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Sample records for network labeled e-network

Link Label Prediction in Signed Citation Network

KAUST Repository

Akujuobi, Uchenna

2016-04-12

Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the
Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

Science.gov (United States)

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.
Label Space Reduction in MPLS Networks: How Much Can A Single Stacked Label Do?

DEFF Research Database (Denmark)

Solano, Fernando; Stidsen, Thomas K.; Fabregat, Ramon

2008-01-01

Most network operators have considered reducing LSR label spaces (number of labels used) as a way of simplifying management of underlaying virtual private networks (VPNs) and therefore reducing operational expenditure (OPEX). The IETF outlined the label merging feature in MPLS-allowing the config......Most network operators have considered reducing LSR label spaces (number of labels used) as a way of simplifying management of underlaying virtual private networks (VPNs) and therefore reducing operational expenditure (OPEX). The IETF outlined the label merging feature in MPLS...
A study on Optical Labelling Techniques for All-Optical Networks

DEFF Research Database (Denmark)

Holm-Nielsen, Pablo Villanueva

2005-01-01

Optical switching has been proposed as an effective solution to overcoming the potential electronic bottleneck in all-optical network nodes carrying IP over WDM. The solution builds on the use of optical labelling as a mean to route packets or bursts of packets through the network. In addition...... of an intermediate wavelength between label erasure and label insertion. The above mentioned functionalities are assembled in whole network systems experiments that validates the different labelling schemes with respect to transmission, wavelength conversion, label swapping and retransmission. Optical labelling...... and specially the orthogonal schemes for optical labelling, are thus shown to be an effective solution to all-optical networks....
Label Propagation with α-Degree Neighborhood Impact for Network Community Detection

Directory of Open Access Journals (Sweden)

Heli Sun

2014-01-01

Full Text Available Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its α-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The α-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.
Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

Directory of Open Access Journals (Sweden)

Yi Sun

2017-12-01

Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.
Including 10-Gigabit-capable Passive Optical Network under End-to-End Generalized Multi-Protocol Label Switching Provisioned Quality of Service

DEFF Research Database (Denmark)

Brewka, Lukasz Jerzy; Gavler, Anders; Wessing, Henrik

2012-01-01

of the network where quality of service signaling is bridged. This article proposes strategies for generalized multi-protocol label switching control over next emerging passive optical network standard, i.e., the 10-gigabit-capable passive optical network. Node management and resource allocation approaches...... are discussed, and possible issues are raised. The analysis shows that consideration of a 10-gigabit-capable passive optical network as a generalized multi-protocol label switching controlled domain is valid and may advance end-to-end quality of service provisioning for passive optical network based customers.......End-to-end quality of service provisioning is still a challenging task despite many years of research and development in this area. Considering a generalized multi-protocol label switching based core/metro network and resource reservation protocol capable home gateways, it is the access part...
Detecting danger labels with RAM-based neural networks

DEFF Research Database (Denmark)

Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.

1996-01-01

An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Result...
Optical label-controlled transparent metro-access network interface

DEFF Research Database (Denmark)

Osadchiy, Alexey Vladimirovich

This thesis presents results obtained during the course of my PhD research on optical signal routing and interfacing between the metropolitan and access segments of optical networks. Due to both increasing capacity demands and variety of emerging services types, new technological challenges...... control. Highlights of my research include my proposal and experimental proof of principle of an optical coherent detection based optical access network architecture providing support for a large number of users over a single distribution fiber; a spectral amplitude encoded label detection technique...... are arising for seamlessly interfacing metropolitan and access networks. Therefore, in this PhD project, I have analyzed those technological challenges and identified the key aspects to be addressed. I have also proposed and experimentally verified a number of solutions to metropolitan and access networks...
Supervised Sequence Labelling with Recurrent Neural Networks

CERN Document Server

Graves, Alex

2012-01-01

Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional...
Deep Neural Network-Based Chinese Semantic Role Labeling

Institute of Scientific and Technical Information of China (English)

ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

2017-01-01

A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.
Detecting community structure using label propagation with consensus weight in complex network

International Nuclear Information System (INIS)

Liang Zong-Wen; Li Jian-Ping; Yang Fan; Petropulu Athina

2014-01-01

Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm (LPA) is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight (LPAcw), and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions. (interdisciplinary physics and related areas of science and technology)
LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

Science.gov (United States)

Berahmand, Kamal; Bouyer, Asgarali

2018-03-01

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.
Study on multiple-hops performance of MOOC sequences-based optical labels for OPS networks

Science.gov (United States)

Zhang, Chongfu; Qiu, Kun; Ma, Chunli

2009-11-01

In this paper, we utilize a new study method that is under independent case of multiple optical orthogonal codes to derive the probability function of MOOCS-OPS networks, discuss the performance characteristics for a variety of parameters, and compare some characteristics of the system employed by single optical orthogonal code or multiple optical orthogonal codes sequences-based optical labels. The performance of the system is also calculated, and our results verify that the method is effective. Additionally it is found that performance of MOOCS-OPS networks would, negatively, be worsened, compared with single optical orthogonal code-based optical label for optical packet switching (SOOC-OPS); however, MOOCS-OPS networks can greatly enlarge the scalability of optical packet switching networks.
Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

Science.gov (United States)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.
E-njoy the first CERN Global Network e-vent!

CERN Multimedia

CERN Bulletin

2010-01-01

Empowered by the considerable interest it received after it was launched, the CERN Global Network takes off and organizes the first e-vent, which will be a special talk on science communication that will be held on 29 June at 4.30 p.m. in the Council Chamber. You can experience it live on the Global Network site and, if you are a Member, provide feedback. Stay linked! On the CERN Global Network webpage, you will be able to choose the topic of the next e-vents. Seven weeks after its launch, about 600 people have already joined the CERN Global Network and six thematic groups have been created. The whole idea of joining the Network is to stay connected or reconnect with life at CERN where seminars, talks and discussions are undoubtedly a very important and much appreciated part of it. This is where the e-vents come into play. “The e-vents enable members of the Global Network to participate in selected events taking place at CERN, such as lectures or panel discussions. They will...
40 Gbit/s NRZ Packet-Length Insensitive Header Extraction for Optical Label Switching Networks

DEFF Research Database (Denmark)

Seoane, Jorge; Kehayas, E; Avramopoulos, H.

2006-01-01

A simple method for 40 Gbit/s NRZ header extraction based on envelope detection for optical label switching networks is presented. The scheme is insensitive to packet length and spacing and can be single-chip integrated cost-effectively......A simple method for 40 Gbit/s NRZ header extraction based on envelope detection for optical label switching networks is presented. The scheme is insensitive to packet length and spacing and can be single-chip integrated cost-effectively...
Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors.

Science.gov (United States)

Khoshgoftaar, Taghi M; Van Hulse, Jason; Napolitano, Amri

2010-05-01

Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural network learning algorithms to distinguish between examples of the various classes, and class noise can lead to the formulation of incorrect hypotheses. Both class imbalance and labeling errors are pervasive problems encountered in a wide variety of application domains. Many studies have been performed to investigate these problems in isolation, but few have focused on their combined effects. This study presents a comprehensive empirical investigation using neural network algorithms to learn from imbalanced data with labeling errors. In particular, the first component of our study investigates the impact of class noise and class imbalance on two common neural network learning algorithms, while the second component considers the ability of data sampling (which is commonly used to address the issue of class imbalance) to improve their performances. Our results, for which over two million models were trained and evaluated, show that conclusions drawn using the more commonly studied C4.5 classifier may not apply when using neural networks.
LHCb: F.E.C. for DAQ networks

CERN Multimedia

Floros, G; Neufeld, N

2014-01-01

The demand for faster and more reliable networks is growing day by day both in commercial and scientific applications, driving many innovations in network protocols, fiber optics and network-controllers. Operating fast links on relatively inexpensive hardware is a very important challenging aspect of this. One important way to enable this is to provide the network with an existing mechanism of error correction, called Forward Error Correction (F.E.C.). Although error-correcting codes exist for over six decades and F.E.C. is applied in various projects, it is still not widespread in Ethernet networks. F.E.C. introduces a very cost effective way to expand the limits of any network based on micro-controllers synthesized on FPGAs, but it is provided only for specific applications, such as backplane systems. Most of the FPGA and/or IP core vendors either do not provide this feature on their Ethernet implementations or their F.E.C. implementations are based on Ethernet micro-controllers that have a different struct...
Virtuelne privatne mreže - moguće rešenje pouzdanih komunikacija / Virtual private networks: Possible solution of reliable communications

Directory of Open Access Journals (Sweden)

Marinko Smiljanić

2008-04-01

Full Text Available U radu su prikazane osnovne karakteristike virtuelnih privatnih mreža (VPN - Virtual Private Networks. Analizirane su VPN mreže na drugom i trećem sloju sistema otvorenog za povezivanje (OSI - Open System Interconnection. Objašnjena je realizacija internet protokola (IP - Internet Protocol VPN mreže i VPN mreže u okruženju višestruke komutacije labela (MPLS - Multi-Protocol Label Switching. Posebna pažnja posvećena je sigurnosti MPLS VPN mreža, naročito sa stanovišta upotrebe u funkcionalnim sistemima veza, kao što je sistem veza Vojske. / In this paper the basic characteristics of the VPN networks are presented. The VPN networks on the second and the third level of the OSI reference model are analyzed. The realization of the IP VPN and VPN networks within MPLS environment is presented as well. Security in MPLS networks is one of the most important characteristics, especially in military communication systems, which is shown in the second part of this paper.

OTDM Networking for Short Range High-Capacity Highly Dynamic Networks

DEFF Research Database (Denmark)

Medhin, Ashenafi Kiros

This PhD thesis aims at investigating the possibility of designing energy-efficient high-capacity (up to Tbit/s) optical network scenarios, leveraging on the effect of collective switching of many bits simultaneously, as is inherent in high bit rate serial optical data signals. The focus...... is on short range highly dynamic networks, catering to data center needs. The investigation concerns optical network scenarios, and experimental implementations of high bit rate serial data packet generation and reception, scalable optical packet labeling, simple optical label extraction and stable ultra...
Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI.

Science.gov (United States)

Dai, Weiying; Varma, Gopal; Scheidegger, Rachel; Alsop, David C

2016-03-01

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) has been widely used to investigate spontaneous low-frequency signal fluctuations across brain resting state networks. However, BOLD only provides relative measures of signal fluctuations. Arterial Spin Labeling (ASL) MRI holds great potential for quantitative measurements of resting state network fluctuations. This study systematically quantified signal fluctuations of the large-scale resting state networks using ASL data from 20 healthy volunteers by separating them from global signal fluctuations and fluctuations caused by residual noise. Global ASL signal fluctuation was 7.59% ± 1.47% relative to the ASL baseline perfusion. Fluctuations of seven detected resting state networks vary from 2.96% ± 0.93% to 6.71% ± 2.35%. Fluctuations of networks and residual noise were 6.05% ± 1.18% and 6.78% ± 1.16% using 4-mm resolution ASL data applied with Gaussian smoothing kernel of 6mm. However, network fluctuations were reduced by 7.77% ± 1.56% while residual noise fluctuation was markedly reduced by 39.75% ± 2.90% when smoothing kernel of 12 mm was applied to the ASL data. Therefore, global and network fluctuations are the dominant structured noise sources in ASL data. Quantitative measurements of resting state networks may enable improved noise reduction and provide insights into the function of healthy and diseased brain. © The Author(s) 2015.
Multi-modular neural networks for the classification of e+e- hadronic events

International Nuclear Information System (INIS)

Proriol, J.

1994-01-01

Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)
Eco label and integrated product policies. Supporting companies by networking

International Nuclear Information System (INIS)

Frey, M.; Iraldo, F.

1999-01-01

In 1998 IEFE Bocconi University (Italy) carried out a project for the diffusion of the European Commission Eco label, the certification of the environmental quality of products. What clearly emerges from this experience is that some Italian SMEs, among the most innovative and market-oriented, are prone and ready to grasp the opportunities connected with the Eco label adoption. The more these enterprises are capable of starting up a network of socio-institutional actors eager to support them in promoting the environmental quality of their products, the more they succeed in exploiting the above mentioned opportunities [it
E-Center: A Collaborative Platform for Wide Area Network Users

Science.gov (United States)

Grigoriev, M.; DeMar, P.; Tierney, B.; Lake, A.; Metzger, J.; Frey, M.; Calyam, P.

2012-12-01

The E-Center is a social collaborative web-based platform for assisting network users in understanding network conditions across network paths of interest to them. It is designed to give a user the necessary tools to isolate, identify, and resolve network performance-related problems. E-Center provides network path information on a link-by-link level, as well as from an end-to-end perspective. In addition to providing current and recent network path data, E-Center is intended to provide a social media environment for them to share issues, ideas, concerns, and problems. The product has a modular design that accommodates integration of other network services that make use of the same network path and performance data.
E-center: A collaborative platform for wide area network users

Energy Technology Data Exchange (ETDEWEB)

Grigoriev, M. [Fermilab; DeMar, P. [Fermilab; Tierney, B. [LBL, Berkeley; Lake, A. [LBL, Berkeley; Metzger, J. [LBL, Berkeley; Frey, M. [Bucknell U.; Calyam, P. [Ohio State U.

2012-01-01

The E-Center is a social collaborative web-based platform for assisting network users in understanding network conditions across network paths of interest to them. It is designed to give a user the necessary tools to isolate, identify, and resolve network performance-related problems. E-Center provides network path information on a link-by-link level, as well as from an end-to-end perspective. In addition to providing current and recent network path data, E-Center is intended to provide a social media environment for them to share issues, ideas, concerns, and problems. The product has a modular design that accommodates integration of other network services that make use of the same network path and performance data.
E-Center: A Collaborative Platform for Wide Area Network Users

International Nuclear Information System (INIS)

Grigoriev, M; DeMar, P; Tierney, B; Lake, A; Metzger, J; Frey, M; Calyam, P

2012-01-01

The E-Center is a social collaborative web-based platform for assisting network users in understanding network conditions across network paths of interest to them. It is designed to give a user the necessary tools to isolate, identify, and resolve network performance-related problems. E-Center provides network path information on a link-by-link level, as well as from an end-to-end perspective. In addition to providing current and recent network path data, E-Center is intended to provide a social media environment for them to share issues, ideas, concerns, and problems. The product has a modular design that accommodates integration of other network services that make use of the same network path and performance data.
High optical label switching add-drop multiplexer nodes with nanoseconds latency for 5G metro/access networks

NARCIS (Netherlands)

Calabretta, N.; Miao, W.; De Waardt, H.

2016-01-01

We present a novel optical add-drop multiplexer for next-generation metro/access networks by exploiting optical label switching technology. Experimental results of a ring network show nanoseconds add/drop operation including multicasting and power equalization of 50Gb/s data.
Network security

CERN Document Server

Perez, André

2014-01-01

This book introduces the security mechanisms deployed in Ethernet, Wireless-Fidelity (Wi-Fi), Internet Protocol (IP) and MultiProtocol Label Switching (MPLS) networks. These mechanisms are grouped throughout the book according to the following four functions: data protection, access control, network isolation, and data monitoring. Data protection is supplied by data confidentiality and integrity control services. Access control is provided by a third-party authentication service. Network isolation is supplied by the Virtual Private Network (VPN) service. Data monitoring consists of applying
Experimental demonstrations of all-optical networking functions for WDM optical networks

Science.gov (United States)

Gurkan, Deniz

The deployment of optical networks will enable high capacity links between users but will introduce the problems associated with transporting and managing more channels. Many network functions should be implemented in optical domain; main reasons are: to avoid electronic processing bottlenecks, to achieve data-format and data-rate independence, to provide reliable and cost efficient control and management information, to simultaneously process multiple wavelength channel operation for wavelength division multiplexed (WDM) optical networks. The following novel experimental demonstrations of network functions in the optical domain are presented: Variable-bit-rate recognition of the header information in a data packet. The technique is reconfigurable for different header sequences and uses optical correlators as look-up tables. The header is processed and a signal is sent to the switch for a series of incoming data packets at 155 Mb/s, 622 Mb/s, and 2.5 Gb/s in a reconfigurable network. Simultaneous optical time-slot-interchange and wavelength conversion of the bits in a 2.5-Gb/s data stream to achieve a reconfigurable time/wavelength switch. The technique uses difference-frequency-generation (DFG) for wavelength conversion and fiber Bragg gratings (FBG) as wavelength-dependent optical time buffers. The WDM header recognition module simultaneously recognizing two header bits on each of two 2.5-Gbit/s WDM packet streams. The module is tunable to enable reconfigurable look-up tables. Simultaneous and independent label swapping and wavelength conversion of two WDM channels for a multi-protocol label switching (MPLS) network. Demonstration of label swapping of distinct 8-bit-long labels for two WDM data channels is presented. Two-dimensional code conversion module for an optical code-division multiple-access (O-CDMA) local area network (LAN) system. Simultaneous wavelength conversion and time shifting is achieved to enable flexible code conversion and increase code re
Network support for e-Science in Latin America

International Nuclear Information System (INIS)

Stanton, M.; Macahdo, I.; Faerman, M.; Moura, A. L.

2007-01-01

Computer networks in Latin America have connected scientists in the region to their peers in other parts of the world since 1986. Starting with the creation of Internet2 in 1996, a new global research network has been extended throughout the world, providing communications infrastructure for large-scale international scientific collaboration. With the creation of the RedCLARA network and its links to Europe and the US between 2004 and 2005, this global network reached the majority of Latin America countries, setting the stage for much closer collaboration between scientists in Latin America and their counterparts in other countries. In this article we describe the development of the research networking infrastructure currently available within the region together with its inter-regional connections, and how this infrastructure is being used for support of e-science. Particular attention is given to the role of the national research and education networks (NRENs) in the region, and of their association, CLARA, in providing networking support for e-science projects. CLARA and Latin American NRENs are active partners in the EU-supported EELA and RINGrid projects, and also are making significant supporting contributions to the success of other international projects with Latin American partners, in fields such as High-Energy Physics, Astronomy and Astrophysics and Space Geodesy, to single out the early adopters of advanced networking technologies. These contributions are described in the article. The article concludes describing future trends in networking infrastructure in the region, in order to meet foreseeable demands for e-science support. These include the widespread adoption of optical networking and support for grid-based applications, as well as the provisioning of significantly higher international bandwidth to meet the declared needs for international collaboration in a number of fields including those mentioned above. (Author)
Vertex labeling and routing in self-similar outerplanar unclustered graphs modeling complex networks

International Nuclear Information System (INIS)

Comellas, Francesc; Miralles, Alicia

2009-01-01

This paper introduces a labeling and optimal routing algorithm for a family of modular, self-similar, small-world graphs with clustering zero. Many properties of this family are comparable to those of networks associated with technological and biological systems with low clustering, such as the power grid, some electronic circuits and protein networks. For these systems, the existence of models with an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization) and also to understand the underlying mechanisms that have shaped their particular structure.
Flux balance analysis of ammonia assimilation network in E. coli predicts preferred regulation point.

