Jianguo

2014-06-01

In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O3) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs' objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O3 was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O3, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O3 monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the

Pre-configured polyhedron based protection against multi-link failures in optical mesh networks.

PubMed

Huang, Shanguo; Guo, Bingli; Li, Xin; Zhang, Jie; Zhao, Yongli; Gu, Wanyi

2014-02-10

This paper focuses on random multi-link failures protection in optical mesh networks, instead of single, the dual or sequential failures of previous studies. Spare resource efficiency and failure robustness are major concerns in link protection strategy designing and a k-regular and k-edge connected structure is proved to be one of the optimal solutions for link protection network. Based on this, a novel pre-configured polyhedron based protection structure is proposed, and it could provide protection for both simultaneous and sequential random link failures with improved spare resource efficiency. Its performance is evaluated in terms of spare resource consumption, recovery rate and average recovery path length, as well as compared with ring based and subgraph protection under probabilistic link failure scenarios. Results show the proposed novel link protection approach has better performance than previous works.

Experimental Design for Multi-drug Combination Studies Using Signaling Networks

PubMed Central

Huang, Hengzhen; Fang, Hong-Bin; Tan, Ming T.

2017-01-01

Summary Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this paper. The simulation studies show our proposed methods perform well. PMID:28960231

A multi-resolution approach to electromagnetic modeling.

NASA Astrophysics Data System (ADS)

Cherevatova, M.; Egbert, G. D.; Smirnov, M. Yu

2018-04-01

We present a multi-resolution approach for three-dimensional magnetotelluric forward modeling. Our approach is motivated by the fact that fine grid resolution is typically required at shallow levels to adequately represent near surface inhomogeneities, topography, and bathymetry, while a much coarser grid may be adequate at depth where the diffusively propagating electromagnetic fields are much smoother. This is especially true for forward modeling required in regularized inversion, where conductivity variations at depth are generally very smooth. With a conventional structured finite-difference grid the fine discretization required to adequately represent rapid variations near the surface are continued to all depths, resulting in higher computational costs. Increasing the computational efficiency of the forward modeling is especially important for solving regularized inversion problems. We implement a multi-resolution finite-difference scheme that allows us to decrease the horizontal grid resolution with depth, as is done with vertical discretization. In our implementation, the multi-resolution grid is represented as a vertical stack of sub-grids, with each sub-grid being a standard Cartesian tensor product staggered grid. Thus, our approach is similar to the octree discretization previously used for electromagnetic modeling, but simpler in that we allow refinement only with depth. The major difficulty arose in deriving the forward modeling operators on interfaces between adjacent sub-grids. We considered three ways of handling the interface layers and suggest a preferable one, which results in similar accuracy as the staggered grid solution, while retaining the symmetry of coefficient matrix. A comparison between multi-resolution and staggered solvers for various models show that multi-resolution approach improves on computational efficiency without compromising the accuracy of the solution.

A Framework for Supporting Survivability, Network Planning and Cross-Layer Optimization in Future Multi-Domain Terabit Networks

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

Baldin, Ilya; Huang, Shu; Gopidi, Rajesh

This final project report describes the accomplishments, products and publications from the award. It includes the overview of the project goals to devise a framework for managing resources in multi-domain, multi-layer networks, as well the details of the mathematical problem formulation and the description of the prototype built to prove the concept.

A novel multi-scale adaptive sampling-based approach for energy saving in leak detection for WSN-based water pipelines

NASA Astrophysics Data System (ADS)

Saqib, Najam us; Faizan Mysorewala, Muhammad; Cheded, Lahouari

2017-12-01

In this paper, we propose a novel monitoring strategy for a wireless sensor networks (WSNs)-based water pipeline network. Our strategy uses a multi-pronged approach to reduce energy consumption based on the use of two types of vibration sensors and pressure sensors, all having different energy levels, and a hierarchical adaptive sampling mechanism to determine the sampling frequency. The sampling rate of the sensors is adjusted according to the bandwidth of the vibration signal being monitored by using a wavelet-based adaptive thresholding scheme that calculates the new sampling frequency for the following cycle. In this multimodal sensing scheme, the duty-cycling approach is used for all sensors to reduce the sampling instances, such that the high-energy, high-precision (HE-HP) vibration sensors have low duty cycles, and the low-energy, low-precision (LE-LP) vibration sensors have high duty cycles. The low duty-cycling (HE-HP) vibration sensor adjusts the sampling frequency of the high duty-cycling (LE-LP) vibration sensor. The simulated test bed considered here consists of a water pipeline network which uses pressure and vibration sensors, with the latter having different energy consumptions and precision levels, at various locations in the network. This is all the more useful for energy conservation for extended monitoring. It is shown that by using the novel features of our proposed scheme, a significant reduction in energy consumption is achieved and the leak is effectively detected by the sensor node that is closest to it. Finally, both the total energy consumed by monitoring as well as the time to detect the leak by a WSN node are computed, and show the superiority of our proposed hierarchical adaptive sampling algorithm over a non-adaptive sampling approach.

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

PubMed

Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

2017-08-10

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

Multi-load Groups Coordinated Load Control Strategy Considering Power Network Constraints

NASA Astrophysics Data System (ADS)

Liu, Meng; Zhao, Binchao; Wang, Jun; Zhang, Guohui; Wang, Xin

2017-05-01

Loads with energy storage property can actively participate in power balance for power systems, this paper takes air conditioner as a controllable load example, proposing a multi-load groups coordinated load control strategy considering power network constraints. Firstly, two load control modes considering recovery of load diversity are designed, blocking power oscillation of aggregated air conditioners. As the same time, air conditioner temperature setpoint recovery control strategy is presented to avoid power recovery peak. Considering inherent characteristics of two load control modes, an coordinated load control mode is designed by combining the both. Basing on this, a multi-load groups coordinated load control strategy is proposed. During the implementing of load control, power network constraints should be satisfied. An indice which can reflect the security of power system operating is defined. By minimizing its value through optimization, the change of air conditioning loads’ aggregated power on each load bus can be calculated. Simulations are conducted on an air conditioners group and New England 10-generator 39-bus system, verifying the effectiveness of the proposed multi-load groups coordinated load control strategy considering power network constraints.

Process-in-Network: A Comprehensive Network Processing Approach

PubMed Central

Urzaiz, Gabriel; Villa, David; Villanueva, Felix; Lopez, Juan Carlos

2012-01-01

A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network. PMID:22969390

PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter.

PubMed

Li, Xiaohua; Li, Yaan; Yu, Jing; Chen, Xiao; Dai, Miao

2015-11-06

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.

Examining Food Risk in the Large using a Complex, Networked System-of-sytems Approach

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

Ambrosiano, John; Newkirk, Ryan; Mc Donald, Mark P

2010-12-03

The food production infrastructure is a highly complex system of systems. Characterizing the risks of intentional contamination in multi-ingredient manufactured foods is extremely challenging because the risks depend on the vulnerabilities of food processing facilities and on the intricacies of the supply-distribution networks that link them. A pure engineering approach to modeling the system is impractical because of the overall system complexity and paucity of data. A methodology is needed to assess food contamination risk 'in the large', based on current, high-level information about manufacturing facilities, corrunodities and markets, that will indicate which food categories are most at risk ofmore » intentional contamination and warrant deeper analysis. The approach begins by decomposing the system for producing a multi-ingredient food into instances of two subsystem archetypes: (1) the relevant manufacturing and processing facilities, and (2) the networked corrunodity flows that link them to each other and consumers. Ingredient manufacturing subsystems are modeled as generic systems dynamics models with distributions of key parameters that span the configurations of real facilities. Networks representing the distribution systems are synthesized from general information about food corrunodities. This is done in a series of steps. First, probability networks representing the aggregated flows of food from manufacturers to wholesalers, retailers, other manufacturers, and direct consumers are inferred from high-level approximate information. This is followed by disaggregation of the general flows into flows connecting 'large' and 'small' categories of manufacturers, wholesalers, retailers, and consumers. Optimization methods are then used to determine the most likely network flows consistent with given data. Vulnerability can be assessed for a potential contamination point using a modified CARVER + Shock model. Once the facility and corrunodity flow models are

Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

PubMed Central

Wu, Kai; Liu, Jing; Wang, Shuai

2016-01-01

Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy. PMID:27886244

Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

NASA Astrophysics Data System (ADS)

Wu, Kai; Liu, Jing; Wang, Shuai

2016-11-01

Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy.

Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization.

PubMed

Nenning, Karl-Heinz; Kollndorfer, Kathrin; Schöpf, Veronika; Prayer, Daniela; Langs, Georg

2015-01-01

Functional magnetic resonance imaging group studies rely on the ability to establish correspondence across individuals. This enables location specific comparison of functional brain characteristics. Registration is often based on morphology and does not take variability of functional localization into account. This can lead to a loss of specificity, or confounds when studying diseases. In this paper we propose multi-subject functional registration by manifold alignment via coupled joint diagonalization. The functional network structure of each subject is encoded in a diffusion map, where functional relationships are decoupled from spatial position. Two-step manifold alignment estimates initial correspondences between functionally equivalent regions. Then, coupled joint diagonalization establishes common eigenbases across all individuals, and refines the functional correspondences. We evaluate our approach on fMRI data acquired during a language paradigm. Experiments demonstrate the benefits in matching accuracy achieved by coupled joint diagonalization compared to previously proposed functional alignment approaches, or alignment based on structural correspondences.