Science.gov (United States)

Wang, Lu; Lai, Luhua; Ouyang, Qi; Tang, Chao

2011-01-25

Nitrogen assimilation is a critical biological process for the synthesis of biomolecules in Escherichia coli. The central ammonium assimilation network in E. coli converts carbon skeleton α-ketoglutarate and ammonium into glutamate and glutamine, which further serve as nitrogen donors for nitrogen metabolism in the cell. This reaction network involves three enzymes: glutamate dehydrogenase (GDH), glutamine synthetase (GS) and glutamate synthase (GOGAT). In minimal media, E. coli tries to maintain an optimal growth rate by regulating the activity of the enzymes to match the availability of the external ammonia. The molecular mechanism and the strategy of the regulation in this network have been the research topics for many investigators. In this paper, we develop a flux balance model for the nitrogen metabolism, taking into account of the cellular composition and biosynthetic requirements for nitrogen. The model agrees well with known experimental results. Specifically, it reproduces all the (15)N isotope labeling experiments in the wild type and the two mutant (ΔGDH and ΔGOGAT) strains of E. coli. Furthermore, the predicted catalytic activities of GDH, GS and GOGAT in different ammonium concentrations and growth rates for the wild type, ΔGDH and ΔGOGAT strains agree well with the enzyme concentrations obtained from western blots. Based on this flux balance model, we show that GS is the preferred regulation point among the three enzymes in the nitrogen assimilation network. Our analysis reveals the pattern of regulation in this central and highly regulated network, thus providing insights into the regulation strategy adopted by the bacteria. Our model and methods may also be useful in future investigations in this and other networks.
Flux balance analysis of ammonia assimilation network in E. coli predicts preferred regulation point.

Directory of Open Access Journals (Sweden)

Lu Wang

Full Text Available Nitrogen assimilation is a critical biological process for the synthesis of biomolecules in Escherichia coli. The central ammonium assimilation network in E. coli converts carbon skeleton α-ketoglutarate and ammonium into glutamate and glutamine, which further serve as nitrogen donors for nitrogen metabolism in the cell. This reaction network involves three enzymes: glutamate dehydrogenase (GDH, glutamine synthetase (GS and glutamate synthase (GOGAT. In minimal media, E. coli tries to maintain an optimal growth rate by regulating the activity of the enzymes to match the availability of the external ammonia. The molecular mechanism and the strategy of the regulation in this network have been the research topics for many investigators. In this paper, we develop a flux balance model for the nitrogen metabolism, taking into account of the cellular composition and biosynthetic requirements for nitrogen. The model agrees well with known experimental results. Specifically, it reproduces all the (15N isotope labeling experiments in the wild type and the two mutant (ΔGDH and ΔGOGAT strains of E. coli. Furthermore, the predicted catalytic activities of GDH, GS and GOGAT in different ammonium concentrations and growth rates for the wild type, ΔGDH and ΔGOGAT strains agree well with the enzyme concentrations obtained from western blots. Based on this flux balance model, we show that GS is the preferred regulation point among the three enzymes in the nitrogen assimilation network. Our analysis reveals the pattern of regulation in this central and highly regulated network, thus providing insights into the regulation strategy adopted by the bacteria. Our model and methods may also be useful in future investigations in this and other networks.
All-Optical Network Subsystems Using Integrated SOA-Based Optical Gates and Flip-Flops for Label-Swapped Netorks

DEFF Research Database (Denmark)

Seoane, Jorge; Holm-Nielsen, Pablo Villanueva; Kehayas, E.

2006-01-01

In this letter, we demonstrate that all-optical network subsystems, offering intelligence in the optical layer, can be constructed by functional integration of integrated all-optical logic gates and flip-flops. In this context, we show 10-Gb/s all-optical 2-bit label address recognition......-level advantages of these all-optical subsystems combined with their realization with compact integrated devices, suggest that they are strong candidates for future packet/label switched optical networks....... by interconnecting two optical gates that perform xor operation on incoming optical labels. We also demonstrate 40-Gb/s all-optical wavelength-switching through an optically controlled wavelength converter, consisting of an integrated flip-flop prototype device driven by an integrated optical gate. The system...
QoE management in wireless networks

CERN Document Server

Wang, Ying; Zhang, Ping

2017-01-01

This SpringerBrief presents research results on QoE management schemes for mobile services, including user services, and resource allocation. Along with a review of the research literature, it offers a data-driven architecture for personalized QoE management in wireless networks. The primary focus is on introducing efficient personalized character extraction mechanisms, e.g., context-aware Bayesian graph model, and cooperative QoE management mechanisms. Moreover, in order to demonstrate in the effectiveness of the QoE model, a QoE measurement platform is described and its collected data examined. The brief concludes with a discussion of future research directions. The example mechanisms and the data-driven architecture provide useful insights into the designs of QoE management, and motivate a new line of thinking for users' satisfaction in future wireless networks.
Development of surrogate models using artificial neural network for building shell energy labelling

NARCIS (Netherlands)

Melo, A.P.; Costola, D.; Lamberts, R.; Hensen, J.L.M.

2014-01-01

Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of
Optical network control plane for multi-domain networking

DEFF Research Database (Denmark)

Manolova, Anna Vasileva

This thesis focuses on multi-domain routing for traffice engineering and survivability support in optical transport networks under the Generalized Multi-Protocol Label Switching (GMPLS) control framework. First, different extensions to the Border Gateway Protocol for multi-domain Traffic...... process are not enough for efficient TE in mesh multi-domain networks. Enhancing the protocol with multi-path dissemination capability, combined with the employment of an end-to-end TE metric proves to be a highly efficient solution. Simulation results show good performance characteristics of the proposed...... is not as essential for improved network performance as the length of the provided paths. Second, the issue of multi-domain survivability support is analyzed. An AS-disjoint paths is beneficial not only for resilience support, but also for facilitating adequate network reactions to changes in the network, which...
Modeling the citation network by network cosmology.

Science.gov (United States)

Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

2015-01-01

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.
Modality-specificity of Selective Attention Networks.

Science.gov (United States)

Stewart, Hannah J; Amitay, Sygal

2015-01-01

To establish the modality specificity and generality of selective attention networks. Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled "general attention." The third component was labeled "auditory attention," as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as "spatial orienting" and "spatial conflict," respectively-they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task-all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.

Novel topological descriptors for analyzing biological networks

Directory of Open Access Journals (Sweden)

Varmuza Kurt K

2010-06-01

Full Text Available Abstract Background Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. Results In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. Conclusions Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.
Adaptive Suspicious Prevention for Defending DoS Attacks in SDN-Based Convergent Networks

OpenAIRE

Dao, Nhu-Ngoc; Kim, Joongheon; Park, Minho; Cho, Sungrae

2016-01-01

The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switchi...
Comparing biological networks via graph compression

Directory of Open Access Journals (Sweden)

Hayashida Morihiro

2010-09-01

Full Text Available Abstract Background Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges. Results This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae, and B. subtilis, and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks. Conclusions Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.
InP monolithically integrated label swapper device for spectral amplitude coded optical packet networks

NARCIS (Netherlands)

Muñoz, P.; García-Olcina, R.; Doménech, J.D.; Rius, M.; Sancho, J.C.; Capmany, J.; Chen, L.R.; Habib, C.; Leijtens, X.J.M.; Vries, de T.; Heck, M.J.R.; Augustin, L.M.; Nötzel, R.; Robbins, D.J.

2010-01-01

In this paper a label swapping device, for spectral amplitude coded optical packet networks, fully integrated using InP technology is presented. Compared to previous demonstrations using discrete component assembly, the device footprint is reduced by a factor of 105 and the operation speed is
Emotion and Social Network Perceptions: How Does Anger Bias Perceptions of Networks?

Science.gov (United States)

2013-03-01

indicate the extent to which they felt angry because previous research suggests that labeling emotions may reduce their impact (Lerner & Keltner , 2000...AFRL-AFOSR-UK-TR-2013-0009 Emotion and Social Network Perceptions: How Does Anger Bias Perceptions of Networks? Professor...REPORT TYPE Final Report 3. DATES COVERED (From – To) 26 August 2011 – 23 February 2013 4. TITLE AND SUBTITLE Emotion and Social Network
Generative adversarial networks for brain lesion detection

Science.gov (United States)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.
Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

Science.gov (United States)

Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

2014-04-21

Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment.
Topology Recognition and Leader Election in Colored Networks

OpenAIRE

Dereniowski, Dariusz; Pelc, Andrzej

2016-01-01

Topology recognition and leader election are fundamental tasks in distributed computing in networks. The first of them requires each node to find a labeled isomorphic copy of the network, while the result of the second one consists in a single node adopting the label 1 (leader), with all other nodes adopting the label 0 and learning a path to the leader. We consider both these problems in networks whose nodes are equipped with not necessarily distinct labels called colors, and ports at each n...
Modality-specificity of selective attention networks

Directory of Open Access Journals (Sweden)

Hannah Jamieson Stewart

2015-11-01

Full Text Available Objective: To establish the modality specificity and generality of selective attention networks. Method: Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT, the Test of Everyday Attention (TEA, and the Test of Attention in Listening (TAiL. These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. Results: The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled ‘general attention’. The third component was labeled ‘auditory attention’, as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as ‘spatial orienting’ and ‘spatial conflict’, respectively – they were comprised of orienting and conflict resolution measures from the vANT, aANT and TAiL attend-location task – all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location. Conclusions: These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.
E-commerce System Security Assessment based on Bayesian Network Algorithm Research

OpenAIRE

Ting Li; Xin Li

2013-01-01

Evaluation of e-commerce network security is based on assessment method Bayesian networks, and it first defines the vulnerability status of e-commerce system evaluation index and the vulnerability of the state model of e-commerce systems, and after the principle of the Bayesian network reliability of e-commerce system and the criticality of the vulnerabilities were analyzed, experiments show that the change method is a good evaluation of the security of e-commerce systems.
Beyond E-business : towards networked structures

NARCIS (Netherlands)

Grefen, P.W.P.J.

2015-01-01

In Beyond E-Business: Towards Networked Structures Paul Grefen returns with his tried and tested BOAT framework for e-business, now fully expanded and updated with the very latest overview of digitally connected business; from business models, organization structures and architecture, to information
The Role of Social Networking Services in eParticipation

DEFF Research Database (Denmark)

Sæbø, Øystein; Rose, Jeremy; Nyvang, Tom

2009-01-01

, content-generation and the development of loosely-coupled communities. They provide the forum for much discussion and interaction. In this respect social networking could contribute to solve some of the problems of engaging their users that eParticipation services often struggle with. This paper...... and social networking because democratic systems favour the interests of larger groups of citizens --- the more voices behind a political proposition, the greater its chances of success. In this context of challenges the study of social networking on the internet and social network theory offers valuable...... insights into the practices and theories of citizen engagement. Social network theory focuses on the chains of relationships that social actors communicate and act within. Some social networking services on the internet attract large numbers of users, and apparently sustain a great deal of interaction...
Percolation of interdependent network of networks

International Nuclear Information System (INIS)

Havlin, Shlomo; Stanley, H. Eugene; Bashan, Amir; Gao, Jianxi; Kenett, Dror Y.

2015-01-01

Complex networks appear in almost every aspect of science and technology. Previous work in network theory has focused primarily on analyzing single networks that do not interact with other networks, despite the fact that many real-world networks interact with and depend on each other. Very recently an analytical framework for studying the percolation properties of interacting networks has been introduced. Here we review the analytical framework and the results for percolation laws for a Network Of Networks (NONs) formed by n interdependent random networks. The percolation properties of a network of networks differ greatly from those of single isolated networks. In particular, because the constituent networks of a NON are connected by node dependencies, a NON is subject to cascading failure. When there is strong interdependent coupling between networks, the percolation transition is discontinuous (first-order) phase transition, unlike the well-known continuous second-order transition in single isolated networks. Moreover, although networks with broader degree distributions, e.g., scale-free networks, are more robust when analyzed as single networks, they become more vulnerable in a NON. We also review the effect of space embedding on network vulnerability. It is shown that for spatially embedded networks any finite fraction of dependency nodes will lead to abrupt transition
Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Science.gov (United States)

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.
A new algorithm to construct phylogenetic networks from trees.

Science.gov (United States)

Wang, J

2014-03-06

Developing appropriate methods for constructing phylogenetic networks from tree sets is an important problem, and much research is currently being undertaken in this area. BIMLR is an algorithm that constructs phylogenetic networks from tree sets. The algorithm can construct a much simpler network than other available methods. Here, we introduce an improved version of the BIMLR algorithm, QuickCass. QuickCass changes the selection strategy of the labels of leaves below the reticulate nodes, i.e., the nodes with an indegree of at least 2 in BIMLR. We show that QuickCass can construct simpler phylogenetic networks than BIMLR. Furthermore, we show that QuickCass is a polynomial-time algorithm when the output network that is constructed by QuickCass is binary.
Implementing e-network-supported inquiry learning in science

DEFF Research Database (Denmark)

Williams, John; Cowie, Bronwen; Khoo, Elaine

2013-01-01

The successful implementation of electronically networked (e-networked) tools to support an inquiry-learning approach in secondary science classrooms is dependent on a range of factors spread between teachers, schools, and students. The teacher must have a clear understanding of the nature......-construct knowledge using a wide range of resources for meaning making and expression of ideas. These outcomes were, however, contingent on the interplay of teacher understanding of the nature of science inquiry and school provision of an effective technological infrastructure and support for flexible curriculum...... of inquiry, the school must provide effective technological infrastructure and sympathetic curriculum parameters, and the students need to be carefully scaffolded to the point of engaging with the inquiry process. Within this study, e-networks supported students to exercise agency, collaborate, and co...
MotifNet: a web-server for network motif analysis.

Science.gov (United States)

Smoly, Ilan Y; Lerman, Eugene; Ziv-Ukelson, Michal; Yeger-Lotem, Esti

2017-06-15

Network motifs are small topological patterns that recur in a network significantly more often than expected by chance. Their identification emerged as a powerful approach for uncovering the design principles underlying complex networks. However, available tools for network motif analysis typically require download and execution of computationally intensive software on a local computer. We present MotifNet, the first open-access web-server for network motif analysis. MotifNet allows researchers to analyze integrated networks, where nodes and edges may be labeled, and to search for motifs of up to eight nodes. The output motifs are presented graphically and the user can interactively filter them by their significance, number of instances, node and edge labels, and node identities, and view their instances. MotifNet also allows the user to distinguish between motifs that are centered on specific nodes and motifs that recur in distinct parts of the network. MotifNet is freely available at http://netbio.bgu.ac.il/motifnet . The website was implemented using ReactJs and supports all major browsers. The server interface was implemented in Python with data stored on a MySQL database. estiyl@bgu.ac.il or michaluz@cs.bgu.ac.il. 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
Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans

Science.gov (United States)

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.
Resolving epidemic network failures through differentiated repair times

DEFF Research Database (Denmark)

Fagertun, Anna Manolova; Ruepp, Sarah Renée; Manzano, Marc

2015-01-01

In this study, the authors investigate epidemic failure spreading in large-scale transport networks under generalisedmulti-protocol label switching control plane. By evaluating the effect of the epidemic failure spreading on the network,they design several strategies for cost-effective network pe...... assigninglower repair times among the network nodes. They believe that the event-driven simulation model can be highly beneficialfor network providers, since it could be used during the network planning process for facilitating cost-effective networksurvivability design.......In this study, the authors investigate epidemic failure spreading in large-scale transport networks under generalisedmulti-protocol label switching control plane. By evaluating the effect of the epidemic failure spreading on the network,they design several strategies for cost-effective network...... performance improvement via differentiated repair times. First, theyidentify the most vulnerable and the most strategic nodes in the network. Then, via extensive event-driven simulations theyshow that strategic placement of resources for improved failure recovery has better performance than randomly...
Data center networks and network architecture

Science.gov (United States)

Esaki, Hiroshi

2014-02-01

This paper discusses and proposes the architectural framework, which is for data center networks. The data center networks require new technical challenges, and it would be good opportunity to change the functions, which are not need in current and future networks. Based on the observation and consideration on data center networks, this paper proposes; (i) Broadcast-free layer 2 network (i.e., emulation of broadcast at the end-node), (ii) Full-mesh point-to-point pipes, and (iii) IRIDES (Invitation Routing aDvertisement for path Engineering System).

Using neural networks with jet shapes to identify b jets in e+e- interactions

International Nuclear Information System (INIS)

Bellantoni, L.; Conway, J.S.; Jacobsen, J.E.; Pan, Y.B.; Wu Saulan

1991-01-01

A feed-forward neural network trained using backpropagation was used to discriminate between b and light quark jets in e + e - → Z 0 → qanti q events. The information presented to the network consisted of 25 jet shape variables. The network successfully identified b jets in two- and three-jet events modeled using a detector simulation. The jet identification efficiency for two-jet events was 61% and the probability to call a light quark jet a b jet equal to 20%. (orig.)
Folding and unfolding phylogenetic trees and networks.

Science.gov (United States)

Huber, Katharina T; Moulton, Vincent; Steel, Mike; Wu, Taoyang

2016-12-01

Phylogenetic networks are rooted, labelled directed acyclic graphswhich are commonly used to represent reticulate evolution. There is a close relationship between phylogenetic networks and multi-labelled trees (MUL-trees). Indeed, any phylogenetic network N can be "unfolded" to obtain a MUL-tree U(N) and, conversely, a MUL-tree T can in certain circumstances be "folded" to obtain aphylogenetic network F(T) that exhibits T. In this paper, we study properties of the operations U and F in more detail. In particular, we introduce the class of stable networks, phylogenetic networks N for which F(U(N)) is isomorphic to N, characterise such networks, and show that they are related to the well-known class of tree-sibling networks. We also explore how the concept of displaying a tree in a network N can be related to displaying the tree in the MUL-tree U(N). To do this, we develop aphylogenetic analogue of graph fibrations. This allows us to view U(N) as the analogue of the universal cover of a digraph, and to establish a close connection between displaying trees in U(N) and reconciling phylogenetic trees with networks.
Behavior Analysis of Bank into E-Commerce under Network Externalities

Institute of Scientific and Technical Information of China (English)

CUI Jisheng; ZHUANG Lei

2017-01-01

With the advent of Internet financial innovation,many commercial banks quietly have started to enter into the E-commercial in order to prevent oligarchs from eroding financial market.From the perspective of industrial division,this paper reveals the nature of a phenomenon that E-commercial enterprises and banks have stepped into each other's field,which E-commerce of banks can give full play to network effects.Then it uues game theory to analyze the motions of banks to involve into E-commerce and the short-term competitive equilibrium of large incumbent E-commercial enterprises as well.For individual rationality,the dominant strategy of banks and E-commercial enterprises is (enter,counterattack).Considering network externalities,it constructs a competing model on banks and incumbent E-commercial enterprises and simulates competitive trends and balanced results of their behaviors,which illustrates that banks can obtain network effect after choosing E-commerce strategy.
Strategies for optical transport network recovery under epidemic network failures

DEFF Research Database (Denmark)

Ruepp, Sarah Renée; Fagertun, Anna Manolova; Kosteas, Vasileios

2015-01-01

The current trend in deploying automatic control plane solutions for increased flexibility in the optical transport layer leads to numerous advantages for both the operators and the customers, but also pose challenges related to the stability of the network and its ability to operate in a robust...... manner under different failure scenarios. This work evaluates two rerouting strategies and proposes four policies for failure handling in a connection-oriented optical transport network, under generalized multiprotocol label switching control plane. The performance of the strategies and the policies......, and that there exist a clear trade-off between policy performance and network resource consumption, which must be addressed by network operators for improved robustness of their transport infrastructures. Applying proactive methods for avoiding areas where epidemic failures spread results in 50% less connections...
New designing of E-Learning systems with using network learning

OpenAIRE

Malayeri, Amin Daneshmand; Abdollahi, Jalal

2010-01-01

One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...
Robust networked H∞ synchronization of nonidentical chaotic Lur'e systems

International Nuclear Information System (INIS)

Yang De-Dong

2014-01-01

We mainly investigate the robust networked H ∞ synchronization problem of nonidentical chaotic Lur'e systems. In the design of the synchronization scheme, some network characteristics, such as nonuniform sampling, transmission-induced delays, and data packet dropouts, are considered. The parameters of master—slave chaotic Lur'e systems often allow differences. The sufficient condition in terms of linear matrix inequality (LMI) is obtained to guarantee the dissipative synchronization of nonidentical chaotic Lur'e systems in network environments. A numerical example is given to illustrate the validity of the proposed method. (general)
Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance.

Science.gov (United States)

Bean, Daniel M; Stringer, Clive; Beeknoo, Neeraj; Teo, James; Dobson, Richard J B

2017-01-01

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King's College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A 'core' subnetwork containing only 13-17% of all edges channelled 83-90% of the patient flow, while an 'ephemeral' network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing.
Implementation of a Framework for Collaborative Social Networks in E-Learning

Science.gov (United States)

Maglajlic, Seid

2016-01-01

This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…
Locating a tree in a phylogenetic network

NARCIS (Netherlands)

Iersel, van L.J.J.; Semple, C.; Steel, M.A.

2010-01-01

Phylogenetic trees and networks are leaf-labelled graphs that are used to describe evolutionary histories of species. The Tree Containment problem asks whether a given phylogenetic tree is embedded in a given phylogenetic network. Given a phylogenetic network and a cluster of species, the Cluster
Undermining and Strengthening Social Networks through Network Modification

Science.gov (United States)

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-10-01

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.
Research on e-commerce transaction networks using multi-agent modelling and open application programming interface

Science.gov (United States)

Piao, Chunhui; Han, Xufang; Wu, Harris

2010-08-01

We provide a formal definition of an e-commerce transaction network. Agent-based modelling is used to simulate e-commerce transaction networks. For real-world analysis, we studied the open application programming interfaces (APIs) from eBay and Taobao e-commerce websites and captured real transaction data. Pajek is used to visualise the agent relationships in the transaction network. We derived one-mode networks from the transaction network and analysed them using degree and betweenness centrality. Integrating multi-agent modelling, open APIs and social network analysis, we propose a new way to study large-scale e-commerce systems.
Label-free detection of DNA hybridization using carbon nanotube network field-effect transistors

Science.gov (United States)

Star, Alexander; Tu, Eugene; Niemann, Joseph; Gabriel, Jean-Christophe P.; Joiner, C. Steve; Valcke, Christian

2006-01-01

We report carbon nanotube network field-effect transistors (NTNFETs) that function as selective detectors of DNA immobilization and hybridization. NTNFETs with immobilized synthetic oligonucleotides have been shown to specifically recognize target DNA sequences, including H63D single-nucleotide polymorphism (SNP) discrimination in the HFE gene, responsible for hereditary hemochromatosis. The electronic responses of NTNFETs upon single-stranded DNA immobilization and subsequent DNA hybridization events were confirmed by using fluorescence-labeled oligonucleotides and then were further explored for label-free DNA detection at picomolar to micromolar concentrations. We have also observed a strong effect of DNA counterions on the electronic response, thus suggesting a charge-based mechanism of DNA detection using NTNFET devices. Implementation of label-free electronic detection assays using NTNFETs constitutes an important step toward low-cost, low-complexity, highly sensitive and accurate molecular diagnostics. hemochromatosis | SNP | biosensor
The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

Science.gov (United States)

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…
Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Directory of Open Access Journals (Sweden)

Jacques Demongeot

2018-01-01

Full Text Available Networks used in biological applications at different scales (molecule, cell and population are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system as well as in their discrete Boolean versions (e.g., non-linear Hopfield system; in both cases, the notion of interaction graph G(J associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J, kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i attractor entropy, (ii isochronal entropy and (iii entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.
Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review

Science.gov (United States)

Cela, Karina L.; Sicilia, Miguel Ángel; Sánchez, Salvador

2015-01-01

E-learning occupies an increasingly prominent place in education. It provides the learner with a rich virtual network where he or she can exchange ideas and information and create synergies through interactions with other members of the network, whether fellow learners or teachers. Social network analysis (SNA) has proven extremely powerful at…
Localizing and placement of network node functions in a network

NARCIS (Netherlands)

Strijkers, R.J.; Meulenhoff, P.J.

2014-01-01

The invention enables placement and use of a network node function in a second network node instead of using the network node function in a first network node. The network node function is e.g. a server function or a router function. The second network node is typically located in or close to the
Allocating resources between network nodes for providing a network node function

OpenAIRE

Strijkers, R.J.; Meulenhoff, P.J.

2014-01-01

The invention provides a method wherein a first network node advertises available resources that a second network node may use to offload network node functions transparently to the first network node. Examples of the first network node are a client device (e.g. PC, notebook, tablet, smart phone), a server (e.g. application server, a proxy server, cloud location, router). Examples of the second network node are an application server, a cloud location or a router. The available resources may b...
Network coding at different layers in wireless networks

CERN Document Server

2016-01-01

This book focuses on how to apply network coding at different layers in wireless networks – including MAC, routing, and TCP – with special focus on cognitive radio networks. It discusses how to select parameters in network coding (e.g., coding field, number of packets involved, and redundant information ration) in order to be suitable for the varying wireless environments. The book explores how to deploy network coding in MAC to improve network performance and examines joint network coding with opportunistic routing to improve the successful rate of routing. In regards to TCP and network coding, the text considers transport layer protocol working with network coding to overcome the transmission error rate, particularly with how to use the ACK feedback of TCP to enhance the efficiency of network coding. The book pertains to researchers and postgraduate students, especially whose interests are in opportunistic routing and TCP in cognitive radio networks.
Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

Science.gov (United States)

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of
Design of energy efficient optical networks with software enabled integrated control plane

DEFF Research Database (Denmark)

Wang, Jiayuan; Yan, Ying; Dittmann, Lars

2015-01-01

energy consumption by proposing a new integrated control plane structure utilising Software Defined Networking technologies. The integrated control plane increases the efficiencies of exchanging control information across different network domains, while introducing new possibilities to the routing...... methods and the control over quality of service (QoS). The structure is defined as an overlay generalised multi-protocol label switching (GMPLS) control model. With the defined structure, the integrated control plane is able to gather information from different domains (i.e. optical core network......'s) routing behaviours. With the flexibility of the routing structure, results show that the energy efficiency of the network can be improved without compromising the QoS for delay/blocking sensitive services....

Labeling Actors and Uncovering Causal Accounts of Their States in Social Networks and Social Media

Science.gov (United States)

Bui, Ngot P.

2016-01-01

The emergence of social networks and social media has resulted in exponential increase in the amount of data that link diverse types of richly structured digital objects e.g., individuals, articles, images, videos, music, etc. Such data are naturally represented as heterogeneous networks with multiple types of objects e.g., actors, video,…
Developing a Framework for E-Manufacturing Based on Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Xu Xi

2013-06-01

Full Text Available This paper analyzes the current situation of business environment and business intelligence systems integration at first. With emerging applications of internet and wireless communication technologies, e-manufacturing is focused on the use of internet, monitoring and communications technologies to make things happen collaboratively on a global basis. A wireless sensor network based data acquisition system gives enormous benefits such as ease and flexibility of deployment in addition to low maintenance and deployment costs. This paper reviews wireless sensor network and its application for e-manufacturing. To provide a dependable, non-intrusive, secure, real-time automated health monitoring, a distributed reconfigurable sensor network is introduced which consists of real and virtual sensor nodes over a communication wireless sensor network using Mica2 motes.
Robust Synchronization in an E/I Network with Medium Synaptic Delay and High Level of Heterogeneity

International Nuclear Information System (INIS)

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

2015-01-01

It is known that both excitatory and inhibitory neuronal networks can achieve robust synchronization only under certain conditions, such as long synaptic delay or low level of heterogeneity. In this work, robust synchronization can be found in an excitatory/inhibitory (E/I) neuronal network with medium synaptic delay and high level of heterogeneity, which often occurs in real neuronal networks. Two effects of post-synaptic potentials (PSP) to network synchronization are presented, and the synaptic contribution of excitatory and inhibitory neurons to robust synchronization in this E/I network is investigated. It is found that both excitatory and inhibitory neurons may contribute to robust synchronization in E/I networks, especially the excitatory PSP has a more positive effect on synchronization in E/I networks than that in excitatory networks. This may explain the strong robustness of synchronization in E/I neuronal networks. (paper)
A FDG-PET Study of Metabolic Networks in Apolipoprotein E ε4 Allele Carriers.

Directory of Open Access Journals (Sweden)

Zhijun Yao

Full Text Available Recently, some studies have applied the graph theory in brain network analysis in Alzheimer's disease (AD and Mild Cognitive Impairment (MCI. However, relatively little research has specifically explored the properties of the metabolic network in apolipoprotein E (APOE ε4 allele carriers. In our study, all the subjects, including ADs, MCIs and NCs (normal controls were divided into 165 APOE ε4 carriers and 165 APOE ε4 noncarriers. To establish the metabolic network for all brain regions except the cerebellum, cerebral glucose metabolism data obtained from FDG-PET (18F-fluorodeoxyglucose positron emission tomography were segmented into 90 areas with automated anatomical labeling (AAL template. Then, the properties of the networks were computed to explore the between-group differences. Our results suggested that both APOE ε4 carriers and noncarriers showed the small-world properties. Besides, compared with APOE ε4 noncarriers, the carriers showed a lower clustering coefficient. In addition, significant changes in 6 hub brain regions were found in between-group nodal centrality. Namely, compared with APOE ε4 noncarriers, significant decreases of the nodal centrality were found in left insula, right insula, right anterior cingulate, right paracingulate gyri, left cuneus, as well as significant increases in left paracentral lobule and left heschl gyrus in APOE ε4 carriers. Increased local short distance interregional correlations and disrupted long distance interregional correlations were found, which may support the point that the APOE ε4 carriers were more similar with AD or MCI in FDG uptake. In summary, the organization of metabolic network in APOE ε4 carriers indicated a less optimal pattern and APOE ε4 might be a risk factor for AD.
Temporal networks

Science.gov (United States)

Holme, Petter; Saramäki, Jari

2012-10-01

A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered
Towards transparent all-optical label-swapped networks: 40 Gbit/s ultra-fast dynamic wavelength routing using integrated devices

DEFF Research Database (Denmark)

Seoane, Jorge; Holm-Nielsen, Pablo Villanueva; Jeppesen, Palle

2006-01-01

All-optical routing of 40 Gbit/s 1.6 ns packets is demonstrated employing integrated devices based on SOA-MZIs. The scheme allows wavelength transparent operation and sub-nanosecond dynamic wavelength selection for future packet/label switched networks....
Working with NASA's OSS E/PO Support Network

Science.gov (United States)

Miner, E. D.; Lowes, L. L.

2001-11-01

With greater and greater emphasis on the inclusion of a public engagement component in all government-supported research funding, many members of the DPS are finding it difficult to find sufficient time and funding to develop a wide-reaching and effective E/PO program. NASA's Office of Space Science, over the last five years, has built a Support Network to assist its funded scientists to establish partnerships with local and/or national science formal or informal education organizations, who are anxious to connect with and use the expertise of space scientists. The OSS Support Network consists of four theme-based 'Forums,' including the Solar System Exploration (SSE) Forum, specifically designed for working with planetary scientists, and seven regional 'Brokers-Facilitators' who are more familiar with partnership and other potential avenues for involvement by scientists. The services provided by the Support Network are free to both the scientists and their potential partners and is not limited to NASA-funded scientists. In addition to its assistance to space scientists, the Support Network is involved in a number of other overarching efforts, including support of a Solar System Ambassador Program, a Solar System Educator Program, Space Place (web and e-mail science products for libraries and small planetariums and museums), an on-line Space Science Resource Directory, annual reports of Space Science E/PO activity, identifying and filling in 'holes' and 'over-populations' in a solar system E/PO product matrix of grade level versus product versus content, research on product effectiveness, and scientific and educational evaluation of space science products. Forum and Broker-Facilitator contact information is available at http://spacescience.nasa.gov/education/resources/ecosystem/index.htm. Handouts with additional information will be available at the meeting.
Modelling dendritic ecological networks in space: An integrated network perspective

Science.gov (United States)

Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger

2013-01-01

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...
Learning gene networks under SNP perturbations using eQTL datasets.

Directory of Open Access Journals (Sweden)

Lingxue Zhang

2014-02-01

Full Text Available The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network
Network configuration of global R&D networks

DEFF Research Database (Denmark)

Hansen, Zaza Nadja Lee; Srai, Jagjit Singh

2011-01-01

, network configuration of global R&D has tended to focus on strategic elements with limited attention given operational effectiveness, or to interfaces with downstream manufacturing operations. Within OM literature, the drivers of configuration of global networks within, engineering, production, supply...... to R&D networks emerged, e.g. product features were more prominent in R&D networks. Furthermore, the study has shown extensive interaction with other operations, including many downstream manufacturing operations. By extending the OM configuration concepts to the configuration of R&D networks......Companies are increasingly globalising their R&D activities, both within the firms and with external partners, with consequent implications for their interaction with manufacturing operations. Previous research in R&D networks has focused on coordination, governance and support elements. However...
Attractive target wave patterns in complex networks consisting of excitable nodes

International Nuclear Information System (INIS)

Zhang Li-Sheng; Mi Yuan-Yuan; Liao Xu-Hong; Qian Yu; Hu Gang

2014-01-01

This review describes the investigations of oscillatory complex networks consisting of excitable nodes, focusing on the target wave patterns or say the target wave attractors. A method of dominant phase advanced driving (DPAD) is introduced to reveal the dynamic structures in the networks supporting oscillations, such as the oscillation sources and the main excitation propagation paths from the sources to the whole networks. The target center nodes and their drivers are regarded as the key nodes which can completely determine the corresponding target wave patterns. Therefore, the center (say node A) and its driver (say node B) of a target wave can be used as a label, (A,B), of the given target pattern. The label can give a clue to conveniently retrieve, suppress, and control the target waves. Statistical investigations, both theoretically from the label analysis and numerically from direct simulations of network dynamics, show that there exist huge numbers of target wave attractors in excitable complex networks if the system size is large, and all these attractors can be labeled and easily controlled based on the information given by the labels. The possible applications of the physical ideas and the mathematical methods about multiplicity and labelability of attractors to memory problems of neural networks are briefly discussed. (topical review - statistical physics and complex systems)
Allocating resources between network nodes for providing a network node function

NARCIS (Netherlands)

Strijkers, R.J.; Meulenhoff, P.J.

2014-01-01

The invention provides a method wherein a first network node advertises available resources that a second network node may use to offload network node functions transparently to the first network node. Examples of the first network node are a client device (e.g. PC, notebook, tablet, smart phone), a
Locating a tree in a phylogenetic network

OpenAIRE

van Iersel, Leo; Semple, Charles; Steel, Mike

2010-01-01

Phylogenetic trees and networks are leaf-labelled graphs that are used to describe evolutionary histories of species. The Tree Containment problem asks whether a given phylogenetic tree is embedded in a given phylogenetic network. Given a phylogenetic network and a cluster of species, the Cluster Containment problem asks whether the given cluster is a cluster of some phylogenetic tree embedded in the network. Both problems are known to be NP-complete in general. In this article, we consider t...
Modelling dendritic ecological networks in space: anintegrated network perspective

Science.gov (United States)

Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.