Multi-Cultural Approaches to Art Criticism.

ERIC Educational Resources Information Center

Congdon, Kristin G.

1989-01-01

Discusses the reasons for the development and inclusion of multiple art criticism formats in the curriculum. Points out the ways in which a variety of world views approach can heighten the appreciation of art. Suggests that a multi-cultural approach will enrich the lives of all students in a pluralistic society. (KO)

Impact of different dietary approaches on glycemic control and cardiovascular risk factors in patients with type 2 diabetes: a protocol for a systematic review and network meta-analysis.

PubMed

Schwingshackl, Lukas; Chaimani, Anna; Hoffmann, Georg; Schwedhelm, Carolina; Boeing, Heiner

2017-03-20

Dietary advice is one of the cornerstones in the management of type 2 diabetes mellitus. The American Diabetes Association recommended a hypocaloric diet for overweight or obese adults with type 2 diabetes in order to induce weight loss. However, there is limited evidence on the optimal approaches to control hyperglycemia in type 2 diabetes patients. The aim of the present study is to assess the comparative efficacy of different dietary approaches on glycemic control and blood lipids in patients with type 2 diabetes mellitus in a systematic review including a standard pairwise and network meta-analysis of randomized trials. We will conduct searches in Cochrane Central Register of Controlled Trials (CENTRAL) on the Cochrane Library, PubMed (from 1966), and Google Scholar. Citations, abstracts, and relevant papers will be screened for eligibility by two reviewers independently. Randomized controlled trials (with a control group or randomized trials with at least two intervention groups) will be included if they meet the following criteria: (1) include type 2 diabetes mellitus, (2) include patients aged ≥18 years, (3) include dietary intervention (different type of diets: e.g., Mediterranean dietary pattern, low-carbohydrate diet, low-fat diet, vegetarian diet, high protein diet); either hypo, iso-caloric, or ad libitum diets, (4) minimum intervention period of 12 weeks. For each outcome measure of interest, random effects pairwise and network meta-analyses will be performed in order to determine the pooled relative effect of each intervention relative to every other intervention in terms of the post-intervention values (or mean differences between the changes from baseline value scores). Subgroup analyses are planned for study length, sample size, age, and sex. This systematic review will synthesize the available evidence on the comparative efficacy of different dietary approaches in the management of glycosylated hemoglobin (primary outcome), fasting glucose

Exact outage analysis of the effect of co-channel interference on secured multi-hop relaying networks

NASA Astrophysics Data System (ADS)

Quang Nguyen, Sang; Kong, Hyung Yun

2016-11-01

In this article, the presence of multi-hop relaying, eavesdropper and co-channel interference (CCI) in the same system model is investigated. Specifically, the effect of CCI on a secured multi-hop relaying network is studied, in which the source communicates with the destination via multi-relay-hopping under the presence of an eavesdropper and CCI at each node. The optimal relay at each cluster is selected to help forward the message from the source to the destination. We apply two relay selection approaches to such a system model, i.e. the optimal relay is chosen based on (1) the maximum channel gain from the transmitter to all relays in the desired cluster and (2) the minimum channel gain from the eavesdropper to all relays in each cluster. For the performance evaluation and comparison, we derived the exact closed form of the secrecy outage probability of the two approaches. That analysis is verified by Monte Carlo simulation. Finally, the effects of the number of hops, the transmit power at the source, relays and the external sources, the distance between the external sources and each node in the system, and the location of the eavesdropper are presented and discussed.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.

PubMed

Babaei, Sepideh; Hulsman, Marc; Reinders, Marcel; de Ridder, Jeroen

2013-01-23

Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

Multi-Criteria Approach in Multifunctional Building Design Process

NASA Astrophysics Data System (ADS)

Gerigk, Mateusz

2017-10-01

The paper presents new approach in multifunctional building design process. Publication defines problems related to the design of complex multifunctional buildings. Currently, contemporary urban areas are characterized by very intensive use of space. Today, buildings are being built bigger and contain more diverse functions to meet the needs of a large number of users in one capacity. The trends show the need for recognition of design objects in an organized structure, which must meet current design criteria. The design process in terms of the complex system is a theoretical model, which is the basis for optimization solutions for the entire life cycle of the building. From the concept phase through exploitation phase to disposal phase multipurpose spaces should guarantee aesthetics, functionality, system efficiency, system safety and environmental protection in the best possible way. The result of the analysis of the design process is presented as a theoretical model of the multifunctional structure. Recognition of multi-criteria model in the form of Cartesian product allows to create a holistic representation of the designed building in the form of a graph model. The proposed network is the theoretical base that can be used in the design process of complex engineering systems. The systematic multi-criteria approach makes possible to maintain control over the entire design process and to provide the best possible performance. With respect to current design requirements, there are no established design rules for multifunctional buildings in relation to their operating phase. Enrichment of the basic criteria with functional flexibility criterion makes it possible to extend the exploitation phase which brings advantages on many levels.

Detection of multiple perturbations in multi-omics biological networks.

PubMed

Griffin, Paula J; Zhang, Yuqing; Johnson, William Evan; Kolaczyk, Eric D

2018-05-17

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA). © 2018, The International Biometric Society.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for

A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu

Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

NASA Astrophysics Data System (ADS)

Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Amar, Chokri Ben

2016-12-01

This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

A Data-Based Approach to Discovering Multi-Topic Influential Leaders.

PubMed

Tang, Xing; Miao, Qiguang; Yu, Shangshang; Quan, Yining

2016-01-01

Recently, increasing numbers of users have adopted microblogging services as their main information source. However, most of them find themselves drowning in the millions of posts produced by other users every day. To cope with this, identifying a set of the most influential people is paramount. Moreover, finding a set of related influential users to expand the coverage of one particular topic is required in real world scenarios. Most of the existing algorithms in this area focus on topology-related methods such as PageRank. These methods mine link structures to find the expected influential rank of users. However, because they ignore the interaction data, these methods turn out to be less effective in social networks. In reality, a variety of topics exist within the information diffusing through the network. Because they have different interests, users play different roles in the diffusion of information related to different topics. As a result, distinguishing influential leaders according to different topics is also worthy of research. In this paper, we propose a multi-topic influence diffusion model (MTID) based on traces acquired from historic information. We decompose the influential scores of users into two parts: the direct influence determined by information propagation along the link structure and indirect influence that extends beyond the restrictions of direct follower relationships. To model the network from a multi-topical viewpoint, we introduce topic pools, each of which represents a particular topic information source. Then, we extract the topic distributions from the traces of tweets, determining the influence propagation probability and content generation probability. In the network, we adopt multiple ground nodes representing topic pools to connect every user through bidirectional links. Based on this multi-topical view of the network, we further introduce the topic-dependent rank (TD-Rank) algorithm to identify the multi-topic influential users

A Data-Based Approach to Discovering Multi-Topic Influential Leaders

PubMed Central

Tang, Xing; Miao, Qiguang; Yu, Shangshang; Quan, Yining

2016-01-01

Recently, increasing numbers of users have adopted microblogging services as their main information source. However, most of them find themselves drowning in the millions of posts produced by other users every day. To cope with this, identifying a set of the most influential people is paramount. Moreover, finding a set of related influential users to expand the coverage of one particular topic is required in real world scenarios. Most of the existing algorithms in this area focus on topology-related methods such as PageRank. These methods mine link structures to find the expected influential rank of users. However, because they ignore the interaction data, these methods turn out to be less effective in social networks. In reality, a variety of topics exist within the information diffusing through the network. Because they have different interests, users play different roles in the diffusion of information related to different topics. As a result, distinguishing influential leaders according to different topics is also worthy of research. In this paper, we propose a multi-topic influence diffusion model (MTID) based on traces acquired from historic information. We decompose the influential scores of users into two parts: the direct influence determined by information propagation along the link structure and indirect influence that extends beyond the restrictions of direct follower relationships. To model the network from a multi-topical viewpoint, we introduce topic pools, each of which represents a particular topic information source. Then, we extract the topic distributions from the traces of tweets, determining the influence propagation probability and content generation probability. In the network, we adopt multiple ground nodes representing topic pools to connect every user through bidirectional links. Based on this multi-topical view of the network, we further introduce the topic-dependent rank (TD-Rank) algorithm to identify the multi-topic influential users

Using multi-class queuing network to solve performance models of e-business sites.

PubMed

Zheng, Xiao-ying; Chen, De-ren

2004-01-01

Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

A neural network based computational model to predict the output power of different types of photovoltaic cells.

PubMed

Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

2017-01-01

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

Functional modules by relating protein interaction networks and gene expression.

PubMed

Tornow, Sabine; Mewes, H W

2003-11-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

Functional modules by relating protein interaction networks and gene expression

PubMed Central

Tornow, Sabine; Mewes, H. W.