2013-01-01

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within
Measurement methods on the complexity of network

Institute of Scientific and Technical Information of China (English)

LIN Lin; DING Gang; CHEN Guo-song

2010-01-01

Based on the size of network and the number of paths in the network,we proposed a model of topology complexity of a network to measure the topology complexity of the network.Based on the analyses of the effects of the number of the equipment,the types of equipment and the processing time of the node on the complexity of the network with the equipment-constrained,a complexity model of equipment-constrained network was constructed to measure the integrated complexity of the equipment-constrained network.The algorithms for the two models were also developed.An automatic generator of the random single label network was developed to test the models.The results show that the models can correctly evaluate the topology complexity and the integrated complexity of the networks.
Labelling subway lines

NARCIS (Netherlands)

Garrido, M.A.; Iturriaga, C.; Márquez, A.; Portillo, J.R.; Reyes, P.; Wolff, A.; Eades, P.; Takaoka, T.

2001-01-01

Graphical features on map, charts, diagrams and graph drawings usually must be annotated with text labels in order to convey their meaning. In this paper we focus on a problem that arises when labeling schematized maps, e.g. for subway networks. We present algorithms for labeling points on a line
Next Generation Network Routing and Control Plane

DEFF Research Database (Denmark)

Fu, Rong

proved, the dominating Border Gateway Protocol (BGP) cannot address all the issues that in inter-domain QoS routing. Thus a new protocol or network architecture has to be developed to be able to carry the inter-domain traffic with the QoS and TE consideration. Moreover, the current network control also...... lacks the ability to cooperate between different domains and operators. The emergence of label switching transport technology such as of Multi-Protocol Label Switching (MPLS) or Generalized MPLS (GMPLS) supports the traffic transport in a finer granularity and more dedicated end-to-end Quality...... (RACF) provides the platform that enables cooperation and ubiquitous integration between networks. In this paper, we investigate in the network architecture, protocols and algorithms for inter-domain QoS routing and traffic engineering. The PCE based inter-domain routing architecture is enhanced...
Virtualized cognitive network architecture for 5G cellular networks

KAUST Repository

Elsawy, Hesham

2015-07-17

Cellular networks have preserved an application agnostic and base station (BS) centric architecture1 for decades. Network functionalities (e.g. user association) are decided and performed regardless of the underlying application (e.g. automation, tactile Internet, online gaming, multimedia). Such an ossified architecture imposes several hurdles against achieving the ambitious metrics of next generation cellular systems. This article first highlights the features and drawbacks of such architectural ossification. Then the article proposes a virtualized and cognitive network architecture, wherein network functionalities are implemented via software instances in the cloud, and the underlying architecture can adapt to the application of interest as well as to changes in channels and traffic conditions. The adaptation is done in terms of the network topology by manipulating connectivities and steering traffic via different paths, so as to attain the applications\\' requirements and network design objectives. The article presents cognitive strategies to implement some of the classical network functionalities, along with their related implementation challenges. The article further presents a case study illustrating the performance improvement of the proposed architecture as compared to conventional cellular networks, both in terms of outage probability and handover rate.
QoE-Driven D2D Media Services Distribution Scheme in Cellular Networks

Directory of Open Access Journals (Sweden)

Mingkai Chen

2017-01-01

Full Text Available Device-to-device (D2D communication has been widely studied to improve network performance and considered as a potential technological component for the next generation communication. Considering the diverse users’ demand, Quality of Experience (QoE is recognized as a new degree of user’s satisfaction for media service transmissions in the wireless communication. Furthermore, we aim at promoting user’s Mean of Score (MOS value to quantify and analyze user’s QoE in the dynamic cellular networks. In this paper, we explore the heterogeneous media service distribution in D2D communications underlaying cellular networks to improve the total users’ QoE. We propose a novel media service scheme based on different QoE models that jointly solve the massive media content dissemination issue for cellular networks. Moreover, we also investigate the so-called Media Service Adaptive Update Scheme (MSAUS framework to maximize users’ QoE satisfaction and we derive the popularity and priority function of different media service QoE expression. Then, we further design Media Service Resource Allocation (MSRA algorithm to schedule limited cellular networks resource, which is based on the popularity function to optimize the total users’ QoE satisfaction and avoid D2D interference. In addition, numerical simulation results indicate that the proposed scheme is more effective in cellular network content delivery, which makes it suitable for various media service propagation.
Social Networking Services in E-Learning

Science.gov (United States)

Weber, Peter; Rothe, Hannes

2016-01-01

This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

Network maintenance

CERN Multimedia

GS Department

2009-01-01

A site-wide network maintenance operation has been scheduled for Saturday 28 February. Most of the network devices of the general purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites throughout the day. This upgrade will not affect the Computer Centre itself, Building 613, the Technical Network and the LHC experiments, dedicated networks at the pits. For further details of this intervention, please contact Netops by phone 74927 or e-mail mailto:Netops@cern.ch. IT/CS Group
Bayesian Network Webserver: a comprehensive tool for biological network modeling.

Science.gov (United States)

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.
Novel approaches to pin cluster synchronization on complex dynamical networks in Lur'e forms

Science.gov (United States)

Tang, Ze; Park, Ju H.; Feng, Jianwen

2018-04-01

This paper investigates the cluster synchronization of complex dynamical networks consisted of identical or nonidentical Lur'e systems. Due to the special topology structure of the complex networks and the existence of stochastic perturbations, a kind of randomly occurring pinning controller is designed which not only synchronizes all Lur'e systems in the same cluster but also decreases the negative influence among different clusters. Firstly, based on an extended integral inequality, the convex combination theorem and S-procedure, the conditions for cluster synchronization of identical Lur'e networks are derived in a convex domain. Secondly, randomly occurring adaptive pinning controllers with two independent Bernoulli stochastic variables are designed and then sufficient conditions are obtained for the cluster synchronization on complex networks consisted of nonidentical Lur'e systems. In addition, suitable control gains for successful cluster synchronization of nonidentical Lur'e networks are acquired by designing some adaptive updating laws. Finally, we present two numerical examples to demonstrate the validity of the control scheme and the theoretical analysis.
Introductory note on Emergent Unconscious Knowledge Networks (Asygnodic Networks

Directory of Open Access Journals (Sweden)

Henry Bakis

2016-05-01

Full Text Available This note introduces the following paper on the concept of Emergent Unconscious Knowledge Networks (Asygnodic Networks created by E. Roche and M. Blaine. The concept of asyngnosis explains a large number of diverse phenomena involving organizations, groups and decision making. It will present the genesis and definition of Asygnodic Networks and will focus on how they raise challenges to traditional theories of decision making and emerging social networks.
Towards a QoE-Driven Resource Control in LTE and LTE-A Networks

Directory of Open Access Journals (Sweden)

Gerardo Gómez

2013-01-01

Full Text Available We propose a novel architecture for providing quality of experience (QoE awareness to mobile operator networks. In particular, we describe a possible architecture for QoE-driven resource control for long-term evolution (LTE and LTE-advanced networks, including a selection of KPIs to be monitored in different network elements. We also provide a description and numerical results of the QoE evaluation process for different data services as well as potential use cases that would benefit from the rollout of the proposed framework.
Applying Physical-Layer Network Coding in Wireless Networks

Directory of Open Access Journals (Sweden)

Liew SoungChang

2010-01-01

Full Text Available A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11. This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.
Temporal networks

CERN Document Server

Saramäki, Jari

2013-01-01

The concept of temporal networks is an extension of complex networks as a modeling framework to include information on when interactions between nodes happen. Many studies of the last decade examine how the static network structure affect dynamic systems on the network. In this traditional approach the temporal aspects are pre-encoded in the dynamic system model. Temporal-network methods, on the other hand, lift the temporal information from the level of system dynamics to the mathematical representation of the contact network itself. This framework becomes particularly useful for cases where there is a lot of structure and heterogeneity both in the timings of interaction events and the network topology. The advantage compared to common static network approaches is the ability to design more accurate models in order to explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks and other spreading phenomena). On the other hand, temporal network methods are mathematically and concept...
Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

Science.gov (United States)

Rich, Scott; Zochowski, Michal; Booth, Victoria

2018-01-01

Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.
Hierarchical Network Design

DEFF Research Database (Denmark)

Thomadsen, Tommy

2005-01-01

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

CERN Multimedia

2007-01-01

In order to optimise the management of the Technical Network (TN), to facilitate understanding of the purpose of devices connected to the TN and to improve security incident handling, the Technical Network Administrators and the CNIC WG have asked IT/CS to verify the "description" and "tag" fields of devices connected to the TN. Therefore, persons responsible for systems connected to the TN will receive e-mails from IT/CS asking them to add the corresponding information in the network database at "network-cern-ch". Thank you very much for your cooperation. The Technical Network Administrators & the CNIC WG
E-learning objects and actor-networks as configuring information literacy teaching

DEFF Research Database (Denmark)

Schreiber, Trine Louise

2017-01-01

Introduction. With actor-network theory (ANT) as the theoretical lens the aim of the paper is to examine attempts to build network for shaping information literacy teaching. Method. The paper is based on a study of a project in 2014-2016 where information professionals representing ten educational...... libraries produced and implemented e-learning objects in information literacy teaching. The material was collected through interviews, observations, documents and feedback sessions. Analysis. Latour´s concept of translation and Callon´s four translation moments are used to analyze the network building...... that a network configuring information literacy teaching based on new interactive roles has not been stabilized. Conclusion. The paper concludes that the strength of ANT is first of all the mediation of an overview of different kinds of actors involved in network building. Further, the paper proposes to combine...
E-Services quality assessment framework for collaborative networks

Science.gov (United States)

Stegaru, Georgiana; Danila, Cristian; Sacala, Ioan Stefan; Moisescu, Mihnea; Mihai Stanescu, Aurelian

2015-08-01

In a globalised networked economy, collaborative networks (CNs) are formed to take advantage of new business opportunities. Collaboration involves shared resources and capabilities, such as e-Services that can be dynamically composed to automate CN participants' business processes. Quality is essential for the success of business process automation. Current approaches mostly focus on quality of service (QoS)-based service selection and ranking algorithms, overlooking the process of service composition which requires interoperable, adaptable and secure e-Services to ensure seamless collaboration, data confidentiality and integrity. Lack of assessment of these quality attributes can result in e-Service composition failure. The quality of e-Service composition relies on the quality of each e-Service and on the quality of the composition process. Therefore, there is the need for a framework that addresses quality from both views: product and process. We propose a quality of e-Service composition (QoESC) framework for quality assessment of e-Service composition for CNs which comprises of a quality model for e-Service evaluation and guidelines for quality of e-Service composition process. We implemented a prototype considering a simplified telemedicine use case which involves a CN in e-Healthcare domain. To validate the proposed quality-driven framework, we analysed service composition reliability with and without using the proposed framework.
Resource aware sensor nodes in wireless sensor networks

International Nuclear Information System (INIS)

Merrett, G V; Al-Hashimi, B M; White, N M; Harris, N R

2005-01-01

Wireless sensor networks are continuing to receive considerable research interest due, in part, to the range of possible applications. One of the greatest challenges facing researchers is in overcoming the limited network lifetime inherent in the small locally powered sensor nodes. In this paper, we propose IDEALS, a system to manage a wireless sensor network using a combination of information management, energy harvesting and energy monitoring, which we label resource awareness. Through this, IDEALS is able to extend the network lifetime for important messages, by controlling the degradation of the network to maximise information throughput
Vulnerability and controllability of networks of networks

International Nuclear Information System (INIS)

Liu, Xueming; Peng, Hao; Gao, Jianxi

2015-01-01

Network science is a highly interdisciplinary field ranging from natural science to engineering technology and it has been applied to model complex systems and used to explain their behaviors. Most previous studies have been focus on isolated networks, but many real-world networks do in fact interact with and depend on other networks via dependency connectivities, forming “networks of networks” (NON). The interdependence between networks has been found to largely increase the vulnerability of interacting systems, when a node in one network fails, it usually causes dependent nodes in other networks to fail, which, in turn, may cause further damage on the first network and result in a cascade of failures with sometimes catastrophic consequences, e.g., electrical blackouts caused by the interdependence of power grids and communication networks. The vulnerability of a NON can be analyzed by percolation theory that can be used to predict the critical threshold where a NON collapses. We review here the analytic framework for analyzing the vulnerability of NON, which yields novel percolation laws for n-interdependent networks and also shows that percolation theory of a single network studied extensively in physics and mathematics in the last 50 years is a specific limited case of the more general case of n interacting networks. Understanding the mechanism behind the cascading failure in NON enables us finding methods to decrease the vulnerability of the natural systems and design of more robust infrastructure systems. By examining the vulnerability of NON under targeted attack and studying the real interdependent systems, we find two methods to decrease the systems vulnerability: (1) protect the high-degree nodes, and (2) increase the degree correlation between networks. Furthermore, the ultimate proof of our understanding of natural and technological systems is reflected in our ability to control them. We also review the recent studies and challenges on the
Design And Planning Of E- Learning EnvironmentE-Education System On Heterogeneous Wireless Network Control System

Directory of Open Access Journals (Sweden)

ThandarOo

2015-06-01

Full Text Available Abstract The purpose of this research is to provide a more efficient and effective communication method between teacher and student with the use of heterogeneous network. Moreover the effective use of heterogeneous network can be emphasized. The system of e-education can develop utilizing wireless network.The e-Education system can help students to communicate with their teacher more easily and effectively using a heterogeneous wireless network system. In this wireless network system students who are blind or dumb will also be able to communicate and learn from the teacher as normal students can do. All the devices or laptops will be connected on wireless LAN. Even when the teacher is not around he will be able to help his students with their study or give instructions easily by using the mobile phone to send text or voice signal. When the teacher sends information to the dumb student it will be converted into sign language for the student to be able to understand. When the dumb student sends the information to the teacher it will be converted into text for the teacher to understand. For the blind student text instructions from the teacher will be converted into audio signal using text-to-speech conversion.Thus the performance of heterogeneous wireless network model can evaluate by using Robust Optimization Method. Therefore the e-Education systems performance improves by evaluating Robust Optimization Method.
Achieving Network Level Privacy in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sungyoung Lee

2010-02-01

Full Text Available Full network level privacy has often been categorized into four sub-categories: Identity, Route, Location and Data privacy. Achieving full network level privacy is a critical and challenging problem due to the constraints imposed by the sensor nodes (e.g., energy, memory and computation power, sensor networks (e.g., mobility and topology and QoS issues (e.g., packet reach-ability and timeliness. In this paper, we proposed two new identity, route and location privacy algorithms and data privacy mechanism that addresses this problem. The proposed solutions provide additional trustworthiness and reliability at modest cost of memory and energy. Also, we proved that our proposed solutions provide protection against various privacy disclosure attacks, such as eavesdropping and hop-by-hop trace back attacks.
Recent advances on failure and recovery in networks of networks

International Nuclear Information System (INIS)

Shekhtman, Louis M.; Danziger, Michael M.; Havlin, Shlomo

2016-01-01

Until recently, network science has focused on the properties of single isolated networks that do not interact or depend on other networks. However it has now been recognized that many real-networks, such as power grids, transportation systems, and communication infrastructures interact and depend on other networks. Here, we will present a review of the framework developed in recent years for studying the vulnerability and recovery of networks composed of interdependent networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes, like for example certain people, play a role in two networks, i.e. in a multiplex. Dependency relations may act recursively and can lead to cascades of failures concluding in sudden fragmentation of the system. We review the analytical solutions for the critical threshold and the giant component of a network of n interdependent networks. The general theory and behavior of interdependent networks has many novel features that are not present in classical network theory. Interdependent networks embedded in space are significantly more vulnerable compared to non-embedded networks. In particular, small localized attacks may lead to cascading failures and catastrophic consequences. Finally, when recovery of components is possible, global spontaneous recovery of the networks and hysteresis phenomena occur. The theory developed for this process points to an optimal repairing strategy for a network of networks. Understanding realistic effects present in networks of networks is required in order to move towards determining system vulnerability.
In-Network Adaptation of Video Streams Using Network Processors

Directory of Open Access Journals (Sweden)

Mohammad Shorfuzzaman

2009-01-01

problem can be addressed, near the network edge, by applying dynamic, in-network adaptation (e.g., transcoding of video streams to meet available connection bandwidth, machine characteristics, and client preferences. In this paper, we extrapolate from earlier work of Shorfuzzaman et al. 2006 in which we implemented and assessed an MPEG-1 transcoding system on the Intel IXP1200 network processor to consider the feasibility of in-network transcoding for other video formats and network processor architectures. The use of “on-the-fly” video adaptation near the edge of the network offers the promise of simpler support for a wide range of end devices with different display, and so forth, characteristics that can be used in different types of environments.
The Y.E.S. Network: An IYPE legacy for engaging future generations of early-career geoscientists

Science.gov (United States)

Gonzales, L. M.; Govoni, D.; Micucci, L.; Gaines, S. M.; Venus, J.; Meng, W.

2009-12-01

The Y.E.S. Network, an association of early-career geoscientists who represent professional societies, geoscience companies, and geoscience departments from across the world, was formed as a direct result of the International Year of Planet Earth (IYPE). Currently the Y.E.S. Network has representatives in thirty-five countries from six continents. The goal of the network is to engage early-career representatives from geological associations and institutions, policy-makers, and delegates from administrative bodies to establish a worldwide network of future leaders, policy-makers and geoscientists who will work collaboratively to address the scientific challenges future generations will face. To this end, the Y.E.S. Network, in collaboration with IYPE and with the patronage of UNESCO, organized the first international Y.E.S. Congress which was hosted by the China University of Geosciences in Beijing. The conference focused on scientific and career challenges faced by early-career geoscientists, with a particular emphasis on how the Y.E.S. Network can work collaborative and internationally towards solving these challenges and furthering the IYPE motto of “Earth Sciences for Society”. The conference focused on the ten major themes of the IYPE (e.g. health, climate, groundwater, ocean, soils, deep earth, megacities, hazards, resources, and life) at its poster and oral sessions. Roundtable symposia engaged senior and early-career geoscientists via presentations, panel discussions, and working group sessions where strategies related to scientific challenges (i.e. climate change in the polar regions, natural hazards, natural resource sustainability) and academic and career pathway challenges (i.e. academic-industry linkages, gender parity in the geosciences, geoscience education sustainability, and international licensure issues) were developed. These strategies were then tasked to the Y.E.S. Network for further development and implementation. Future Y.E.S. Network
Dominating biological networks.

Directory of Open Access Journals (Sweden)

Tijana Milenković

Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

Future High Capacity Backbone Networks

DEFF Research Database (Denmark)

Wang, Jiayuan

are proposed. The work focuses on energy efficient routing algorithms in a dynamic optical core network environment, with Generalized MultiProtocol Label Switching (GMPLS) as the control plane. Energy ef- ficient routing algorithms for energy savings and CO2 savings are proposed, and their performance...... aiming for reducing the dynamic part of the energy consumption of the network may increase the fixed part of the energy consumption meanwhile. In the second half of the thesis, the conflict between energy efficiency and Quality of Service (QoS) is addressed by introducing a novel software defined......This thesis - Future High Capacity Backbone Networks - deals with the energy efficiency problems associated with the development of future optical networks. In the first half of the thesis, novel approaches for using multiple/single alternative energy sources for improving energy efficiency...
Network Analysis Tools: from biological networks to clusters and pathways.

Science.gov (United States)

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.
Effective Fusion of Multi-Modal Remote Sensing Data in a Fully Convolutional Network for Semantic Labeling

Directory of Open Access Journals (Sweden)

Wenkai Zhang

2017-12-01

Full Text Available In recent years, Fully Convolutional Networks (FCN have led to a great improvement of semantic labeling for various applications including multi-modal remote sensing data. Although different fusion strategies have been reported for multi-modal data, there is no in-depth study of the reasons of performance limits. For example, it is unclear, why an early fusion of multi-modal data in FCN does not lead to a satisfying result. In this paper, we investigate the contribution of individual layers inside FCN and propose an effective fusion strategy for the semantic labeling of color or infrared imagery together with elevation (e.g., Digital Surface Models. The sensitivity and contribution of layers concerning classes and multi-modal data are quantified by recall and descent rate of recall in a multi-resolution model. The contribution of different modalities to the pixel-wise prediction is analyzed explaining the reason of the poor performance caused by the plain concatenation of different modalities. Finally, based on the analysis an optimized scheme for the fusion of layers with image and elevation information into a single FCN model is derived. Experiments are performed on the ISPRS Vaihingen 2D Semantic Labeling dataset (infrared and RGB imagery as well as elevation and the Potsdam dataset (RGB imagery and elevation. Comprehensive evaluations demonstrate the potential of the proposed approach.
Technologies for Home Networking

DEFF Research Database (Denmark)

A broad overview of the home networking field, ranging from wireless technologies to practical applications. In the future, it is expected that private networks (e.g. home networks) will become part of the global network ecosystem, participating in sharing their own content, running IP...
Enabling software defined networking experiments in networked critical infrastructures

Directory of Open Access Journals (Sweden)

Béla Genge

2014-05-01

Full Text Available Nowadays, the fact that Networked Critical Infrastructures (NCI, e.g., power plants, water plants, oil and gas distribution infrastructures, and electricity grids, are targeted by significant cyber threats is well known. Nevertheless, recent research has shown that specific characteristics of NCI can be exploited in the enabling of more efficient mitigation techniques, while novel techniques from the field of IP networks can bring significant advantages. In this paper we explore the interconnection of NCI communication infrastructures with Software Defined Networking (SDN-enabled network topologies. SDN provides the means to create virtual networking services and to implement global networking decisions. It relies on OpenFlow to enable communication with remote devices and has been recently categorized as the “Next Big Technology”, which will revolutionize the way decisions are implemented in switches and routers. Therefore, the paper documents the first steps towards enabling an SDN-NCI and presents the impact of a Denial of Service experiment over traffic resulting from an XBee sensor network which is routed across an emulated SDN network.
Resilience of networks to environmental stress: From regular to random networks

Science.gov (United States)

Eom, Young-Ho

2018-04-01

Despite the huge interest in network resilience to stress, most of the studies have concentrated on internal stress damaging network structure (e.g., node removals). Here we study how networks respond to environmental stress deteriorating their external conditions. We show that, when regular networks gradually disintegrate as environmental stress increases, disordered networks can suddenly collapse at critical stress with hysteresis and vulnerability to perturbations. We demonstrate that this difference results from a trade-off between node resilience and network resilience to environmental stress. The nodes in the disordered networks can suppress their collapses due to the small-world topology of the networks but eventually collapse all together in return. Our findings indicate that some real networks can be highly resilient against environmental stress to a threshold yet extremely vulnerable to the stress above the threshold because of their small-world topology.
Psychology and social networks: a dynamic network theory perspective.

Science.gov (United States)

Westaby, James D; Pfaff, Danielle L; Redding, Nicholas

2014-04-01

Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Trust Building Mechanisms for Electronic Business Networks and Their Relation to eSkills

OpenAIRE

Radoslav Delina; Michal Tkáč

2010-01-01

Globalization, supported by information and communication technologies, changes the rules of competitiveness and increases the significance of information, knowledge and network cooperation. In line with this trend, the need for efficient trust-building tools has emerged. The absence of trust building mechanisms and strategies was identified within several studies. Through trust development, participation on e-business network and usage of network services will increase a...
How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

Science.gov (United States)

Kim, Jinyoung

2017-12-01

As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.
Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

Science.gov (United States)

Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2017-03-01

In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.
Network chemistry, network toxicology, network informatics, and network behavioristics: A scientific outline

OpenAIRE

WenJun Zhang

2016-01-01

In present study, I proposed some new sciences: network chemistry, network toxicology, network informatics, and network behavioristics. The aims, scope and scientific foundation of these sciences are outlined.
Online and Offline Social Networks: Use of Social Networking Sites by Emerging Adults

Science.gov (United States)

Subrahmanyam, Kaveri; Reich, Stephanie M.; Waechter, Natalia; Espinoza, Guadalupe

2008-01-01

Social networking sites (e.g., MySpace and Facebook) are popular online communication forms among adolescents and emerging adults. Yet little is known about young people's activities on these sites and how their networks of "friends" relate to their other online (e.g., instant messaging) and offline networks. In this study, college students…
Finding quasi-optimal network topologies for information transmission in active networks.

Science.gov (United States)

Baptista, Murilo S; de Carvalho, Josué X; Hussein, Mahir S

2008-01-01

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.
Finding quasi-optimal network topologies for information transmission in active networks.

Directory of Open Access Journals (Sweden)

Murilo S Baptista

Full Text Available This work clarifies the relation between network circuit (topology and behaviour (information transmission and synchronization in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.
Static analysis of topology-dependent broadcast networks

DEFF Research Database (Denmark)

Nanz, Sebastian; Nielson, Flemming; Nielson, Hanne Riis

2010-01-01

changing network topology is a crucial ingredient. In this paper, we develop a static analysis that automatically constructs an abstract transition system, labelled by actions and connectivity information, to yield a mobility-preserving finite abstraction of the behaviour of a network expressed......Broadcast semantics poses significant challenges over point-to-point communication when it comes to formal modelling and analysis. Current approaches to analysing broadcast networks have focused on fixed connectivities, but this is unsuitable in the case of wireless networks where the dynamically...... in a process calculus with asynchronous local broadcast. Furthermore, we use model checking based on a 3-valued temporal logic to distinguish network behaviour which differs under changing connectivity patterns. (C) 2009 Elsevier Inc. All rights reserved....
Heterogeneous network architectures

DEFF Research Database (Denmark)

Christiansen, Henrik Lehrmann

2006-01-01

is flexibility. This thesis investigates such heterogeneous network architectures and how to make them flexible. A survey of algorithms for network design is presented, and it is described how using heuristics can increase the speed. A hierarchical, MPLS based network architecture is described......Future networks will be heterogeneous! Due to the sheer size of networks (e.g., the Internet) upgrades cannot be instantaneous and thus heterogeneity appears. This means that instead of trying to find the olution, networks hould be designed as being heterogeneous. One of the key equirements here...... and it is discussed that it is advantageous to heterogeneous networks and illustrated by a number of examples. Modeling and simulation is a well-known way of doing performance evaluation. An approach to event-driven simulation of communication networks is presented and mixed complexity modeling, which can simplify...
Directed network modules

International Nuclear Information System (INIS)

Palla, Gergely; Farkas, Illes J; Pollner, Peter; Derenyi, Imre; Vicsek, Tamas

2007-01-01

A search technique locating network modules, i.e. internally densely connected groups of nodes in directed networks is introduced by extending the clique percolation method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Renyi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own web-pages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and Google's web-pages, overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory network tend to overlap via out-hubs
Holographic spin networks from tensor network states

Science.gov (United States)

Singh, Sukhwinder; McMahon, Nathan A.; Brennen, Gavin K.

2018-01-01

In the holographic correspondence of quantum gravity, a global on-site symmetry at the boundary generally translates to a local gauge symmetry in the bulk. We describe one way how the global boundary on-site symmetries can be gauged within the formalism of the multiscale renormalization ansatz (MERA), in light of the ongoing discussion between tensor networks and holography. We describe how to "lift" the MERA representation of the ground state of a generic one dimensional (1D) local Hamiltonian, which has a global on-site symmetry, to a dual quantum state of a 2D "bulk" lattice on which the symmetry appears gauged. The 2D bulk state decomposes in terms of spin network states, which label a basis in the gauge-invariant sector of the bulk lattice. This decomposition is instrumental to obtain expectation values of gauge-invariant observables in the bulk, and also reveals that the bulk state is generally entangled between the gauge and the remaining ("gravitational") bulk degrees of freedom that are not fixed by the symmetry. We present numerical results for ground states of several 1D critical spin chains to illustrate that the bulk entanglement potentially depends on the central charge of the underlying conformal field theory. We also discuss the possibility of emergent topological order in the bulk using a simple example, and also of emergent symmetries in the nongauge (gravitational) sector in the bulk. More broadly, our holographic model translates the MERA, a tensor network state, to a superposition of spin network states, as they appear in lattice gauge theories in one higher dimension.
Adaptive Suspicious Prevention for Defending DoS Attacks in SDN-Based Convergent Networks.

Science.gov (United States)

Dao, Nhu-Ngoc; Kim, Joongheon; Park, Minho; Cho, Sungrae

2016-01-01

The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switching (MPLS) technique. Software-defined networking (SDN), on the other hand, is expected to be the ideal future for the convergent network since it can provide a controllable, dynamic, and cost-effective network. However, SDN has an original structural vulnerability behind a lot of advantages, which is the centralized control plane. As the brains of the network, a controller manages the whole network, which is attractive to attackers. In this context, we proposes a novel solution called adaptive suspicious prevention (ASP) mechanism to protect the controller from the Denial of Service (DoS) attacks that could incapacitate an SDN. The ASP is integrated with OpenFlow protocol to detect and prevent DoS attacks effectively. Our comprehensive experimental results show that the ASP enhances the resilience of an SDN network against DoS attacks by up to 38%.
Europeans build 10-GigE network

CERN Multimedia

2003-01-01

" CERN, SURFnet and the University of Amsterdam announced that they have succeeded in building and testing a Trans European 10 Gbps Ethernet (10 GE) network. Crossing four countries and spanning 1700 km, the network uses the new 10 GE WAN PHY transmission technology capable of transmitting the equivalent of 1.5 complete data CDs every second" (1 page).

The cell wall of Arabidopsis thaliana influences actin network dynamics.

Science.gov (United States)

Tolmie, Frances; Poulet, Axel; McKenna, Joseph; Sassmann, Stefan; Graumann, Katja; Deeks, Michael; Runions, John

2017-07-20

In plant cells, molecular connections link the cell wall-plasma membrane-actin cytoskeleton to form a continuum. It is hypothesized that the cell wall provides stable anchor points around which the actin cytoskeleton remodels. Here we use live cell imaging of fluorescently labelled marker proteins to quantify the organization and dynamics of the actin cytoskeleton and to determine the impact of disrupting connections within the continuum. Labelling of the actin cytoskeleton with green fluorescent protein (GFP)-fimbrin actin-binding domain 2 (FABD2) resulted in a network composed of fine filaments and thicker bundles that appeared as a highly dynamic remodelling meshwork. This differed substantially from the GFP-Lifeact-labelled network that appeared much more sparse with thick bundles that underwent 'simple movement', in which the bundles slightly change position, but in such a manner that the structure of the network was not substantially altered during the time of observation. Label-dependent differences in actin network morphology and remodelling necessitated development of two new image analysis techniques. The first of these, 'pairwise image subtraction', was applied to measurement of the more rapidly remodelling actin network labelled with GFP-FABD2, while the second, 'cumulative fluorescence intensity', was used to measure bulk remodelling of the actin cytoskeleton when labelled with GFP-Lifeact. In each case, these analysis techniques show that the actin cytoskeleton has a decreased rate of bulk remodelling when the cell wall-plasma membrane-actin continuum is disrupted either by plasmolysis or with isoxaben, a drug that specifically inhibits cellulose deposition. Changes in the rate of actin remodelling also affect its functionality, as observed by alteration in Golgi body motility. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
How has climate change altered network connectivity in a mountain stream network?

Science.gov (United States)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.; Johnson, S.

2017-12-01

Connectivity along river networks is broadly recognized as dynamic, with seasonal and event-based expansion and contraction of the network extent. Intermittently flowing streams are particularly important as they define a crucial threshold for continuously connected waters that enable migration by aquatic species. In the Pacific northwestern U.S., changes in atmospheric circulation have been found to alter rainfall patterns and result in decreased summer low-flows in the region. However, the impact of this climate dynamic on network connectivity is heretofore unstudied. Thus, we ask: How has connectivity in the riparian corridor changed in response to observed changes in climate? In this study we take the well-studied H.J. Andrews Experimental Forest as representative of mountain river networks in the Pacific northwestern U.S. First, we analyze 63 years of stream gauge information from a network of 11 gauges to document observed changes in timing and magnitude of stream discharge. We found declining magnitudes of seasonal low-flows and shifting seasonality of water export from the catchment, both of which we attribute to changes in precipitation timing and storage as snow vs. rainfall. Next, we use these discharge data to drive a reduced-complexity model of the river network to simulate network connectivity over 63 years. Model results show that network contraction (i.e., minimum network extent) has decreased over the past 63 years. Unexpectedly, the increasing winter peak flows did not correspond with increasing network expansion, suggesting a geologic control on maximum flowing network extent. We find dynamic expansion and contraction of the network primarily occurs during period of catchment discharge less than about 1 m3/s at the outlet, whereas the network extent is generally constant for discharges from 1 to 300 m3/s. Results of our study are of interest to scientists focused on connectivity as a control on ecological processes both directly (e.g., fish
Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

Directory of Open Access Journals (Sweden)

Stavros I. Dimitriadis

2017-12-01

Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node
Verification of mobile ad hoc networks : an algebraic approach

NARCIS (Netherlands)

Ghassemi, F.; Fokkink, W.J.; Movaghar, A.

2011-01-01

We introduced Computed Network Process Theory to reason about protocols for mobile ad hoc networks (MANETs). Here we explore the applicability of our framework in two regards: model checking and equational reasoning. The operational semantics of our framework is based on constrained labeled
Network effects on scientific collaborations.

Directory of Open Access Journals (Sweden)

Shahadat Uddin

Full Text Available BACKGROUND: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. METHODOLOGY/PRINCIPAL FINDINGS: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality, we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count and formation (tie strength between authors of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of 'steel structure' for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s. Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations. CONCLUSIONS/SIGNIFICANCE: Authors' network positions in co
Security Analysis of a Software Defined Wide Area Network Solution

OpenAIRE

Rajendran, Ashok

2016-01-01

Enterprise wide area network (WAN) is a private network that connects the computers and other devices across an organisation's branch locations and the data centers. It forms the backbone of enterprise communication. Currently, multiprotocol label switching (MPLS) is commonly used to provide this service. As a recent alternative to MPLS, software-dened wide area networking (SD-WAN) solutions are being introduced as an IP based cloud-networking service for enterprises. SD-WAN virtualizes the n...
Design issues of optical router for metropolitan optical network (MON) applications

Science.gov (United States)

Wei, Wei; Zeng, QingJi

2001-10-01

The popularity of the Internet has caused the traffic on the Metro Area Network (MAN) to grow drastically every year. It is believed that Wavelength Division Multiplexing (WDM) has become a cornerstone technology in the MAN. Solutions to provide a MAN with high bandwidth, good scalability and easy management are being constantly searched from both IP and WDM. In this paper we firstly propose a metro optical network architecture based on GMPLS--a flexible, highly scalable IP over WDM optical network architecture for the delivery of public network IP services. Two kinds of node including Electronic Label Switching Router (E-LSR) and Optical Router (O-LSR) are involved in this metro optical network architecture. Secondly, we mainly focus on design issues of OR including multi-granularity electro-optical hybrid switching fabrics, intelligent OTU, contro l plane software and etc. And we also discuss some issues such as routing, forwarding and management of OR. Finally, we reach conclusions that OR based on GMPLS and hybrid-switching fabrics is suitable for current multi-services application environment of MON and optimistic for IP traffic transfer.
Improved Extension Neural Network and Its Applications

Directory of Open Access Journals (Sweden)

Yu Zhou

2014-01-01

Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.
Evolutionary analysis of apolipoprotein E by Maximum Likelihood and complex network methods

Directory of Open Access Journals (Sweden)

Leandro de Jesus Benevides

Full Text Available Abstract Apolipoprotein E (apo E is a human glycoprotein with 299 amino acids, and it is a major component of very low density lipoproteins (VLDL and a group of high-density lipoproteins (HDL. Phylogenetic studies are important to clarify how various apo E proteins are related in groups of organisms and whether they evolved from a common ancestor. Here, we aimed at performing a phylogenetic study on apo E carrying organisms. We employed a classical and robust method, such as Maximum Likelihood (ML, and compared the results using a more recent approach based on complex networks. Thirty-two apo E amino acid sequences were downloaded from NCBI. A clear separation could be observed among three major groups: mammals, fish and amphibians. The results obtained from ML method, as well as from the constructed networks showed two different groups: one with mammals only (C1 and another with fish (C2, and a single node with the single sequence available for an amphibian. The accordance in results from the different methods shows that the complex networks approach is effective in phylogenetic studies. Furthermore, our results revealed the conservation of apo E among animal groups.
Robust Learning of High-dimensional Biological Networks with Bayesian Networks

Science.gov (United States)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.
Medical image segmentation by means of constraint satisfaction neural network

International Nuclear Information System (INIS)

Chen, C.T.; Tsao, C.K.; Lin, W.C.

1990-01-01

This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency
Activity of cardiorespiratory networks revealed by transsynaptic virus expressing GFP.