2003-01-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships. PMID:14576317

A multi-disciplinary approach for the integrated assessment of water alterations under climate change

NASA Astrophysics Data System (ADS)

Sperotto, Anna; Torresan, Silvia; Molina, Jose Luis; Pulido Velazquez, Manuel; Critto, Andrea; Marcomini, Antonio

2017-04-01

Understanding the co-evolution and interrelations between natural and human pressures on water systems is required to ensure a sustainable management of resources under uncertain climate change conditions. To pursue multi-disciplinary research is therefore necessary to consider the multiplicity of stressors affecting water resources, take into account alternative perspectives (i.e. social, economic and environmental objective and priorities) and deal with uncertainty which characterize climate change scenarios. However, approaches commonly adopted in water quality assessment are predominantly mono-disciplinary, single-stressors oriented and apply concepts and models specific of different academic disciplines (e.g. physics, hydrology, ecology, sociology, economy) which, in fact, seldom shed their conceptual blinders failing to provide truly integrated results. In this context, the paper discusses the benefits and limits of adopting a multi-disciplinary approach where different knowledge domains collaborate and quantitative and qualitative information, coming from multiple conceptual and model-based research, are integrated in a harmonic manner. Specifically, Bayesian Networks are used as meta-modelling tool for structuring and combining the probabilistic information available in existing hydrological models, climate change and land use projections, historical observations and expert opinion. The developed network allows to perform a stochastic multi-risk assessment considering the interlacing between climate (i.e. irregularities in water regime) and land use changes (i.e. agriculture, urbanization) and their cascading impacts on water quality parameters (i.e. nutrients loadings). Main objective of the model is the development of multi-risk scenarios to assess and communicate the probability of not meeting a "Good chemical water status" over future timeframe taking into account projected climatic and not climatic conditions. The outcomes are finally used to identify

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

PubMed

Wei, Yawei; Venayagamoorthy, Ganesh Kumar

2017-09-01

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uncoordinated MAC for Adaptive Multi Beam Directional Networks: Analysis and Evaluation

DTIC Science & Technology

2016-08-01

control (MAC) policies for emerging systems that are equipped with fully digital antenna arrays which are capable of adaptive multi-beam directional...Adaptive Beam- forming, Multibeam, Directional Networking, Random Access, Smart Antennas I. INTRODUCTION Fully digital beamforming antenna arrays that...are capable of adaptive multi-beam communications are quickly becoming a reality. These antenna arrays allow users to form multiple simultaneous

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

PubMed

Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

Social Network Types and Mental Health Among LGBT Older Adults

PubMed Central

Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I.; Bryan, Amanda E. B.; Muraco, Anna

2017-01-01

Purpose of the Study: This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. Design and Methods: We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. Results: We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Implications: Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. PMID:28087798

Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches.

PubMed

Zounemat-Kermani, Mohammad; Ramezani-Charmahineh, Abdollah; Adamowski, Jan; Kisi, Ozgur

2018-06-13

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

Combined inverse-forward artificial neural networks for fast and accurate estimation of the diffusion coefficients of cartilage based on multi-physics models.

PubMed

Arbabi, Vahid; Pouran, Behdad; Weinans, Harrie; Zadpoor, Amir A

2016-09-06

Analytical and numerical methods have been used to extract essential engineering parameters such as elastic modulus, Poisson׳s ratio, permeability and diffusion coefficient from experimental data in various types of biological tissues. The major limitation associated with analytical techniques is that they are often only applicable to problems with simplified assumptions. Numerical multi-physics methods, on the other hand, enable minimizing the simplified assumptions but require substantial computational expertise, which is not always available. In this paper, we propose a novel approach that combines inverse and forward artificial neural networks (ANNs) which enables fast and accurate estimation of the diffusion coefficient of cartilage without any need for computational modeling. In this approach, an inverse ANN is trained using our multi-zone biphasic-solute finite-bath computational model of diffusion in cartilage to estimate the diffusion coefficient of the various zones of cartilage given the concentration-time curves. Robust estimation of the diffusion coefficients, however, requires introducing certain levels of stochastic variations during the training process. Determining the required level of stochastic variation is performed by coupling the inverse ANN with a forward ANN that receives the diffusion coefficient as input and returns the concentration-time curve as output. Combined together, forward-inverse ANNs enable computationally inexperienced users to obtain accurate and fast estimation of the diffusion coefficients of cartilage zones. The diffusion coefficients estimated using the proposed approach are compared with those determined using direct scanning of the parameter space as the optimization approach. It has been shown that both approaches yield comparable results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Proteolytic crosstalk in multi-protease networks

NASA Astrophysics Data System (ADS)

Ogle, Curtis T.; Mather, William H.

2016-04-01

Processive proteases, such as ClpXP in E. coli, are conserved enzyme assemblies that can recognize and rapidly degrade proteins. These proteases are used for a number of purposes, including degrading mistranslated proteins and controlling cellular stress response. However, proteolytic machinery within the cell is limited in capacity and can lead to a bottleneck in protein degradation, whereby many proteins compete (‘queue’) for proteolytic resources. Previous work has demonstrated that such queueing can lead to pronounced statistical relationships between different protein counts when proteins compete for a single common protease. However, real cells contain many different proteases, e.g. ClpXP, ClpAP, and Lon in E. coli, and it is not clear how competition between proteins for multiple classes of protease would influence the dynamics of cellular networks. In the present work, we theoretically demonstrate that a multi-protease proteolytic bottleneck can substantially couple the dynamics for both simple and complex (oscillatory) networks, even between substrates with substantially different affinities for protease. For these networks, queueing often leads to strong positive correlations between protein counts, and these correlations are strongest near the queueing theoretic point of balance. Furthermore, we find that the qualitative behavior of these networks depends on the relative size of the absolute affinity of substrate to protease compared to the cross affinity of substrate to protease, leading in certain regimes to priority queue statistics.

Multi-mode clustering model for hierarchical wireless sensor networks

NASA Astrophysics Data System (ADS)

Hu, Xiangdong; Li, Yongfu; Xu, Huifen

2017-03-01

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

A multi-disciplinary approach for the integrated assessment of multiple risks in delta areas.

NASA Astrophysics Data System (ADS)

Sperotto, Anna; Torresan, Silvia; Critto, Andrea; Marcomini, Antonio

2016-04-01

The assessment of climate change related risks is notoriously difficult due to the complex and uncertain combinations of hazardous events that might happen, the multiplicity of physical processes involved, the continuous changes and interactions of environmental and socio-economic systems. One important challenge lies in predicting and modelling cascades of natural and man -made hazard events which can be triggered by climate change, encompassing different spatial and temporal scales. Another regard the potentially difficult integration of environmental, social and economic disciplines in the multi-risk concept. Finally, the effective interaction between scientists and stakeholders is essential to ensure that multi-risk knowledge is translated into efficient adaptation and management strategies. The assessment is even more complex at the scale of deltaic systems which are particularly vulnerable to global environmental changes, due to the fragile equilibrium between the presence of valuable natural ecosystems and relevant economic activities. Improving our capacity to assess the combined effects of multiple hazards (e.g. sea-level rise, storm surges, reduction in sediment load, local subsidence, saltwater intrusion) is therefore essential to identify timely opportunities for adaptation. A holistic multi-risk approach is here proposed to integrate terminology, metrics and methodologies from different research fields (i.e. environmental, social and economic sciences) thus creating shared knowledge areas to advance multi risk assessment and management in delta regions. A first testing of the approach, including the application of Bayesian network analysis for the assessment of impacts of climate change on key natural systems (e.g. wetlands, protected areas, beaches) and socio-economic activities (e.g. agriculture, tourism), is applied in the Po river delta in Northern Italy. The approach is based on a bottom-up process involving local stakeholders early in different

Virtual optical network mapping and core allocation in elastic optical networks using multi-core fibers

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-11-01

Virtualization technology can greatly improve the efficiency of the networks by allowing the virtual optical networks to share the resources of the physical networks. However, it will face some challenges, such as finding the efficient strategies for virtual nodes mapping, virtual links mapping and spectrum assignment. It is even more complex and challenging when the physical elastic optical networks using multi-core fibers. To tackle these challenges, we establish a constrained optimization model to determine the optimal schemes of optical network mapping, core allocation and spectrum assignment. To solve the model efficiently, tailor-made encoding scheme, crossover and mutation operators are designed. Based on these, an efficient genetic algorithm is proposed to obtain the optimal schemes of the virtual nodes mapping, virtual links mapping, core allocation. The simulation experiments are conducted on three widely used networks, and the experimental results show the effectiveness of the proposed model and algorithm.

An analysis of multi-type relational interactions in FMA using graph motifs with disjointness constraints.

PubMed

Zhang, Guo-Qiang; Luo, Lingyun; Ogbuji, Chime; Joslyn, Cliff; Mejino, Jose; Sahoo, Satya S

2012-01-01

The interaction of multiple types of relationships among anatomical classes in the Foundational Model of Anatomy (FMA) can provide inferred information valuable for quality assurance. This paper introduces a method called Motif Checking (MOCH) to study the effects of such multi-relation type interactions for detecting logical inconsistencies as well as other anomalies represented by the motifs. MOCH represents patterns of multi-type interaction as small labeled (with multiple types of edges) sub-graph motifs, whose nodes represent class variables, and labeled edges represent relational types. By representing FMA as an RDF graph and motifs as SPARQL queries, fragments of FMA are automatically obtained as auditing candidates. Leveraging the scalability and reconfigurability of Semantic Web Technology, we performed exhaustive analyses of a variety of labeled sub-graph motifs. The quality assurance feature of MOCH comes from the distinct use of a subset of the edges of the graph motifs as constraints for disjointness, whereby bringing in rule-based flavor to the approach as well. With possible disjointness implied by antonyms, we performed manual inspection of the resulting FMA fragments and tracked down sources of abnormal inferred conclusions (logical inconsistencies), which are amendable for programmatic revision of the FMA. Our results demonstrate that MOCH provides a unique source of valuable information for quality assurance. Since our approach is general, it is applicable to any ontological system with an OWL representation.

An Analysis of Multi-type Relational Interactions in FMA Using Graph Motifs with Disjointness Constraints

PubMed Central

Zhang, Guo-Qiang; Luo, Lingyun; Ogbuji, Chime; Joslyn, Cliff; Mejino, Jose; Sahoo, Satya S

2012-01-01

The interaction of multiple types of relationships among anatomical classes in the Foundational Model of Anatomy (FMA) can provide inferred information valuable for quality assurance. This paper introduces a method called Motif Checking (MOCH) to study the effects of such multi-relation type interactions for detecting logical inconsistencies as well as other anomalies represented by the motifs. MOCH represents patterns of multi-type interaction as small labeled (with multiple types of edges) sub-graph motifs, whose nodes represent class variables, and labeled edges represent relational types. By representing FMA as an RDF graph and motifs as SPARQL queries, fragments of FMA are automatically obtained as auditing candidates. Leveraging the scalability and reconfigurability of Semantic Web Technology, we performed exhaustive analyses of a variety of labeled sub-graph motifs. The quality assurance feature of MOCH comes from the distinct use of a subset of the edges of the graph motifs as constraints for disjointness, whereby bringing in rule-based flavor to the approach as well. With possible disjointness implied by antonyms, we performed manual inspection of the resulting FMA fragments and tracked down sources of abnormal inferred conclusions (logical inconsistencies), which are amendable for programmatic revision of the FMA. Our results demonstrate that MOCH provides a unique source of valuable information for quality assurance. Since our approach is general, it is applicable to any ontological system with an OWL representation. PMID:23304382

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model.

PubMed

Mei, Shuang; Wang, Yudan; Wen, Guojun

2018-04-02

Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

Social Network Types and Mental Health Among LGBT Older Adults.

PubMed

Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I; Bryan, Amanda E B; Muraco, Anna

2017-02-01

This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Mathematical model of snake-type multi-directional wave generation

NASA Astrophysics Data System (ADS)

Muarif; Halfiani, Vera; Rusdiana, Siti; Munzir, Said; Ramli, Marwan

2018-01-01

Research on extreme wave generation is one intensive research on water wave study because the fact that the occurrence of this wave in the ocean can cause serious damage to the ships and offshore structures. One method to be used to generate the wave is self-correcting. This method controls the signal on the wavemakers in a wave tank. Some studies also consider the nonlinear wave generation in a wave tank by using numerical approach. Study on wave generation is essential in the effectiveness and efficiency of offshore structure model testing before it can be operated in the ocean. Generally, there are two types of wavemakers implemented in the hydrodynamic laboratory, piston-type and flap-type. The flap-type is preferred to conduct a testing to a ship in deep water. Single flap wavemaker has been explained in many studies yet snake-type wavemaker (has more than one flap) is still a case needed to be examined. Hence, the formulation in controlling the wavemaker need to be precisely analyzed such that the given input can generate the desired wave in the space-limited wave tank. By applying the same analogy and methodhology as the previous study, this article represents multi-directional wave generation by implementing snake-type wavemakers.

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

PubMed Central

Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

2013-01-01

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

Causal functional contributions and interactions in the attention network of the brain: an objective multi-perturbation analysis.

PubMed

Zavaglia, Melissa; Hilgetag, Claus C

2016-06-01

Spatial attention is a prime example for the distributed network functions of the brain. Lesion studies in animal models have been used to investigate intact attentional mechanisms as well as perspectives for rehabilitation in the injured brain. Here, we systematically analyzed behavioral data from cooling deactivation and permanent lesion experiments in the cat, where unilateral deactivation of the posterior parietal cortex (in the vicinity of the posterior middle suprasylvian cortex, pMS) or the superior colliculus (SC) cause a severe neglect in the contralateral hemifield. Counterintuitively, additional deactivation of structures in the opposite hemisphere reverses the deficit. Using such lesion data, we employed a game-theoretical approach, multi-perturbation Shapley value analysis (MSA), for inferring functional contributions and network interactions of bilateral pMS and SC from behavioral performance in visual attention studies. The approach provides an objective theoretical strategy for lesion inferences and allows a unique quantitative characterization of regional functional contributions and interactions on the basis of multi-perturbations. The quantitative analysis demonstrated that right posterior parietal cortex and superior colliculus made the strongest positive contributions to left-field orienting, while left brain regions had negative contributions, implying that their perturbation may reverse the effects of contralateral lesions or improve normal function. An analysis of functional modulations and interactions among the regions revealed redundant interactions (implying functional overlap) between regions within each hemisphere, and synergistic interactions between bilateral regions. To assess the reliability of the MSA method in the face of variable and incomplete input data, we performed a sensitivity analysis, investigating how much the contribution values of the four regions depended on the performance of specific configurations and on the

Type-I cosmic-string network

NASA Astrophysics Data System (ADS)

Hiramatsu, Takashi; Sendouda, Yuuiti; Takahashi, Keitaro; Yamauchi, Daisuke; Yoo, Chul-Moon

2013-10-01

We study the network of Type-I cosmic strings using the field-theoretic numerical simulations in the Abelian-Higgs model. For Type-I strings, the gauge field plays an important role, and thus we find that the correlation length of the strings is strongly dependent upon the parameter β, the ratio between the masses of the scalar field and the gauge field, namely, β=mφ2/mA2. In particular, if we take the cosmic expansion into account, the network becomes densest in the comoving box for a specific value of β for β<1.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function

Unravelling Darwin's entangled bank: architecture and robustness of mutualistic networks with multiple interaction types.

PubMed

Dáttilo, Wesley; Lara-Rodríguez, Nubia; Jordano, Pedro; Guimarães, Paulo R; Thompson, John N; Marquis, Robert J; Medeiros, Lucas P; Ortiz-Pulido, Raul; Marcos-García, Maria A; Rico-Gray, Victor

2016-11-30

Trying to unravel Darwin's entangled bank further, we describe the architecture of a network involving multiple forms of mutualism (pollination by animals, seed dispersal by birds and plant protection by ants) and evaluate whether this multi-network shows evidence of a structure that promotes robustness. We found that species differed strongly in their contributions to the organization of the multi-interaction network, and that only a few species contributed to the structuring of these patterns. Moreover, we observed that the multi-interaction networks did not enhance community robustness compared with each of the three independent mutualistic networks when analysed across a range of simulated scenarios of species extinction. By simulating the removal of highly interacting species, we observed that, overall, these species enhance network nestedness and robustness, but decrease modularity. We discuss how the organization of interlinked mutualistic networks may be essential for the maintenance of ecological communities, and therefore the long-term ecological and evolutionary dynamics of interactive, species-rich communities. We suggest that conserving these keystone mutualists and their interactions is crucial to the persistence of species-rich mutualistic assemblages, mainly because they support other species and shape the network organization. © 2016 The Author(s).

The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective.

PubMed

Li, Ying Hong; Wang, Pan Pan; Li, Xiao Xu; Yu, Chun Yan; Yang, Hong; Zhou, Jin; Xue, Wei Wei; Tan, Jun; Zhu, Feng

2016-01-01

The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.

Neural network approaches to capture temporal information

NASA Astrophysics Data System (ADS)

van Veelen, Martijn; Nijhuis, Jos; Spaanenburg, Ben

2000-05-01

The automated design and construction of neural networks receives growing attention of the neural networks community. Both the growing availability of computing power and development of mathematical and probabilistic theory have had severe impact on the design and modelling approaches of neural networks. This impact is most apparent in the use of neural networks to time series prediction. In this paper, we give our views on past, contemporary and future design and modelling approaches to neural forecasting.

Traffic placement policies for a multi-band network

NASA Technical Reports Server (NTRS)

Maly, Kurt J.; Foudriat, E. C.; Game, David; Mukkamala, R.; Overstreet, C. Michael

1990-01-01

Recently protocols were introduced that enable the integration of synchronous traffic (voice or video) and asynchronous traffic (data) and extend the size of local area networks without loss in speed or capacity. One of these is DRAMA, a multiband protocol based on broadband technology. It provides dynamic allocation of bandwidth among clusters of nodes in the total network. A number of traffic placement policies for such networks are proposed and evaluated. Metrics used for performance evaluation include average network access delay, degree of fairness of access among the nodes, and network throughput. The feasibility of the DRAMA protocol is established through simulation studies. DRAMA provides effective integration of synchronous and asychronous traffic due to its ability to separate traffic types. Under the suggested traffic placement policies, the DRAMA protocol is shown to handle diverse loads, mixes of traffic types, and numbers of nodes, as well as modifications to the network structure and momentary traffic overloads.

Determining a bisection bandwidth for a multi-node data communications network

DOEpatents

Faraj, Ahmad A.

2010-01-26

Methods, systems, and products are disclosed for determining a bisection bandwidth for a multi-node data communications network that include: partitioning nodes in the network into a first sub-network and a second sub-network in dependence upon a topology of the network; sending, by each node in the first sub-network to a destination node in the second sub-network, a first message having a predetermined message size; receiving, by each node in the first sub-network from a source node in the second sub-network, a second message; measuring, by each node in the first sub-network, the elapsed communications time between the sending of the first message and the receiving of the second message; selecting the longest elapsed communications time; and calculating the bisection bandwidth for the network in dependence upon the number of the nodes in the first sub-network, the predetermined message size of the first test message, and the longest elapsed communications time.