Science.gov (United States)

Irnaten, M; Neff, R A; Wang, J; Loewy, A D; Mettenleiter, T C; Mendelowitz, D

2001-01-01

A fluorescent transneuronal marker capable of labeling individual neurons in a central network while maintaining their normal physiology would permit functional studies of neurons within entire networks responsible for complex behaviors such as cardiorespiratory reflexes. The Bartha strain of pseudorabies virus (PRV), an attenuated swine alpha herpesvirus, can be used as a transsynaptic marker of neural circuits. Bartha PRV invades neuronal networks in the CNS through peripherally projecting axons, replicates in these parent neurons, and then travels transsynaptically to continue labeling the second- and higher-order neurons in a time-dependent manner. A Bartha PRV mutant that expresses green fluorescent protein (GFP) was used to visualize and record from neurons that determine the vagal motor outflow to the heart. Here we show that Bartha PRV-GFP-labeled neurons retain their normal electrophysiological properties and that the labeled baroreflex pathways that control heart rate are unaltered by the virus. This novel transynaptic virus permits in vitro studies of identified neurons within functionally defined neuronal systems including networks that mediate cardiovascular and respiratory function and interactions. We also demonstrate superior laryngeal motorneurons fire spontaneously and synapse on cardiac vagal neurons in the nucleus ambiguus. This cardiorespiratory pathway provides a neural basis of respiratory sinus arrhythmias.
Robustness of weighted networks

Science.gov (United States)

Bellingeri, Michele; Cassi, Davide

2018-01-01

Complex network response to node loss is a central question in different fields of network science because node failure can cause the fragmentation of the network, thus compromising the system functioning. Previous studies considered binary networks where the intensity (weight) of the links is not accounted for, i.e. a link is either present or absent. However, in real-world networks the weights of connections, and thus their importance for network functioning, can be widely different. Here, we analyzed the response of real-world and model networks to node loss accounting for link intensity and the weighted structure of the network. We used both classic binary node properties and network functioning measure, introduced a weighted rank for node importance (node strength), and used a measure for network functioning that accounts for the weight of the links (weighted efficiency). We find that: (i) the efficiency of the attack strategies changed using binary or weighted network functioning measures, both for real-world or model networks; (ii) in some cases, removing nodes according to weighted rank produced the highest damage when functioning was measured by the weighted efficiency; (iii) adopting weighted measure for the network damage changed the efficacy of the attack strategy with respect the binary analyses. Our results show that if the weighted structure of complex networks is not taken into account, this may produce misleading models to forecast the system response to node failure, i.e. consider binary links may not unveil the real damage induced in the system. Last, once weighted measures are introduced, in order to discover the best attack strategy, it is important to analyze the network response to node loss using nodes rank accounting the intensity of the links to the node.
[Social networks in drinking behaviors among Japanese: support network, drinking network, and intervening network].

Science.gov (United States)

Yoshihara, Chika; Shimizu, Shinji

2005-10-01

The national representative sample was analyzed to examine the relationship between respondents' drinking practice and the social network which was constructed of three different types of network: support network, drinking network, and intervening network. Non-parametric statistical analysis was conducted with chi square method and ANOVA analysis, due to the risk of small samples in some basic tabulation cells. The main results are as follows: (1) In the support network of workplace associates, moderate drinkers enjoyed much more sociable support care than both nondrinkers and hard drinkers, which might suggest a similar effect as the French paradox. Meanwhile in the familial and kinship network, the more intervening care support was provided, the harder respondents' drinking practice. (2) The drinking network among Japanese people for both sexes is likely to be convergent upon certain types of network categories and not decentralized in various categories. This might reflect of the drinking culture of Japan, which permits people to drink everyday as a practice, especially male drinkers. Subsequently, solitary drinking is not optional for female drinkers. (3) Intervening network analysis showed that the harder the respondents' drinking practices, the more frequently their drinking behaviors were checked in almost all the categories of network. A rather complicated gender double-standard was found in the network of hard drinkers with their friends, particularly for female drinkers. Medical professionals played a similar intervening role for men as family and kinship networks but to a less degree than friends for females. The social network is considerably associated with respondents' drinking, providing both sociability for moderate drinkers and intervention for hard drinkers, depending on network categories. To minimize the risk of hard drinking and advance self-healthy drinking there should be more research development on drinking practice and the social network.
Epidemics scenarios in the "Romantic network".

Directory of Open Access Journals (Sweden)

Alexsandro M Carvalho

Full Text Available The networks of sexual contacts together with temporal interactions play key roles in the spread of sexually transmitted infections. Unfortunately, data for this kind of network is scarce. One of the few exceptions, the "Romantic network", is a complete structure of a real sexual network in a high school. Based on many network measurements the authors of the work have concluded that it does not correspond to any other model network. Regarding the temporal structure, several studies indicate that relationship timing can have an effect on the diffusion throughout networks, as relationship order determines transmission routes. The aim is to check if the particular structure, static and dynamic, of the Romantic network is determinant for the propagation of an STI. We performed simulations in two scenarios: the static network where all contacts are available and the dynamic case where contacts evolve over time. In the static case, we compared the epidemic results in the Romantic network with some paradigmatic topologies. In the dynamic scenario, we considered the dynamics of formation of pairs in the Romantic network and we studied the propagation of the diseases. Our results suggest that although this real network cannot be labeled as a Watts-Strogatz network, it is, in regard to the propagation of an STI, very similar to a high disorder network. Additionally, we found that: the effect that any individual contacting an externally infected subject is to make the network closer to a fully connected one, the higher the contact degree of patient zero the faster the spread of the outbreaks, and the epidemic impact is proportional to the numbers of contacts per unit time. Finally, our simulations confirm that relationship timing severely reduced the final outbreak size, and also, show a clear correlation between the average degree and the outbreak size over time.
Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo

2015-09-15

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.
Control Plane Strategies for Elastic Optical Networks

DEFF Research Database (Denmark)

Turus, Ioan

Networks (EONs) concept is proposed as a solution to enable a more flexible handling of the optical capacity and allows an increase of available capacity over the existing optical infrastructure. One main requirement for enabling EONs is to have a flexible spectrum structure (i.e.Flex-Grid) which allows...... the spectrum to be used as an on-demand resource. Flex-Grid raises new challenges for controlling the dynamic spectrum slots environment. This thesis addresses, as part of the Celtic project “Elastic Optical Networks” (EONet), the control of Flex-Grid architectures by extending the capabilities of a GMPLS...... (Generalized Multi-Protocol Label Switching)-based control framework in accordance with existing IETF standards and recommendations. The usual approach of extending capacity in transport networks by incrementally adding more optical resources results in a very inefficient usage and determines a high power...
Revisiting Network Organization in Practice

DEFF Research Database (Denmark)

Hu, Yimei; Sørensen, Olav Jull

of networks in a network organization, which are internal market, IT networks, informal and social networks, global R&D project networks, global R&D specialists’ network, and alliances with external partners. Though the case TNCs are network-based, hierarchies remain to be an important part...... of the organizational designs, which we refer to duality of organization. In terms of duality of organization, there are three emerging patterns of duality, i.e. market-led, value-led and directed network organization. More important, we find that an organization is not only dual but also ternary since...
Software Defined Networking

DEFF Research Database (Denmark)

Caba, Cosmin Marius

Network Service Providers (NSP) often choose to overprovision their networks instead of deploying proper Quality of Services (QoS) mechanisms that allow for traffic differentiation and predictable quality. This tendency of overprovisioning is not sustainable for the simple reason that network...... resources are limited. Hence, to counteract this trend, current QoS mechanisms must become simpler to deploy and operate, in order to motivate NSPs to employ QoS techniques instead of overprovisioning. Software Defined Networking (SDN) represents a paradigm shift in the way telecommunication and data...... generic perspective (e.g. service provisioning speed, resources availability). As a result, new mechanisms for providing QoS are proposed, solutions for SDN-specific QoS challenges are designed and tested, and new network management concepts are prototyped, all aiming to improve QoS for network services...
The research and implementation of a unified identity authentication in e-government network

Science.gov (United States)

Feng, Zhou

Current problem existing in e-government network is that the applications of information system are developed independently by various departments, and each has its own specific set of authentication and access control mechanism. To build a comprehensive information system in favor of sharing and exchanging information, a sound and secure unified e-government authentication system is firstly needed. The paper, combining with practical development of e-government network, carries out a thorough discussion on how to achieve data synchronization between unified authentication system and related application systems.

Networks in ATLAS

CERN Document Server

Mc Kee, Shawn Patrick; The ATLAS collaboration

2016-01-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks....
Networks in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00260714; The ATLAS collaboration

2017-01-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks....
Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

Science.gov (United States)

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.
Community-centred Networks and Networking among Companies, Educational and Cultural Institutions and Research

DEFF Research Database (Denmark)

Konnerup, Ulla; Dirckinck-Holmfeld, Lone

2010-01-01

This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...
On Identities in Modern Networks

Directory of Open Access Journals (Sweden)

Libor Polcak

2014-09-01

Full Text Available Communicating parties inside computer networks use different kind of identifiers. Some of these identifiers are stable, e.g., logins used to access a specific service, some are only temporary, e.g., dynamically assigned IP addresses. This paper tackles several challenges of lawful interception that emerged in modern networks. The main contribution is the graph model that links identities learnt from various sources distributed in a network. The inferred identities result into an interception of more detailed data in conformance with the issued court order. The approach deals with network address translation, short-lived identifiers and simultaneous usage of different identities. The approach was evaluated to be viable during real network testing based on various means to learn identities of users connected to a network.
A Network of Networks Perspective on Global Trade.

Science.gov (United States)

Maluck, Julian; Donner, Reik V

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990-2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector's role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network's substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed to
Conceptualizing the e-Learning Assessment Domain using an Ontology Network

Directory of Open Access Journals (Sweden)

Lucía Romero

2012-09-01

Full Text Available During the last year, approaches that use ontologies, the backbone of the Semantic Web technologies, for different purposes in the assessment domain of e-Learning have emerged. One of these purposes is the use of ontologies as a mean of providing a structure to guide the automated design of assessments. The most of the approaches that deal with this problem have proposed individual ontologies that model only a part of the assessment domain. The main contribution of this paper is an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of it. The main advantage of this network is that it is enriched with rules for considering not only technical aspects of an assessment but also pedagogic
eWOM credibility on social networking sites: A framework

OpenAIRE

Moran, Gillian; Muzellec, Laurent

2017-01-01

Social networking sites (SNS) offer brands the ability to spread positive electronic Word of Mouth (eWOM) for the purposes of building awareness and acquiring new customers. However, the credibility of eWOM is threatened of late as marketers increasingly try to manipulate eWOM practices on SNS. A greater understanding of eWOM credibility is necessary to better enable marketers to leverage true consumer engagement by generating credible peer-to-peer communications. Yet, to date, there is no on...
Wavelength-Converter Saving Span Restoration in GMPLS Controlled WDM Optical Networks

DEFF Research Database (Denmark)

Ruepp, Sarah Renée; Buron, Jakob Due; Andriolli, N.

2006-01-01

We present two label preference schemes to reduce wavelength-conversion during restoration path setup in GMPLS controlled optical networks exploiting span restoration. The amount of required wavelength-conversions can be reduced up to 34 percent.......We present two label preference schemes to reduce wavelength-conversion during restoration path setup in GMPLS controlled optical networks exploiting span restoration. The amount of required wavelength-conversions can be reduced up to 34 percent....
E3Net: a system for exploring E3-mediated regulatory networks of cellular functions.

Science.gov (United States)

Han, Youngwoong; Lee, Hodong; Park, Jong C; Yi, Gwan-Su

2012-04-01

Ubiquitin-protein ligase (E3) is a key enzyme targeting specific substrates in diverse cellular processes for ubiquitination and degradation. The existing findings of substrate specificity of E3 are, however, scattered over a number of resources, making it difficult to study them together with an integrative view. Here we present E3Net, a web-based system that provides a comprehensive collection of available E3-substrate specificities and a systematic framework for the analysis of E3-mediated regulatory networks of diverse cellular functions. Currently, E3Net contains 2201 E3s and 4896 substrates in 427 organisms and 1671 E3-substrate specific relations between 493 E3s and 1277 substrates in 42 organisms, extracted mainly from MEDLINE abstracts and UniProt comments with an automatic text mining method and additional manual inspection and partly from high throughput experiment data and public ubiquitination databases. The significant functions and pathways of the extracted E3-specific substrate groups were identified from a functional enrichment analysis with 12 functional category resources for molecular functions, protein families, protein complexes, pathways, cellular processes, cellular localization, and diseases. E3Net includes interactive analysis and navigation tools that make it possible to build an integrative view of E3-substrate networks and their correlated functions with graphical illustrations and summarized descriptions. As a result, E3Net provides a comprehensive resource of E3s, substrates, and their functional implications summarized from the regulatory network structures of E3-specific substrate groups and their correlated functions. This resource will facilitate further in-depth investigation of ubiquitination-dependent regulatory mechanisms. E3Net is freely available online at http://pnet.kaist.ac.kr/e3net.
Performance evaluations of hybrid modulation with different optical labels over PDQ in high bit-rate OLS network systems.

Science.gov (United States)

Xu, M; Li, Y; Kang, T Z; Zhang, T S; Ji, J H; Yang, S W

2016-11-14

Two orthogonal modulation optical label switching(OLS) schemes, which are based on payload of polarization multiplexing-differential quadrature phase shift keying(POLMUX-DQPSK or PDQ) modulated with identifications of duobinary (DB) label and pulse position modulation(PPM) label, are researched in high bit-rate OLS network. The BER performance of hybrid modulation with payload and label signals are discussed and evaluated in theory and simulation. The theoretical BER expressions of PDQ, PDQ-DB and PDQ-PPM are given with analysis method of hybrid modulation encoding in different the bit-rate ratios of payload and label. Theoretical derivation results are shown that the payload of hybrid modulation has a certain gain of receiver sensitivity than payload without label. The sizes of payload BER gain obtained from hybrid modulation are related to the different types of label. The simulation results are consistent with that of theoretical conclusions. The extinction ratio (ER) conflicting between hybrid encoding of intensity and phase types can be compromised and optimized in OLS system of hybrid modulation. The BER analysis method of hybrid modulation encoding in OLS system can be applied to other n-ary hybrid modulation or combination modulation systems.
Vulnerability of network of networks

Science.gov (United States)

Havlin, S.; Kenett, D. Y.; Bashan, A.; Gao, J.; Stanley, H. E.

2014-10-01

Our dependence on networks - be they infrastructure, economic, social or others - leaves us prone to crises caused by the vulnerabilities of these networks. There is a great need to develop new methods to protect infrastructure networks and prevent cascade of failures (especially in cases of coupled networks). Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How, and at which cost can one restructure the network such that it will become more robust against malicious attacks? The gradual increase in attacks on the networks society depends on - Internet, mobile phone, transportation, air travel, banking, etc. - emphasize the need to develop new strategies to protect and defend these crucial networks of communication and infrastructure networks. One example is the threat of liquid explosives a few years ago, which completely shut down air travel for days, and has created extreme changes in regulations. Such threats and dangers warrant the need for new tools and strategies to defend critical infrastructure. In this paper we review recent advances in the theoretical understanding of the vulnerabilities of interdependent networks with and without spatial embedding, attack strategies and their affect on such networks of networks as well as recently developed strategies to optimize and repair failures caused by such attacks.
A random network based, node attraction facilitated network evolution method

Directory of Open Access Journals (Sweden)

WenJun Zhang

2016-03-01

Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.
How to construct the statistic network? An association network of herbaceous

Directory of Open Access Journals (Sweden)

WenJun Zhang

2012-06-01

Full Text Available In present study I defined a new type of network, the statistic network. The statistic network is a weighted and non-deterministic network. In the statistic network, a connection value, i.e., connection weight, represents connection strength and connection likelihood between two nodes and its absolute value falls in the interval (0,1]. The connection value is expressed as a statistical measure such as correlation coefficient, association coefficient, or Jaccard coefficient, etc. In addition, all connections of the statistic network can be statistically tested for their validity. A connection is true if the connection value is statistically significant. If all connection values of a node are not statistically significant, it is an isolated node. An isolated node has not any connection to other nodes in the statistic network. Positive and negative connection values denote distinct connectiontypes (positive or negative association or interaction. In the statistic network, two nodes with the greater connection value will show more similar trend in the change of their states. At any time we can obtain a sample network of the statistic network. A sample network is a non-weighted and deterministic network. Thestatistic network, in particular the plant association network that constructed from field sampling, is mostly an information network. Most of the interspecific relationships in plant community are competition and cooperation. Therefore in comparison to animal networks, the methodology of statistic network is moresuitable to construct plant association networks. Some conclusions were drawn from this study: (1 in the plant association network, most connections are weak and positive interactions. The association network constructed from Spearman rank correlation has most connections and isolated taxa are fewer. From net linear correlation,linear correlation, to Spearman rank correlation, the practical number of connections and connectance in the
eQTL Networks Reveal Complex Genetic Architecture in the Immature Soybean Seed

Directory of Open Access Journals (Sweden)

Yung-Tsi Bolon

2014-03-01

Full Text Available The complex network of regulatory factors and interactions involved in transcriptional regulation within the seed is not well understood. To evaluate gene expression regulation in the immature seed, we utilized a genetical genomics approach on a soybean [ (L. Merr.] recombinant inbred line (RIL population and produced a genome-wide expression quantitative trait loci (eQTL dataset. The validity of the dataset was confirmed by mapping the eQTL hotspot for flavonoid biosynthesis-related genes to a region containing repeats of chalcone synthase (CHS genes known to correspond to the soybean inhibitor locus that regulates seed color. We then identified eQTL for genes with seed-specific expression and discovered striking eQTL hotspots at distinct genomic intervals on chromosomes (Chr 20, 7, and 13. The main eQTL hotspot for transcriptional regulation of fatty acid biosynthesis genes also coincided with regulation of oleosin genes. Transcriptional upregulation of genesets from eQTL with opposite allelic effects were also found. Gene–eQTL networks were constructed and candidate regulatory genes were identified from these three key loci specific to seed expression and enriched in genes involved in seed oil accumulation. Our data provides new insight into the complex nature of gene networks in the immature soybean seed and the genetic architecture that contributes to seed development.
Collaborative Tools for e-Participation across Networks: The Comuno Networking Site for Public Governance and Services

Directory of Open Access Journals (Sweden)

Michael Kaschesky

2010-04-01

Full Text Available This paper presents collaborative tools for public participation across multiple networking sites. The tools are part of the Comuno networking site for public governance and services, which is particularly targeted at the public sector (currently in alpha testing at http://comuno.org. The Broadcast tool allows cross-posting content from Comuno to a wide variety of other networking sites, such as Facebook or Twitter. The UserFeed and TopicFeed tools build RSS feeds from content published by a specific user or under a specific topic. The LifeStream tool gathers a user’s activities across multiple networking sites in the private account section at Comuno. These tools and related aspects of the Comuno networking site are discussed and presented in the context of deliberation and opinion-forming in a Swiss bilingual city.
Networks of networks – An introduction

International Nuclear Information System (INIS)

Kenett, Dror Y.; Perc, Matjaž; Boccaletti, Stefano

2015-01-01

Graphical abstract: Interdependent network reciprocity. Only those blue cooperative domains that are initially present on both networks survive. Abstract: This is an introduction to the special issue titled “Networks of networks” that is in the making at Chaos, Solitons & Fractals. Recent research and reviews attest to the fact that networks of networks are the next frontier in network science [1–7]. Not only are interactions limited and thus inadequately described by well-mixed models, it is also a fact that the networks that should be an integral part of such models are often interconnected, thus making the processes that are unfolding on them interdependent. From the World economy and transportation systems to social media, it is clear that processes taking place in one network might significantly affect what is happening in many other networks. Within an interdependent system, each type of interaction has a certain relevance and meaning, so that treating all the links identically inevitably leads to information loss. Networks of networks, interdependent networks, or multilayer networks are therefore a much better and realistic description of such systems, and this Special Issue is devoted to their structure, dynamics and evolution, as well as to the study of emergent properties in multi-layered systems in general. Topics of interest include but are not limited to the spread of epidemics and information, percolation, diffusion, synchronization, collective behavior, and evolutionary games on networks of networks. Interdisciplinary work on all aspects of networks of networks, regardless of background and motivation, is very welcome.
Attachment and social networks.