An adaptive neural swarm approach for intrusion defense in ad hoc networks

NASA Astrophysics Data System (ADS)

Cannady, James

2011-06-01

Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

Modeling multi-process connectivity in river deltas: extending the single layer network analysis to a coupled multilayer network framework

NASA Astrophysics Data System (ADS)

Tejedor, A.; Longjas, A.; Foufoula-Georgiou, E.

2017-12-01

Previous work [e.g. Tejedor et al., 2016 - GRL] has demonstrated the potential of using graph theory to study key properties of the structure and dynamics of river delta channel networks. Although the distribution of fluxes in river deltas is mostly driven by the connectivity of its channel network a significant part of the fluxes might also arise from connectivity between the channels and islands due to overland flow and seepage. This channel-island-subsurface interaction creates connectivity pathways which facilitate or inhibit transport depending on their degree of coupling. The question we pose here is how to collectively study system connectivity that emerges from the aggregated action of different processes (different in nature, intensity and time scales). Single-layer graphs as those introduced for delta channel networks are inadequate as they lack the ability to represent coupled processes, and neglecting across-process interactions can lead to mis-representation of the overall system dynamics. We present here a framework that generalizes the traditional representation of networks (single-layer graphs) to the so-called multi-layer networks or multiplex. A multi-layer network conceptualizes the overall connectivity arising from different processes as distinct graphs (layers), while allowing at the same time to represent interactions between layers by introducing interlayer links (across process interactions). We illustrate this framework using a study of the joint connectivity that arises from the coupling of the confined flow on the channel network and the overland flow on islands, on a prototype delta. We show the potential of the multi-layer framework to answer quantitatively questions related to the characteristic time scales to steady-state transport in the system as a whole when different levels of channel-island coupling are modulated by different magnitudes of discharge rates.

A Novel Network Attack Audit System based on Multi-Agent Technology

NASA Astrophysics Data System (ADS)

Jianping, Wang; Min, Chen; Xianwen, Wu

A network attack audit system which includes network attack audit Agent, host audit Agent and management control center audit Agent is proposed. And the improved multi-agent technology is carried out in the network attack audit Agent which has achieved satisfactory audit results. The audit system in terms of network attack is just in-depth, and with the function improvement of network attack audit Agent, different attack will be better analyzed and audit. In addition, the management control center Agent should manage and analyze audit results from AA (or HA) and audit data on time. And the history files of network packets and host log data should also be audit to find deeper violations that cannot be found in real time.

Confidant Network Types and Well-Being Among Older Europeans

PubMed Central

Litwin, Howard; Stoeckel, Kimberly J.

2014-01-01

Purpose of the Study: To derive a typology of confidant networks among older adults in Europe and to examine them in relation to country differences and well-being (CASP-12). Design and Methods: The study population was composed of persons aged 65 and older in 16 countries from the 4th wave of the Survey of Health, Ageing and Retirement in Europe (N = 28,697). K-means cluster analysis was applied to data from a newly implemented name-generating network inventory. CASP-12 scores were regressed on network type controlling for country and potential sociodemographic and health confounders. Results: Six prototypical confidant network types were discerned, including proximal and distal family-based networks of varying configurations, as well as friend-based and other-based network types. Regional country differences in network type constellations were observed. Better well-being was found to be associated with network types with greater social capital. Respondents with no named confidants had the lowest CASP-12 scores, and those embedded in “other” network types also exhibited a negative association with well-being. Implications: The study demonstrates the utility of name-generating network inventories in understanding the social capital of older persons. It also shows that accessible family ties are strong correlates of well-being in this population. Finally, it documents the importance of improving the means to detect the small but significant subgroup of isolated older people—those who have no confidants on whom they may rely. PMID:23749390

A neural network approach to cloud classification

NASA Technical Reports Server (NTRS)

Lee, Jonathan; Weger, Ronald C.; Sengupta, Sailes K.; Welch, Ronald M.

1990-01-01

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.

Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks

NASA Astrophysics Data System (ADS)

Fieuzal, R.; Marais Sicre, C.; Baup, F.

2017-05-01

The yield forecasting of corn constitutes a key issue in agricultural management, particularly in the context of demographic pressure and climate change. This study presents two methods to estimate yields using artificial neural networks: a diagnostic approach based on all the satellite data acquired throughout the agricultural season, and a real-time approach, where estimates are updated after each image was acquired in the microwave and optical domains (Formosat-2, Spot-4/5, TerraSAR-X, and Radarsat-2) throughout the crop cycle. The results are based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO (Centre d'Études de la BIOsphère) laboratory in 2010 over an agricultural region in southwestern France. Among the tested sensor configurations (multi-frequency, multi-polarization or multi-source data), the best yield estimation performance (using the diagnostic approach) is obtained with reflectance acquired in the red wavelength region, with a coefficient of determination of 0.77 and an RMSE of 6.6 q ha-1. In the real-time approach the combination of red reflectance and CHH backscattering coefficients provides the best compromise between the accuracy and earliness of the yield estimate (more than 3 months before the harvest), with an R2 of 0.69 and an RMSE of 7.0 q ha-1 during the development of the central stem. The two best yield estimates are similar in most cases (for more than 80% of the monitored fields), and the differences are related to discrepancies in the crop growth cycle and/or the consequences of pests.

Lung nodule malignancy prediction using multi-task convolutional neural network

NASA Astrophysics Data System (ADS)

Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

2017-03-01

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

mEducator: A Best Practice Network for Repurposing and Sharing Medical Educational Multi-type Content

NASA Astrophysics Data System (ADS)

Bamidis, Panagiotis D.; Kaldoudi, Eleni; Pattichis, Costas

Although there is an abundance of medical educational content available in individual EU academic institutions, this is not widely available or easy to discover and retrieve, due to lack of standardized content sharing mechanisms. The mEducator EU project will face this lack by implementing and experimenting between two different sharing mechanisms, namely, one based one mashup technologies, and one based on semantic web services. In addition, the mEducator best practice network will critically evaluate existing standards and reference models in the field of e-learning in order to enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, repurposed and re-used across European higher academic institutions. Educational content included in mEducator covers and represents the whole range of medical educational content, from traditional instructional teaching to active learning and experiential teaching/studying approaches. It spans the whole range of types, from text to exam sheets, algorithms, teaching files, computer programs (simulators or games) and interactive objects (like virtual patients and electronically traced anatomies), while it covers a variety of topics. In this paper, apart from introducing the relevant project concepts and strategies, emphasis is also placed on the notion of (dynamic) user-generated content, its advantages and peculiarities, as well as, gaps in current research and technology practice upon its embedding into existing standards.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Optimization-based channel constrained data aggregation routing algorithms in multi-radio wireless sensor networks.

PubMed

Yen, Hong-Hsu

2009-01-01

In wireless sensor networks, data aggregation routing could reduce the number of data transmissions so as to achieve energy efficient transmission. However, data aggregation introduces data retransmission that is caused by co-channel interference from neighboring sensor nodes. This kind of co-channel interference could result in extra energy consumption and significant latency from retransmission. This will jeopardize the benefits of data aggregation. One possible solution to circumvent data retransmission caused by co-channel interference is to assign different channels to every sensor node that is within each other's interference range on the data aggregation tree. By associating each radio with a different channel, a sensor node could receive data from all the children nodes on the data aggregation tree simultaneously. This could reduce the latency from the data source nodes back to the sink so as to meet the user's delay QoS. Since the number of radios on each sensor node and the number of non-overlapping channels are all limited resources in wireless sensor networks, a challenging question here is to minimize the total transmission cost under limited number of non-overlapping channels in multi-radio wireless sensor networks. This channel constrained data aggregation routing problem in multi-radio wireless sensor networks is an NP-hard problem. I first model this problem as a mixed integer and linear programming problem where the objective is to minimize the total transmission subject to the data aggregation routing, channel and radio resources constraints. The solution approach is based on the Lagrangean relaxation technique to relax some constraints into the objective function and then to derive a set of independent subproblems. By optimally solving these subproblems, it can not only calculate the lower bound of the original primal problem but also provide useful information to get the primal feasible solutions. By incorporating these Lagrangean multipliers

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

PubMed Central

Barrett, Louise; Henzi, S. Peter; Lusseau, David

2012-01-01

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution. PMID:22734054

A Multi-Hop Clustering Mechanism for Scalable IoT Networks.

PubMed

Sung, Yoonyoung; Lee, Sookyoung; Lee, Meejeong

2018-03-23

It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.

A Multi-Hop Clustering Mechanism for Scalable IoT Networks

PubMed Central

2018-01-01

It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63–87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6–89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network. PMID:29570691

Research of future network with multi-layer IP address

NASA Astrophysics Data System (ADS)

Li, Guoling; Long, Zhaohua; Wei, Ziqiang

2018-04-01

The shortage of IP addresses and the scalability of routing systems [1] are challenges for the Internet. The idea of dividing existing IP addresses between identities and locations is one of the important research directions. This paper proposed a new decimal network architecture based on IPv9 [11], and decimal network IP address from E.164 principle of traditional telecommunication network, the IP address level, which helps to achieve separation and identification and location of IP address, IP address form a multilayer network structure, routing scalability problem in remission at the same time, to solve the problem of IPv4 address depletion. On the basis of IPv9, a new decimal network architecture is proposed, and the IP address of the decimal network draws on the E.164 principle of the traditional telecommunication network, and the IP addresses are hierarchically divided, which helps to realize the identification and location separation of IP addresses, the formation of multi-layer IP address network structure, while easing the scalability of the routing system to find a way out of IPv4 address exhausted. In addition to modifying DNS [10] simply and adding the function of digital domain, a DDNS [12] is formed. At the same time, a gateway device is added, that is, IPV9 gateway. The original backbone network and user network are unchanged.

Multi-agent modelling framework for water, energy and other resource networks

NASA Astrophysics Data System (ADS)

Knox, S.; Selby, P. D.; Meier, P.; Harou, J. J.; Yoon, J.; Lachaut, T.; Klassert, C. J. A.; Avisse, N.; Mohamed, K.; Tomlinson, J.; Khadem, M.; Tilmant, A.; Gorelick, S.

2015-12-01

Bespoke modelling tools are often needed when planning future engineered interventions in the context of various climate, socio-economic and geopolitical futures. Such tools can help improve system operating policies or assess infrastructure upgrades and their risks. A frequently used approach is to simulate and/or optimise the impact of interventions in engineered systems. Modelling complex infrastructure systems can involve incorporating multiple aspects into a single model, for example physical, economic and political. This presents the challenge of combining research from diverse areas into a single system effectively. We present the Pynsim 'Python Network Simulator' framework, a library for building simulation models capable of representing, the physical, institutional and economic aspects of an engineered resources system. Pynsim is an open source, object oriented code aiming to promote integration of different modelling processes through a single code library. We present two case studies that demonstrate important features of Pynsim's design. The first is a large interdisciplinary project of a national water system in the Middle East with modellers from fields including water resources, economics, hydrology and geography each considering different facets of a multi agent system. It includes: modelling water supply and demand for households and farms; a water tanker market with transfer of water between farms and households, and policy decisions made by government institutions at district, national and international level. This study demonstrates that a well-structured library of code can provide a hub for development and act as a catalyst for integrating models. The second focuses on optimising the location of new run-of-river hydropower plants. Using a multi-objective evolutionary algorithm, this study analyses different network configurations to identify the optimal placement of new power plants within a river network. This demonstrates that Pynsim can be

A bivariate model for analyzing recurrent multi-type automobile failures

NASA Astrophysics Data System (ADS)

Sunethra, A. A.; Sooriyarachchi, M. R.

2017-09-01

The failure mechanism in an automobile can be defined as a system of multi-type recurrent failures where failures can occur due to various multi-type failure modes and these failures are repetitive such that more than one failure can occur from each failure mode. In analysing such automobile failures, both the time and type of the failure serve as response variables. However, these two response variables are highly correlated with each other since the timing of failures has an association with the mode of the failure. When there are more than one correlated response variables, the fitting of a multivariate model is more preferable than separate univariate models. Therefore, a bivariate model of time and type of failure becomes appealing for such automobile failure data. When there are multiple failure observations pertaining to a single automobile, such data cannot be treated as independent data because failure instances of a single automobile are correlated with each other while failures among different automobiles can be treated as independent. Therefore, this study proposes a bivariate model consisting time and type of failure as responses adjusted for correlated data. The proposed model was formulated following the approaches of shared parameter models and random effects models for joining the responses and for representing the correlated data respectively. The proposed model is applied to a sample of automobile failures with three types of failure modes and up to five failure recurrences. The parametric distributions that were suitable for the two responses of time to failure and type of failure were Weibull distribution and multinomial distribution respectively. The proposed bivariate model was programmed in SAS Procedure Proc NLMIXED by user programming appropriate likelihood functions. The performance of the bivariate model was compared with separate univariate models fitted for the two responses and it was identified that better performance is secured by

The Erector Set Computer: Building a Virtual Workstation over a Large Multi-Vendor Network.

ERIC Educational Resources Information Center

Farago, John M.

1989-01-01

Describes a computer network developed at the City University of New York Law School that uses device sharing and local area networking to create a simulated law office. Topics discussed include working within a multi-vendor environment, and the communication, information, and database access services available through the network. (CLB)

New methodologies for multi-scale time-variant reliability analysis of complex lifeline networks

NASA Astrophysics Data System (ADS)

Kurtz, Nolan Scot

be more or less computationally difficult, while the resolution of the network also has large affects. To take advantage of some types of topologies, network hierarchical structures with super-link representation have been used in the literature to increase the computational efficiency by analyzing smaller, densely connected networks; however, such structures were based on user input and subjective at times. To address this, algorithms must be automated and reliable. These hierarchical structures may indicate the structure of the network itself. This risk analysis methodology has been expanded to larger networks using such automated hierarchical structures. Component importance is the most important objective from such network analysis; however, this may only provide the information of which bridges to inspect/repair earliest and little else. High correlations influence such component importance measures in a negative manner. Additionally, a regional approach is not appropriately modelled. To investigate a more regional view, group importance measures based on hierarchical structures have been created. Such structures may also be used to create regional inspection/repair approaches. Using these analytical, quantitative risk approaches, the next generation of decision makers may make both component and regional-based optimal decisions using information from both network function and further effects of infrastructure deterioration.

Molecular transport network security using multi-wavelength optical spins.

PubMed

Tunsiri, Surachai; Thammawongsa, Nopparat; Mitatha, Somsak; Yupapin, Preecha P

2016-01-01

Multi-wavelength generation system using an optical spin within the modified add-drop optical filter known as a PANDA ring resonator for molecular transport network security is proposed. By using the dark-bright soliton pair control, the optical capsules can be constructed and applied to securely transport the trapped molecules within the network. The advantage is that the dark and bright soliton pair (components) can securely propagate for long distance without electromagnetic interference. In operation, the optical intensity from PANDA ring resonator is fed into gold nano-antenna, where the surface plasmon oscillation between soliton pair and metallic waveguide is established.

A Systems Approach Identifies Networks and Genes Linking Sleep and Stress: Implications for Neuropsychiatric Disorders

PubMed Central

Jiang, Peng; Scarpa, Joseph R.; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D.; Hao, Ke; Summa, Keith C.; Yang, He S.; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H.; Turek, Fred W.; Kasarskis, Andrew

2016-01-01

SUMMARY Sleep dysfunction and stress susceptibility are co-morbid complex traits, which often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multi-level organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J×A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests the interplay between sleep, stress, and neuropathology emerge from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework to interrogate the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders. PMID:25921536

Retrieval of cloud properties from POLDER-3 data using the neural network approach

NASA Astrophysics Data System (ADS)

Di Noia, A.; Hasekamp, O. P.

2017-12-01

Satellite multi-angle spectroplarimetry is a useful technique for observing the microphysical properties of clouds and aerosols. Most of the algorithms for the retrieval of cloud and aerosol properties from satellite measurements require multiple calls to radiative transfer models, which make the retrieval computationally expensive. A traditional alternative to these schemes is represented by lookup-tables (LUTs), where the retrieval is performed by choosing, within a predefined database of combinations of clouds or aerosol properties, the combination that best fits the measurements. LUT retrievals are quicker than full-physics, iterative retrievals, but their accuracy is limited by the number of entries stored in the LUT. Another retrieval method capable of producing very quick retrievals without a big sacrifice in accuracy is the neural network method. Neural network methods are routinely applied to several types of satellite measurements, but their application to multi-angle spectropolarimetric data is still in its early stage, because of the difficulty of accounting for the angular variability of the measurements in the training process. We have recently developed a neural network scheme for the retrieval of cloud properties from POLDER-3 data. The neural network retrieval is trained using synthetic measurements performed for realistic combinations of cloud properties and measurement angles, and is able to process an entire orbit in about 20 seconds. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products during one year show encouraging retrieval performance for cloud optical thickness and effective radius. A discussion of the setup of the neural network and of the validation results will be the main topic of our presentation.

A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

PubMed

Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

2015-04-01

Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

Design of multi-phase dynamic chemical networks

NASA Astrophysics Data System (ADS)

Chen, Chenrui; Tan, Junjun; Hsieh, Ming-Chien; Pan, Ting; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.

2017-08-01

Template-directed polymerization reactions enable the accurate storage and processing of nature's biopolymer information. This mutualistic relationship of nucleic acids and proteins, a network known as life's central dogma, is now marvellously complex, and the progressive steps necessary for creating the initial sequence and chain-length-specific polymer templates are lost to time. Here we design and construct dynamic polymerization networks that exploit metastable prion cross-β phases. Mixed-phase environments have been used for constructing synthetic polymers, but these dynamic phases emerge naturally from the growing peptide oligomers and create environments suitable both to nucleate assembly and select for ordered templates. The resulting templates direct the amplification of a phase containing only chain-length-specific peptide-like oligomers. Such multi-phase biopolymer dynamics reveal pathways for the emergence, self-selection and amplification of chain-length- and possibly sequence-specific biopolymers.

Cross-Layer Scheme to Control Contention Window for Per-Flow in Asymmetric Multi-Hop Networks

NASA Astrophysics Data System (ADS)

Giang, Pham Thanh; Nakagawa, Kenji

The IEEE 802.11 MAC standard for wireless ad hoc networks adopts Binary Exponential Back-off (BEB) mechanism to resolve bandwidth contention between stations. BEB mechanism controls the bandwidth allocation for each station by choosing a back-off value from one to CW according to the uniform random distribution, where CW is the contention window size. However, in asymmetric multi-hop networks, some stations are disadvantaged in opportunity of access to the shared channel and may suffer severe throughput degradation when the traffic load is large. Then, the network performance is degraded in terms of throughput and fairness. In this paper, we propose a new cross-layer scheme aiming to solve the per-flow unfairness problem and achieve good throughput performance in IEEE 802.11 multi-hop ad hoc networks. Our cross-layer scheme collects useful information from the physical, MAC and link layers of own station. This information is used to determine the optimal Contention Window (CW) size for per-station fairness. We also use this information to adjust CW size for each flow in the station in order to achieve per-flow fairness. Performance of our cross-layer scheme is examined on various asymmetric multi-hop network topologies by using Network Simulator (NS-2).