Science.gov (United States)

Gillath, Omri; C Karantzas, Gery; Lee, Juwon

2018-02-21

The current review covers two lines of research linking attachment and social networks. One focuses on attachment networks (the people who fulfill one's attachment needs), examining composition and age-related differences pertaining to these networks. The other line integrates attachment with social network analysis to investigate how individual differences in adult attachment are associated with the management and characteristics (e.g., density, multiplexity, and centrality) of people's social networks. We show that most people's attachment networks are small and hierarchical, with one figure being the primary attachment figure (often a mother or romantic partner, depending on age). Furthermore, attachment style predicts network characteristics and management, such that insecurity is associated with less closeness, multiplexity, centrality, and poorer management (less maintenance, more dissolution). Copyright © 2018 Elsevier Ltd. All rights reserved.
Recurrent Neural Networks to Correct Satellite Image Classification Maps

Science.gov (United States)

Maggiori, Emmanuel; Charpiat, Guillaume; Tarabalka, Yuliya; Alliez, Pierre

2017-09-01

While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps.
E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.

Science.gov (United States)

Trapp, Philip; Echeveste, Rodrigo; Gros, Claudius

2018-06-12

Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.

A Network of Networks Perspective on Global Trade

Science.gov (United States)

Maluck, Julian; Donner, Reik V.

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990–2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector’s role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network’s substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed
High-accuracy local positioning network for the alignment of the Mu2e experiment.

Energy Technology Data Exchange (ETDEWEB)

Hejdukova, Jana B. [Czech Technical Univ., Prague (Czech Republic)

2017-06-01

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

NARCIS (Netherlands)

Galagan, D.V.

2004-01-01

Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a
Heterodox networks

DEFF Research Database (Denmark)

Lala, Purnima; Kumar, Ambuj

2016-01-01

It is imperative for the service providers to bring innovation in the network design to meet the exponential growth of mobile subscribers for multi-technology future wireless networks. As a matter of research, studies on providing services to moving subscriber groups aka ‘Place Time Capacity (PTC......)’ have not been considered much in the literature. In this article we present Heterodox networks as an innovative and alternate approach to handle the PTC congestion. We describe two different approaches to combat the PTC congestion where the traditional terrestrial infrastructure fails to provide......-Configurable Intelligent Distributed Antenna System (SCIDAS)’ that overlays intelligence over the conventional DAS architecture and latter is in the form of a swarm of intelligent hovering base stations working in a team to cooperatively resolve the PTC congestion at the Area of Event (AoE). A suitable network...
Network Coded Software Defined Networking

DEFF Research Database (Denmark)

Krigslund, Jeppe; Hansen, Jonas; Roetter, Daniel Enrique Lucani

2015-01-01

Software Defined Networking (SDN) and Network Coding (NC) are two key concepts in networking that have garnered a large attention in recent years. On the one hand, SDN's potential to virtualize services in the Internet allows a large flexibility not only for routing data, but also to manage....... This paper advocates for the use of SDN to bring about future Internet and 5G network services by incorporating network coding (NC) functionalities. The inherent flexibility of both SDN and NC provides a fertile ground to envision more efficient, robust, and secure networking designs, that may also...
Creating, generating and comparing random network models with NetworkRandomizer.

Science.gov (United States)

Tosadori, Gabriele; Bestvina, Ivan; Spoto, Fausto; Laudanna, Carlo; Scardoni, Giovanni

2016-01-01

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.
Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System

Directory of Open Access Journals (Sweden)

A. A. Salama

2015-03-01

Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.
Connection Management and Recovery Strategies under Epidemic Network Failures in Optical Transport Networks

DEFF Research Database (Denmark)

Fagertun, Anna Manolova; Ruepp, Sarah Renée

2014-01-01

The current trend in deploying automatic control plane solutions for increased flexibility in the optical transport layer leads to numerous advantages for both the operators and the customers, but also pose challenges related to the stability of the network and its ability to operate in a robust...... manner under attacks. This work proposes four policies for failure handling in a connection-oriented optical transport network, under Generalized MultiProtocol Label Switching control plane, and evaluates their performance under multiple correlated large-scale failures. We employ the Susceptible...... of their transport infrastructures. Applying proactive methods for avoiding areas where epidemic failures spread results in 50% less connections requiring recovery, which translates in improved quality of service to customers....
Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

Directory of Open Access Journals (Sweden)

Yang Dan

2008-12-01

Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.
Novel Scheme for Packet Forwarding without Header Modifications in Optical Networks

DEFF Research Database (Denmark)

Wessing, Henrik; Christiansen, Henrik Lehrmann; Fjelde, Tina

2002-01-01

We present a novel scheme for packet forwarding in optical packet-switched networks and we further demonstrate its good scalability through simulations. The scheme requires neither header modification nor any label distribution protocol, thus reducing component cost while simplifying network...
Hacking the Cell: Network Intrusion and Exploitation by Adenovirus E1A.

Science.gov (United States)

King, Cason R; Zhang, Ali; Tessier, Tanner M; Gameiro, Steven F; Mymryk, Joe S

2018-05-01

As obligate intracellular parasites, viruses are dependent on their infected hosts for survival. Consequently, viruses are under enormous selective pressure to utilize available cellular components and processes to their own advantage. As most, if not all, cellular activities are regulated at some level via protein interactions, host protein interaction networks are particularly vulnerable to viral exploitation. Indeed, viral proteins frequently target highly connected "hub" proteins to "hack" the cellular network, defining the molecular basis for viral control over the host. This widespread and successful strategy of network intrusion and exploitation has evolved convergently among numerous genetically distinct viruses as a result of the endless evolutionary arms race between pathogens and hosts. Here we examine the means by which a particularly well-connected viral hub protein, human adenovirus E1A, compromises and exploits the vulnerabilities of eukaryotic protein interaction networks. Importantly, these interactions identify critical regulatory hubs in the human proteome and help define the molecular basis of their function. Copyright © 2018 King et al.
Medical reliable network using concatenated channel codes through GSM network.

Science.gov (United States)

Ahmed, Emtithal; Kohno, Ryuji

2013-01-01

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

DEFF Research Database (Denmark)

Buron, Jakob Due; Ruepp, Sarah Renée; Andriolli, N.

2006-01-01

The proposed label preference scheme reduces blocking of span restoration requests in GMPLS optical networks with limited wavelength conversion. By minimizing resource contention and conversion usage, it increases recovery percentage and reduces control plane load.......The proposed label preference scheme reduces blocking of span restoration requests in GMPLS optical networks with limited wavelength conversion. By minimizing resource contention and conversion usage, it increases recovery percentage and reduces control plane load....
Image Quality Assessment of JPEG Compressed Mars Science Laboratory Mastcam Images using Convolutional Neural Networks

Science.gov (United States)

Kerner, H. R.; Bell, J. F., III; Ben Amor, H.

2017-12-01

The Mastcam color imaging system on the Mars Science Laboratory Curiosity rover acquires images within Gale crater for a variety of geologic and atmospheric studies. Images are often JPEG compressed before being downlinked to Earth. While critical for transmitting images on a low-bandwidth connection, this compression can result in image artifacts most noticeable as anomalous brightness or color changes within or near JPEG compression block boundaries. In images with significant high-frequency detail (e.g., in regions showing fine layering or lamination in sedimentary rocks), the image might need to be re-transmitted losslessly to enable accurate scientific interpretation of the data. The process of identifying which images have been adversely affected by compression artifacts is performed manually by the Mastcam science team, costing significant expert human time. To streamline the tedious process of identifying which images might need to be re-transmitted, we present an input-efficient neural network solution for predicting the perceived quality of a compressed Mastcam image. Most neural network solutions require large amounts of hand-labeled training data for the model to learn the target mapping between input (e.g. distorted images) and output (e.g. quality assessment). We propose an automatic labeling method using joint entropy between a compressed and uncompressed image to avoid the need for domain experts to label thousands of training examples by hand. We use automatically labeled data to train a convolutional neural network to estimate the probability that a Mastcam user would find the quality of a given compressed image acceptable for science analysis. We tested our model on a variety of Mastcam images and found that the proposed method correlates well with image quality perception by science team members. When assisted by our proposed method, we estimate that a Mastcam investigator could reduce the time spent reviewing images by a minimum of 70%.
Model checking mobile ad hoc networks

NARCIS (Netherlands)

Ghassemi, Fatemeh; Fokkink, Wan

2016-01-01

Modeling arbitrary connectivity changes within mobile ad hoc networks (MANETs) makes application of automated formal verification challenging. We use constrained labeled transition systems as a semantic model to represent mobility. To model check MANET protocols with respect to the underlying
Entropy of network ensembles

Science.gov (United States)

Bianconi, Ginestra

2009-03-01

In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.
Report on Asian Environment Information Network; 'Asia kankyo joho network' ni kansuru hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-03-01

The goal is the construction of Asian Environment Information Network (AEInet) in accordance with a contract signed between Indonesia's LIPI (Indonesian Institute of Science) and NEDO under NEDO's Research Cooperation Project Concerning the Development of Environment Measuring Laser Radar (LR). The network is so designed and constituted as to operate on a private line between Indonesia and Japan via IP (Internet protocol) and to enable the exchange on the Internet network of the data collected/analyzed by the Indonesian LR system and of articles of e-mail between scientists of the two countries. The AEInet will be utilized for the collection/analysis of LR-collected data; exchange of observed data and the result of processing; provision of support to environment information scientists in exchanging e-mail and information; and the search of databases for the implementation of the project. In this paper, the outline and functions of the system, network system design, WWW server construction, network operating status, joint researches with Indonesia, etc., are described. (NEDO)
Dead end metabolites--defining the known unknowns of the E. coli metabolic network.

Directory of Open Access Journals (Sweden)

Amanda Mackie

Full Text Available The EcoCyc database is an online scientific database which provides an integrated view of the metabolic and regulatory network of the bacterium Escherichia coli K-12 and facilitates computational exploration of this important model organism. We have analysed the occurrence of dead end metabolites within the database--these are metabolites which lack the requisite reactions (either metabolic or transport that would account for their production or consumption within the metabolic network. 127 dead end metabolites were identified from the 995 compounds that are contained within the EcoCyc metabolic network. Their presence reflects either a deficit in our representation of the network or in our knowledge of E. coli metabolism. Extensive literature searches resulted in the addition of 38 transport reactions and 3 metabolic reactions to the database and led to an improved representation of the pathway for Vitamin B12 salvage. 39 dead end metabolites were identified as components of reactions that are not physiologically relevant to E. coli K-12--these reactions are properties of purified enzymes in vitro that would not be expected to occur in vivo. Our analysis led to improvements in the software that underpins the database and to the program that finds dead end metabolites within EcoCyc. The remaining dead end metabolites in the EcoCyc database likely represent deficiencies in our knowledge of E. coli metabolism.
Tyrosine B10 triggers a heme propionate hydrogen bonding network loop with glutamine E7 moiety

International Nuclear Information System (INIS)

Ramos-Santana, Brenda J.; López-Garriga, Juan

2012-01-01

Highlights: ► H-bonding network loop by PheB10Tyr mutation is proposed. ► The propionate group H-bonding network restricted the flexibility of the heme. ► The hydrogen bonding interaction modulates the electron density of the iron. ► Propionate H-bonding network loop explains the heme-ligand stabilization. -- Abstract: Propionates, as peripheral groups of the heme active center in hemeproteins have been described to contribute in the modulation of heme reactivity and ligand selection. These electronic characteristics prompted the question of whether the presence of hydrogen bonding networks between propionates and distal amino acids present in the heme ligand moiety can modulate physiological relevant events, like ligand binding association and dissociation activities. Here, the role of these networks was evaluated by NMR spectroscopy using the hemoglobin I PheB10Tyr mutant from Lucina pectinata as model for TyrB10 and GlnE7 hemeproteins. 1 H-NMR results for the rHbICN PheB10Tyr derivative showed chemical shifts of TyrB10 OHη at 31.00 ppm, GlnE7 N ε1 H/N ε2 H at 10.66 ppm/−3.27 ppm, and PheE11 C δ H at 11.75 ppm, indicating the presence of a crowded, collapsed, and constrained distal pocket. Strong dipolar contacts and inter-residues crosspeaks between GlnE7/6-propionate group, GlnE7/TyrB10 and TyrB10/CN suggest that this hydrogen bonding network loop between GlnE7, TyrB10, 6-propionate group, and the heme ligand contribute significantly to the modulation of the heme iron electron density as well as the ligand stabilization mechanism. Therefore, the network loop presented here support the fact that the electron withdrawing character of the hydrogen bonding is controlled by the interaction of the propionates and the nearby electronic environments contributing to the modulation of the heme electron density state. Thus, we hypothesize that in hemeproteins with similar electrostatic environment the flexibility of the heme-6-propionate promotes a hydrogen
Overcoming uncertainty for within-network relational machine learning

OpenAIRE

Pfeiffer, Joseph J.

2015-01-01

People increasingly communicate through email and social networks to maintain friendships and conduct business, as well as share online content such as pictures, videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in...

Cloud-Centric and Logically Isolated Virtual Network Environment Based on Software-Defined Wide Area Network

Directory of Open Access Journals (Sweden)

Dongkyun Kim

2017-12-01

Full Text Available Recent development of distributed cloud environments requires advanced network infrastructure in order to facilitate network automation, virtualization, high performance data transfer, and secured access of end-to-end resources across regional boundaries. In order to meet these innovative cloud networking requirements, software-defined wide area network (SD-WAN is primarily demanded to converge distributed cloud resources (e.g., virtual machines (VMs in a programmable and intelligent manner over distant networks. Therefore, this paper proposes a logically isolated networking scheme designed to integrate distributed cloud resources to dynamic and on-demand virtual networking over SD-WAN. The performance evaluation and experimental results of the proposed scheme indicate that virtual network convergence time is minimized in two different network models such as: (1 an operating OpenFlow-oriented SD-WAN infrastructure (KREONET-S which is deployed on the advanced national research network in Korea, and (2 Mininet-based experimental and emulated networks.
Networks in ATLAS

Science.gov (United States)

McKee, Shawn; ATLAS Collaboration

2017-10-01

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

Science.gov (United States)

Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

2015-01-01

This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…
Influence of choice of null network on small-world parameters of structural correlation networks.

Directory of Open Access Journals (Sweden)

S M Hadi Hosseini

Full Text Available In recent years, coordinated variations in brain morphology (e.g., volume, thickness have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1 networks constructed by topology randomization (TOP, 2 networks matched to the distributional properties of the observed covariance matrix (HQS, and 3 networks generated from correlation of randomized input data (COR. The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.
Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

Science.gov (United States)

Hosseini, S. M. Hadi; Kesler, Shelli R.

2013-01-01

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672
Voice Quality Estimation in Wireless Networks

Directory of Open Access Journals (Sweden)

Petr Zach

2015-01-01

Full Text Available This article deals with the impact of Wireless (Wi-Fi networks on the perceived quality of voice services. The Quality of Service (QoS metrics must be monitored in the computer network during the voice data transmission to ensure proper voice service quality the end-user has paid for, especially in the wireless networks. In addition to the QoS, research area called Quality of Experience (QoE provides metrics and methods for quality evaluation from the end-user’s perspective. This article focuses on a QoE estimation of Voice over IP (VoIP calls in the wireless networks using network simulator. Results contribute to voice quality estimation based on characteristics of the wireless network and location of a wireless client.
Network Coded Software Defined Networking

DEFF Research Database (Denmark)

Hansen, Jonas; Roetter, Daniel Enrique Lucani; Krigslund, Jeppe

2015-01-01

Software defined networking has garnered large attention due to its potential to virtualize services in the Internet, introducing flexibility in the buffering, scheduling, processing, and routing of data in network routers. SDN breaks the deadlock that has kept Internet network protocols stagnant...... for decades, while applications and physical links have evolved. This article advocates for the use of SDN to bring about 5G network services by incorporating network coding (NC) functionalities. The latter constitutes a major leap forward compared to the state-of-the- art store and forward Internet paradigm...
Green mobile networks a networking perspective

CERN Document Server

Ansari, Nirwan

2016-01-01

Combines the hot topics of energy efficiency and next generation mobile networking, examining techniques and solutions. Green communications is a very hot topic. Ever increasing mobile network bandwidth rates significantly impacts on operating costs due to aggregate network energy consumption. As such, design on 4G networks and beyond has increasingly started to focus on 'energy efficiency' or so-called 'green' networks. Many techniques and solutions have been proposed to enhance the energy efficiency of mobile networks, yet no book has provided an in-depth analysis of the energy consumption issues in mobile networks nor offers detailed theories, tools and solutions for solving the energy efficiency problems.
Hyperbolicity measures democracy in real-world networks

Science.gov (United States)

Borassi, Michele; Chessa, Alessandro; Caldarelli, Guido

2015-09-01

In this work, we analyze the hyperbolicity of real-world networks, a geometric quantity that measures if a space is negatively curved. We provide two improvements in our understanding of this quantity: first of all, in our interpretation, a hyperbolic network is "aristocratic", since few elements "connect" the system, while a non-hyperbolic network has a more "democratic" structure with a larger number of crucial elements. The second contribution is the introduction of the average hyperbolicity of the neighbors of a given node. Through this definition, we outline an "influence area" for the vertices in the graph. We show that in real networks the influence area of the highest degree vertex is small in what we define "local" networks (i.e., social or peer-to-peer networks), and large in "global" networks (i.e., power grid, metabolic networks, or autonomous system networks).
Community detection using preference networks

Science.gov (United States)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.
Modular architectures for quantum networks

Science.gov (United States)

Pirker, A.; Wallnöfer, J.; Dür, W.