Nonlinear dynamics of the complex multi-scale network

NASA Astrophysics Data System (ADS)

Makarov, Vladimir V.; Kirsanov, Daniil; Goremyko, Mikhail; Andreev, Andrey; Hramov, Alexander E.

2018-04-01

In this paper, we study the complex multi-scale network of nonlocally coupled oscillators for the appearance of chimera states. Chimera is a special state in which, in addition to the asynchronous cluster, there are also completely synchronous parts in the system. We show that the increase of nodes in subgroups leads to the destruction of the synchronous interaction within the common ring and to the narrowing of the chimera region.

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation.

PubMed

Xue, Yuan; Xu, Tao; Zhang, Han; Long, L Rodney; Huang, Xiaolei

2018-05-03

Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

NASA Astrophysics Data System (ADS)

Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

2016-09-01

In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

Quantitative analysis of vascular parameters for micro-CT imaging of vascular networks with multi-resolution.

PubMed

Zhao, Fengjun; Liang, Jimin; Chen, Xueli; Liu, Junting; Chen, Dongmei; Yang, Xiang; Tian, Jie

2016-03-01

Previous studies showed that all the vascular parameters from both the morphological and topological parameters were affected with the altering of imaging resolutions. However, neither the sensitivity analysis of the vascular parameters at multiple resolutions nor the distinguishability estimation of vascular parameters from different data groups has been discussed. In this paper, we proposed a quantitative analysis method of vascular parameters for vascular networks of multi-resolution, by analyzing the sensitivity of vascular parameters at multiple resolutions and estimating the distinguishability of vascular parameters from different data groups. Combining the sensitivity and distinguishability, we designed a hybrid formulation to estimate the integrated performance of vascular parameters in a multi-resolution framework. Among the vascular parameters, degree of anisotropy and junction degree were two insensitive parameters that were nearly irrelevant with resolution degradation; vascular area, connectivity density, vascular length, vascular junction and segment number were five parameters that could better distinguish the vascular networks from different groups and abide by the ground truth. Vascular area, connectivity density, vascular length and segment number not only were insensitive to multi-resolution but could also better distinguish vascular networks from different groups, which provided guidance for the quantification of the vascular networks in multi-resolution frameworks.

Systems Biology Approaches for Host–Fungal Interactions: An Expanding Multi-Omics Frontier

PubMed Central

Culibrk, Luka; Croft, Carys A.

2016-01-01

Abstract Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic “two systems” biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host–fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale. PMID:26885725

Multi-layer service function chaining scheduling based on auxiliary graph in IP over optical network

NASA Astrophysics Data System (ADS)

Li, Yixuan; Li, Hui; Liu, Yuze; Ji, Yuefeng

2017-10-01

Software Defined Optical Network (SDON) can be considered as extension of Software Defined Network (SDN) in optical networks. SDON offers a unified control plane and makes optical network an intelligent transport network with dynamic flexibility and service adaptability. For this reason, a comprehensive optical transmission service, able to achieve service differentiation all the way down to the optical transport layer, can be provided to service function chaining (SFC). IP over optical network, as a promising networking architecture to interconnect data centers, is the most widely used scenarios of SFC. In this paper, we offer a flexible and dynamic resource allocation method for diverse SFC service requests in the IP over optical network. To do so, we firstly propose the concept of optical service function (OSF) and a multi-layer SFC model. OSF represents the comprehensive optical transmission service (e.g., multicast, low latency, quality of service, etc.), which can be achieved in multi-layer SFC model. OSF can also be considered as a special SF. Secondly, we design a resource allocation algorithm, which we call OSF-oriented optical service scheduling algorithm. It is able to address multi-layer SFC optical service scheduling and provide comprehensive optical transmission service, while meeting multiple optical transmission requirements (e.g., bandwidth, latency, availability). Moreover, the algorithm exploits the concept of Auxiliary Graph. Finally, we compare our algorithm with the Baseline algorithm in simulation. And simulation results show that our algorithm achieves superior performance than Baseline algorithm in low traffic load condition.

A Social Network Approach to Understanding an Insurgency

DTIC Science & Technology

2007-07-01

and a framework for testing theories regarding struc- tured social relationships.6 Equally relevant is the understanding of a social network approach...A Social Network Approach to Understanding an Insurgency BRIAN REED The study of networks, interactions, and relationships has a long history...characteristics of social network analysis is often counter-intuitive to traditional military thinking, rooted in the efficiency of a hierarchy that

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.

PubMed

Mihaljević, Bojan; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro

2014-01-01

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type

NASA Astrophysics Data System (ADS)

Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

2015-03-01

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

Social network types and functional dependency in older adults in Mexico.

PubMed

Doubova Dubova, Svetlana Vladislavovna; Pérez-Cuevas, Ricardo; Espinosa-Alarcón, Patricia; Flores-Hernández, Sergio

2010-02-27

Social networks play a key role in caring for older adults. A better understanding of the characteristics of different social networks types (TSNs) in a given community provides useful information for designing policies to care for this age group. Therefore this study has three objectives: 1) To derive the TSNs among older adults affiliated with the Mexican Institute of Social Security; 2) To describe the main characteristics of the older adults in each TSN, including the instrumental and economic support they receive and their satisfaction with the network; 3) To determine the association between functional dependency and the type of social network. Secondary data analysis of the 2006 Survey of Autonomy and Dependency (N = 3,348). The TSNs were identified using the structural approach and cluster analysis. The association between functional dependency and the TSNs was evaluated with Poisson regression with robust variance analysis in which socio-demographic characteristics, lifestyle and medical history covariates were included. We identified five TSNs: diverse with community participation (12.1%), diverse without community participation (44.3%); widowed (32.0%); nonfriends-restricted (7.6%); nonfamily-restricted (4.0%). Older adults belonging to widowed and restricted networks showed a higher proportion of dependency, negative self-rated health and depression. Older adults with functional dependency more likely belonged to a widowed network (adjusted prevalence ratio 1.5; 95%CI: 1.1-2.1). The derived TSNs were similar to those described in developed countries. However, we identified the existence of a diverse network without community participation and a widowed network that have not been previously described. These TSNs and restricted networks represent a potential unmet need of social security affiliates.

UMDR: Multi-Path Routing Protocol for Underwater Ad Hoc Networks with Directional Antenna

NASA Astrophysics Data System (ADS)

Yang, Jianmin; Liu, Songzuo; Liu, Qipei; Qiao, Gang

2018-01-01

This paper presents a new routing scheme for underwater ad hoc networks based on directional antennas. Ad hoc networks with directional antennas have become a hot research topic because of space reuse may increase networks capacity. At present, researchers have applied traditional self-organizing routing protocols (such as DSR, AODV) [1] [2] on this type of networks, and the routing scheme is based on the shortest path metric. However, such routing schemes often suffer from long transmission delays and frequent link fragmentation along the intermediate nodes of the selected route. This is caused by a unique feature of directional transmission, often called as “deafness”. In this paper, we take a different approach to explore the advantages of space reuse through multipath routing. This paper introduces the validity of the conventional routing scheme in underwater ad hoc networks with directional antennas, and presents a special design of multipath routing algorithm for directional transmission. The experimental results show a significant performance improvement in throughput and latency.

Operational validation of a multi-period and multi-criteria model conditioning approach for the prediction of rainfall-runoff processes in small forest catchments

NASA Astrophysics Data System (ADS)

Choi, H.; Kim, S.

2012-12-01

Most of hydrologic models have generally been used to describe and represent the spatio-temporal variability of hydrological processes in the watershed scale. Though it is an obvious fact that hydrological responses have the time varying nature, optimal values of model parameters were normally considered as time invariants or constants in most cases. The recent paper of Choi and Beven (2007) presents a multi-period and multi-criteria model conditioning approach. The approach is based on the equifinality thesis within the Generalised Likelihood Uncertainty Estimation (GLUE) framework. In their application, the behavioural TOPMODEL parameter sets are determined by several performance measures for global (annual) and short (30-days) periods, clustered using a Fuzzy C-means algorithm, into 15 types representing different hydrological conditions. Their study shows a good performance on the calibration of a rainfall-runoff model in a forest catchment, and also gives strong indications that it is uncommon to find model realizations that were behavioural over all multi-periods and all performance measures, and multi-period model conditioning approach may become new effective tool for predictions of hydrological processes in ungauged catchments. This study is a follow-up study on the Choi and Beven's (2007) model conditioning approach to test how the approach is effective for the prediction of rainfall-runoff responses in ungauged catchments. To achieve this purpose, 6 small forest catchments are selected among the several hydrological experimental catchments operated by Korea Forest Research Institute. In each catchment, long-term hydrological time series data varying from 10 to 30 years were available. The areas of the selected catchments range from 13.6 to 37.8 ha, and all areas are covered by coniferous or broad-leaves forests. The selected catchments locate in the southern coastal area to the northern part of South Korea. The bed rocks are Granite gneiss, Granite or

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

A multi-scale Q1/P0 approach to langrangian shock hydrodynamics.

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

Shashkov, Mikhail; Love, Edward; Scovazzi, Guglielmo

A new multi-scale, stabilized method for Q1/P0 finite element computations of Lagrangian shock hydrodynamics is presented. Instabilities (of hourglass type) are controlled by a stabilizing operator derived using the variational multi-scale analysis paradigm. The resulting stabilizing term takes the form of a pressure correction. With respect to currently implemented hourglass control approaches, the novelty of the method resides in its residual-based character. The stabilizing residual has a definite physical meaning, since it embeds a discrete form of the Clausius-Duhem inequality. Effectively, the proposed stabilization samples and acts to counter the production of entropy due to numerical instabilities. The proposed techniquemore » is applicable to materials with no shear strength, for which there exists a caloric equation of state. The stabilization operator is incorporated into a mid-point, predictor/multi-corrector time integration algorithm, which conserves mass, momentum and total energy. Encouraging numerical results in the context of compressible gas dynamics confirm the potential of the method.« less

Distributed Sensing and Processing for Multi-Camera Networks

NASA Astrophysics Data System (ADS)

Sankaranarayanan, Aswin C.; Chellappa, Rama; Baraniuk, Richard G.

Sensor networks with large numbers of cameras are becoming increasingly prevalent in a wide range of applications, including video conferencing, motion capture, surveillance, and clinical diagnostics. In this chapter, we identify some of the fundamental challenges in designing such systems: robust statistical inference, computationally efficiency, and opportunistic and parsimonious sensing. We show that the geometric constraints induced by the imaging process are extremely useful for identifying and designing optimal estimators for object detection and tracking tasks. We also derive pipelined and parallelized implementations of popular tools used for statistical inference in non-linear systems, of which multi-camera systems are examples. Finally, we highlight the use of the emerging theory of compressive sensing in reducing the amount of data sensed and communicated by a camera network.

Retrieving Biome Types from Multi-angle Spectral Data

NASA Astrophysics Data System (ADS)

Schull, M. A.; Xu, L.; Latorre, P.; Samanta, A.; Myneni, R. B.; Knyazikhin, Y.

2009-12-01

Many studies have been conducted to demonstrate the ability of multi-angle spectral data to discriminate plant dominant species. Most have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing/unknown land cover. An ancillary objective of the MISR LAI/FPAR algorithm is classification of global vegetation into biome types. The algorithm is based on the 3D radiative transfer equation. Its performance suggests that is has valid LAI retrievals and correct biome identification in about 20% of the pixels. However with a probability of about 70%, uncertainties in LAI retrievals due to biome misclassification do not exceed uncertainties in the observations. In this poster we present an approach to improve reliability of the distribution of biomes and dominant species from multi angle spectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy bidirectional reflectance factor in terms of the leaf spectrum and two spectrally invariant and structurally varying variables - recollision and directional escape probabilities. Theoretical and empirical analyses of ground and airborne data acquired by AVIRIS, AirMISR over two sites in New England and CHRIS/PROBA over BARAX site in Spain suggest that the canopy spectral invariants convey information about canopy structure at both the macro and micro scales. These properties allow for the natural separation of biome classes based on the location of points on the total escape probability vs the proportional escape ratio log-log plane.

Robust hepatic vessel segmentation using multi deep convolution network

NASA Astrophysics Data System (ADS)

Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

2017-03-01

Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

PubMed Central

2016-01-01

Background Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach. PMID:26812235

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach.

PubMed

Laghari, Samreen; Niazi, Muaz A

2016-01-01

Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.

A Mixtures-of-Trees Framework for Multi-Label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods. PMID:25927011

Multi-Lingual Deep Neural Networks for Language Recognition

DTIC Science & Technology

2016-08-08

training configurations for the NIST 2011 and 2015 lan- guage recognition evaluations (LRE11 and LRE15). The best per- forming multi-lingual BN-DNN...very ef- fective approach in the NIST 2015 language recognition evaluation (LRE15) open training condition [4, 5]. In this work we evaluate the impact...language are summarized in Table 2. Two language recognition tasks are used for evaluating the multi-lingual bottleneck systems. The first is the NIST

A Multi-organisational Approach to Service Delivery

NASA Astrophysics Data System (ADS)

Purchase, Valerie; Mills, John; Parry, Glenn

Who is involved in delivering a service? There has been growing recognition in a wide variety of contexts that service is increasingly being delivered by multi-rather than single-organisational entities. Such recognition is evident not only in our experience but in a number of areas of literature including strategy development, core competence analysis, operations and supply chain management, and is reflected in and further facilitated by ICT developments. Customers have always been involved in some degree in the process of value delivery and such involvement is increasing to include complex co-creation of value. Such interactions are challenging when they involve individual customers, however, this becomes ever more challenging when the 'customer' is another organisation or when there are multiple 'customers'. Within this chapter we will consider some of the key drivers for a multi-organisational approach to service delivery; examine the ways in which the parties involved in service co-creation have expanded to include multiple service providers and customers; and finally, identify some of the challenges created by a multi-organisational approach to service delivery.

Finite-time stability of neutral-type neural networks with random time-varying delays

NASA Astrophysics Data System (ADS)

Ali, M. Syed; Saravanan, S.; Zhu, Quanxin

2017-11-01

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

Software defined multi-OLT passive optical network for flexible traffic allocation

NASA Astrophysics Data System (ADS)

Zhang, Shizong; Gu, Rentao; Ji, Yuefeng; Zhang, Jiawei; Li, Hui

2016-10-01

With the rapid growth of 4G mobile network and vehicular network services mobile terminal users have increasing demand on data sharing among different radio remote units (RRUs) and roadside units (RSUs). Meanwhile, commercial video-streaming, video/voice conference applications delivered through peer-to-peer (P2P) technology are still keep on stimulating the sharp increment of bandwidth demand in both business and residential subscribers. However, a significant issue is that, although wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) technology have been proposed to fulfil the ever-increasing bandwidth demand in access network, the bandwidth of optical fiber is not unlimited due to the restriction of optical component properties and modulation/demodulation technology, and blindly increase the wavelength cannot meet the cost-sensitive characteristic of the access network. In this paper, we propose a software defined multi-OLT PON architecture to support efficient scheduling of access network traffic. By introducing software defined networking technology and wavelength selective switch into TWDM PON system in central office, multiple OLTs can be considered as a bandwidth resource pool and support flexible traffic allocation for optical network units (ONUs). Moreover, under the configuration of the control plane, ONUs have the capability of changing affiliation between different OLTs under different traffic situations, thus the inter-OLT traffic can be localized and the data exchange pressure of the core network can be released. Considering this architecture is designed to be maximum following the TWDM PON specification, the existing optical distribution network (ODN) investment can be saved and conventional EPON/GPON equipment can be compatible with the proposed architecture. What's more, based on this architecture, we propose a dynamic wavelength scheduling algorithm, which can be deployed as an application on control plane

Classification of boreal forest by satellite and inventory data using neural network approach

NASA Astrophysics Data System (ADS)

Romanov, A. A.

2012-12-01

The main objective of this research was to develop methodology for boreal (Siberian Taiga) land cover classification in a high accuracy level. The study area covers the territories of Central Siberian several parts along the Yenisei River (60-62 degrees North Latitude): the right bank includes mixed forest and dark taiga, the left - pine forests; so were taken as a high heterogeneity and statistically equal surfaces concerning spectral characteristics. Two main types of data were used: time series of middle spatial resolution satellite images (Landsat 5, 7 and SPOT4) and inventory datasets from the nature fieldworks (used for training samples sets preparation). Method of collecting field datasets included a short botany description (type/species of vegetation, density, compactness of the crowns, individual height and max/min diameters representative of each type, surface altitude of the plot), at the same time the geometric characteristic of each training sample unit corresponded to the spatial resolution of satellite images and geo-referenced (prepared datasets both of the preliminary processing and verification). The network of test plots was planned as irregular and determined by the landscape oriented approach. The main focus of the thematic data processing has been allocated for the use of neural networks (fuzzy logic inc.); therefore, the results of field studies have been converting input parameter of type / species of vegetation cover of each unit and the degree of variability. Proposed approach involves the processing of time series separately for each image mainly for the verification: shooting parameters taken into consideration (time, albedo) and thus expected to assess the quality of mapping. So the input variables for the networks were sensor bands, surface altitude, solar angels and land surface temperature (for a few experiments); also given attention to the formation of the formula class on the basis of statistical pre-processing of results of

Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics.

PubMed

Sridharan, Gautham Vivek; D'Alessandro, Matthew; Bale, Shyam Sundhar; Bhagat, Vicky; Gagnon, Hugo; Asara, John M; Uygun, Korkut; Yarmush, Martin L; Saeidi, Nima

2017-09-01

Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

40 CFR 51.353 - Network type and program evaluation.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 2 2011-07-01 2011-07-01 false Network type and program evaluation. 51... Requirements § 51.353 Network type and program evaluation. Basic and enhanced I/M programs can be centralized.... (a) Presumptive equivalency. A decentralized network consisting of stations that only perform...

Star-type oscillatory networks with generic Kuramoto-type coupling: A model for "Japanese drums synchrony"

NASA Astrophysics Data System (ADS)

Vlasov, Vladimir; Pikovsky, Arkady; Macau, Elbert E. N.