2018-05-01

We consider the problem of generating multipartite entangled states in a quantum network upon request. We follow a top-down approach, where the required entanglement is initially present in the network in form of network states shared between network devices, and then manipulated in such a way that the desired target state is generated. This minimizes generation times, and allows for network structures that are in principle independent of physical links. We present a modular and flexible architecture, where a multi-layer network consists of devices of varying complexity, including quantum network routers, switches and clients, that share certain resource states. We concentrate on the generation of graph states among clients, which are resources for numerous distributed quantum tasks. We assume minimal functionality for clients, i.e. they do not participate in the complex and distributed generation process of the target state. We present architectures based on shared multipartite entangled Greenberger–Horne–Zeilinger states of different size, and fully connected decorated graph states, respectively. We compare the features of these architectures to an approach that is based on bipartite entanglement, and identify advantages of the multipartite approach in terms of memory requirements and complexity of state manipulation. The architectures can handle parallel requests, and are designed in such a way that the network state can be dynamically extended if new clients or devices join the network. For generation or dynamical extension of the network states, we propose a quantum network configuration protocol, where entanglement purification is used to establish high fidelity states. The latter also allows one to show that the entanglement generated among clients is private, i.e. the network is secure.
Topology of the European Network of Earth Observation Networks and the need for an European Network of Networks

Science.gov (United States)

Masó, Joan; Serral, Ivette; McCallum, Ian; Blonda, Palma; Plag, Hans-Peter

2016-04-01

ConnectinGEO (Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations" is an H2020 Coordination and Support Action with the primary goal of linking existing Earth Observation networks with science and technology (S&T) communities, the industry sector, the Group on Earth Observations (GEO), and Copernicus. The project will end in February 2017. ConnectinGEO will initiate a European Network of Earth Observation Networks (ENEON) that will encompass space-based, airborne and in-situ observations networks. ENEON will be composed of project partners representing thematic observation networks along with the GEOSS Science and Technology Stakeholder Network, GEO Communities of Practices, Copernicus services, Sentinel missions and in-situ support data representatives, representatives of the European space-based, airborne and in-situ observations networks. This communication presents the complex panorama of Earth Observations Networks in Europe. The list of networks is classified by discipline, variables, geospatial scope, etc. We also capture the membership and relations with other networks and umbrella organizations like GEO. The result is a complex interrelation between networks that can not be clearly expressed in a flat list. Technically the networks can be represented as nodes with relations between them as lines connecting the nodes in a graph. We have chosen RDF as a language and an AllegroGraph 3.3 triple store that is visualized in several ways using for example Gruff 5.7. Our final aim is to identify gaps in the EO Networks and justify the need for a more structured coordination between them.
Increasing Scalability of Researcher Network Extraction from the Web

Science.gov (United States)

Asada, Yohei; Matsuo, Yutaka; Ishizuka, Mitsuru

Social networks, which describe relations among people or organizations as a network, have recently attracted attention. With the help of a social network, we can analyze the structure of a community and thereby promote efficient communications within it. We investigate the problem of extracting a network of researchers from the Web, to assist efficient cooperation among researchers. Our method uses a search engine to get the cooccurences of names of two researchers and calculates the streangth of the relation between them. Then we label the relation by analyzing the Web pages in which these two names cooccur. Research on social network extraction using search engines as ours, is attracting attention in Japan as well as abroad. However, the former approaches issue too many queries to search engines to extract a large-scale network. In this paper, we propose a method to filter superfluous queries and facilitates the extraction of large-scale networks. By this method we are able to extract a network of around 3000-nodes. Our experimental results show that the proposed method reduces the number of queries significantly while preserving the quality of the network as compared to former methods.
Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Directory of Open Access Journals (Sweden)

Lan Liu

2017-01-01

Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qnetwork malware and provide a theoretical basis to reduce and prevent network security incidents.
The stability of financial market networks

Science.gov (United States)

Yan, Xin-Guo; Xie, Chi; Wang, Gang-Jin

2014-08-01

We investigate the stability of a financial market network by measuring its topological robustness, namely the ability of the network to resist structural or topological changes. The closing prices of 710 stocks in the Shanghai Stock Exchange (SSE) from 2005 to 2011 are chosen as the empirical data. We divide the period into three sub-periods: before, during, and after the US sub-prime crisis. By monitoring the size of the clusters which fall apart from the network after removing the nodes (i.e., the listed companies in the SSE), we find that: i) the SSE network is sensitive to the nodes' failure, which implies that the network is unstable. ii) the SSE network before the financial crisis has the strongest robustness against the intentional topological damage; iii) the hubs (i.e., highly connected nodes) connect with each other directly and play a vital important role in maintaining SSE network's stability.
Cloud networking understanding cloud-based data center networks

CERN Document Server

Lee, Gary

2014-01-01

Cloud Networking: Understanding Cloud-Based Data Center Networks explains the evolution of established networking technologies into distributed, cloud-based networks. Starting with an overview of cloud technologies, the book explains how cloud data center networks leverage distributed systems for network virtualization, storage networking, and software-defined networking. The author offers insider perspective to key components that make a cloud network possible such as switch fabric technology and data center networking standards. The final chapters look ahead to developments in architectures
Macroecology of pollination networks

DEFF Research Database (Denmark)

Nielsen, Kristian Trøjelsgaard; Olesen, Jens Mogens

2013-01-01

towards the tropics, and that network topology would be affected by current climate. Location Global. Methods Each network was organized as a presence/absence matrix, consisting of P plant species, A pollinator species and their links. From these matrices, network parameters were estimated. Additionally...... with either latitude or elevation. However, network modularity decreased significantly with latitude whereas mean number of links per plant species (Lp) and A/P ratio peaked at mid-latitude. Above 500 m a.s.l., A/P ratio decreased and mean number of links per pollinator species (La) increased with elevation......Aim Interacting communities of species are organized into complex networks, and network analysis is reckoned to be a strong tool for describing their architecture. Many species assemblies show strong macroecological patterns, e.g. increasing species richness with decreasing latitude, but whether...
Innovations, status, and networks

NARCIS (Netherlands)

P. Wang (Pengfei)

2016-01-01

markdownabstractTo obtain and maintain competitive advantage, firms need to implement appropriate innovation strategies (i.e. exploration or exploitation) and acquire status in external networks. In this dissertation, I investigate how innovation strategy, status, and network structure jointly
Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

Science.gov (United States)

Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-04-08

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.
Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

Directory of Open Access Journals (Sweden)

Masaya Murakami

2018-04-01

Full Text Available Virtualization of wireless sensor networks (WSN is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions and nodes (i.e., neurons. We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

Deterministic bound for avionics switched networks according to networking features using network calculus

Directory of Open Access Journals (Sweden)

Feng HE

2017-12-01

Full Text Available The state of the art avionics system adopts switched networks for airborne communications. A major concern in the design of the networks is the end-to-end guarantee ability. Analytic methods have been developed to compute the worst-case delays according to the detailed configurations of flows and networks within avionics context, such as network calculus and trajectory approach. It still lacks a relevant method to make a rapid performance estimation according to some typically switched networking features, such as networking scale, bandwidth utilization and average flow rate. The goal of this paper is to establish a deterministic upper bound analysis method by using these networking features instead of the complete network configurations. Two deterministic upper bounds are proposed from network calculus perspective: one is for a basic estimation, and another just shows the benefits from grouping strategy. Besides, a mathematic expression for grouping ability is established based on the concept of network connecting degree, which illustrates the possibly minimal grouping benefit. For a fully connected network with 4 switches and 12 end systems, the grouping ability coming from grouping strategy is 15â20%, which just coincides with the statistical data (18â22% from the actual grouping advantage. Compared with the complete network calculus analysis method for individual flows, the effectiveness of the two deterministic upper bounds is no less than 38% even with remarkably varied packet lengths. Finally, the paper illustrates the design process for an industrial Avionics Full DupleX switched Ethernet (AFDX networking case according to the two deterministic upper bounds and shows that a better control for network connecting, when designing a switched network, can improve the worst-case delays dramatically. Keywords: Deterministic bound, Grouping ability, Network calculus, Networking features, Switched networks
Information theoretic description of networks

Science.gov (United States)

Wilhelm, Thomas; Hollunder, Jens

2007-11-01

We present a new information theoretic approach for network characterizations. It is developed to describe the general type of networks with n nodes and L directed and weighted links, i.e., it also works for the simpler undirected and unweighted networks. The new information theoretic measures for network characterizations are based on a transmitter-receiver analogy of effluxes and influxes. Based on these measures, we classify networks as either complex or non-complex and as either democracy or dictatorship networks. Directed networks, in particular, are furthermore classified as either information spreading and information collecting networks. The complexity classification is based on the information theoretic network complexity measure medium articulation (MA). It is proven that special networks with a medium number of links ( L∼n1.5) show the theoretical maximum complexity MA=(log n)2/2. A network is complex if its MA is larger than the average MA of appropriately randomized networks: MA>MAr. A network is of the democracy type if its redundancy Rdictatorship network. In democracy networks all nodes are, on average, of similar importance, whereas in dictatorship networks some nodes play distinguished roles in network functioning. In other words, democracy networks are characterized by cycling of information (or mass, or energy), while in dictatorship networks there is a straight through-flow from sources to sinks. The classification of directed networks into information spreading and information collecting networks is based on the conditional entropies of the considered networks ( H(A/B)=uncertainty of sender node if receiver node is known, H(B/A)=uncertainty of receiver node if sender node is known): if H(A/B)>H(B/A), it is an information collecting network, otherwise an information spreading network. Finally, different real networks (directed and undirected, weighted and unweighted) are classified according to our general scheme.
Path selection and bandwidth allocation in MPLS networks: a nonlinear programming approach

Science.gov (United States)

Burns, J. E.; Ott, Teunis J.; de Kock, Johan M.; Krzesinski, Anthony E.

2001-07-01

Multi-protocol Label Switching extends the IPv4 destination-based routing protocols to provide new and scalable routing capabilities in connectionless networks using relatively simple packet forwarding mechanisms. MPLS networks carry traffic on virtual connections called label switched paths. This paper considers path selection and bandwidth allocation in MPLS networks in order to optimize the network quality of service. The optimization is based upon the minimization of a non-linear objective function which under light load simplifies to OSPF routing with link metrics equal to the link propagation delays. The behavior under heavy load depends on the choice of certain parameters: It can essentially be made to minimize maximal expected utilization, or to maximize minimal expected weighted slacks (both over all links). Under certain circumstances it can be made to minimize the probability that a link has an instantaneous offered load larger than its transmission capacity. We present a model of an MPLS network and an algorithm to find and capacitate optimal LSPs. The algorithm is an improvement of the well-known flow deviation non-linear programming method. The algorithm is applied to compute optimal LSPs for several test networks carrying a single traffic class.
A flood-based information flow analysis and network minimization method for gene regulatory networks.

Science.gov (United States)

Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

2013-04-24

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.
Altered Cerebral Blood Flow Covariance Network in Schizophrenia.

Science.gov (United States)

Liu, Feng; Zhuo, Chuanjun; Yu, Chunshui

2016-01-01

Many studies have shown abnormal cerebral blood flow (CBF) in schizophrenia; however, it remains unclear how topological properties of CBF network are altered in this disorder. Here, arterial spin labeling (ASL) MRI was employed to measure resting-state CBF in 96 schizophrenia patients and 91 healthy controls. CBF covariance network of each group was constructed by calculating across-subject CBF covariance between 90 brain regions. Graph theory was used to compare intergroup differences in global and nodal topological measures of the network. Both schizophrenia patients and healthy controls had small-world topology in CBF covariance networks, implying an optimal balance between functional segregation and integration. Compared with healthy controls, schizophrenia patients showed reduced small-worldness, normalized clustering coefficient and local efficiency of the network, suggesting a shift toward randomized network topology in schizophrenia. Furthermore, schizophrenia patients exhibited altered nodal centrality in the perceptual-, affective-, language-, and spatial-related regions, indicating functional disturbance of these systems in schizophrenia. This study demonstrated for the first time that schizophrenia patients have disrupted topological properties in CBF covariance network, which provides a new perspective (efficiency of blood flow distribution between brain regions) for understanding neural mechanisms of schizophrenia.
A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder

Directory of Open Access Journals (Sweden)

Xiaoling Tao

2016-03-01

Full Text Available Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN, and support vector machine (SVM by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.
Covering the Monitoring Network: A Unified Framework to Protect E-Commerce Security

Directory of Open Access Journals (Sweden)

Lirong Qiu

2017-01-01

Full Text Available Multimedia applications in smart electronic commerce (e-commerce, such as online trading and Internet marketing, always face security in storage and transmission of digital images and videos. This study addresses the problem of security in e-commerce and proposes a unified framework to analyze the security data. First, to allocate the definite security resources optimally, we build our e-commerce monitoring model as an undirected network, where a monitored node is a vertex of the graph and a connection between vertices is an undirected edge. Moreover, we aim to find a minimal cover for the monitoring network as the optimal solution of resource allocation, which is defined as the network monitoring minimization problem (NMM. This problem is proved to be NP-hard. Second, by analyzing the latent threats, we design a novel and trusted monitoring system that can integrate incident monitoring, data analysis, risk assessment, and security warnings. This system does not touch users’ privacy data. Third, we propose a sequential model-based risk assessment method, which can predict the risk according to the text semantics. Our experimental results on web scale data demonstrate that our system is flexible enough when monitoring, which also verify the effectiveness and efficiency of our system.
Advance Network Reservation and Provisioning for Science

Energy Technology Data Exchange (ETDEWEB)

Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

2009-07-10

guaranteed bandwidth of secure virtual circuits at a certain time, for a certain bandwidth and length of time. Though OSCARS operates within the ESnet, it also supplies end-to-end provisioning between multiple autonomous network domains. OSCARS gets reservation requests through a standard web service interface, and conducts a Quality-of-service (QoS) path for bandwidth guarantees. Multi-protocol Label Switching (MPLS) and the Resource Reservation Protocol (RSVP) enable to create a virtual circuit using Label Switched Paths (LSP's). It contains three main components: a reservation manager, a bandwidth scheduler, and a path setup subsystem. The bandwidth scheduler needs to have information about the current and future states of the network topology in order to accomplish end-to-end bandwidth guaranteed paths.
Networking: the view from HEP

Science.gov (United States)

McKee, Shawn

2017-10-01

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

Science.gov (United States)

Havlin, Shlomo

Network science have been focused on the properties of a single isolated network that does not interact or depends on other networks. In reality, many real-networks, such as power grids, transportation and communication infrastructures interact and depend on other networks. I will present a framework for studying the vulnerability and the recovery of networks of interdependent networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes like certain locations play a role in two networks -multiplex. This may happen recursively and can lead to a cascade of failures and to a sudden fragmentation of the system. I will present analytical solutions for the critical threshold and the giant component of a network of n interdependent networks. I will show, that the general theory has many novel features that are not present in the classical network theory. When recovery of components is possible global spontaneous recovery of the networks and hysteresis phenomena occur and the theory suggests an optimal repairing strategy of system of systems. I will also show that interdependent networks embedded in space are significantly more vulnerable compared to non embedded networks. In particular, small localized attacks may lead to cascading failures and catastrophic consequences.Thus, analyzing data of real network of networks is highly required to understand the system vulnerability. DTRA, ONR, Israel Science Foundation.
The Political Activity in the Network Environment

Directory of Open Access Journals (Sweden)

Марианна Юрьевна Павлютенкова

2015-12-01

Full Text Available The rapid development and deep penetration into all areas of modern society of information and communication technologies significantly increase the role of network interactions. Network structures represented primarily social networks, embedded in the public policy process and became one of the key political actors. Online communities take the form of public policy, where the formation of public opinion and political decision-making plays the main role. Networking environment opens up new opportunities for the opposition and protest movements, civic participation, and control of public policy in general. The article gives an insight on the political aspects of social networking, concludes on the trend formation and network's strengthening of the political activity in a wide distribution of e-networking and e-communications.
Telecommunication Networks

DEFF Research Database (Denmark)

Olsen, Rasmus Løvenstein; Balachandran, Kartheepan; Hald, Sara Ligaard

2014-01-01

In this chapter, we look into the role of telecommunication networks and their capability of supporting critical infrastructure systems and applications. The focus is on smart grids as the key driving example, bearing in mind that other such systems do exist, e.g., water management, traffic control......, etc. First, the role of basic communication is examined with a focus on critical infrastructures. We look at heterogenic networks and standards for smart grids, to give some insight into what has been done to ensure inter-operability in this direction. We then go to the physical network, and look...... threats to the critical infrastructure. Finally, before our conclusions and outlook, we give a brief overview of some key activities in the field and what research directions are currently investigated....
Didactic Networks: A Proposal for e-learning Content Generation

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F. Javier Del Alamo

2010-12-01

Full Text Available The Didactic Networks proposed in this paper are based on previous publications in the field of the RSR (Rhetorical-Semantic Relations. The RSR is a set of primitive relations used for building a specific kind of semantic networks for artificial intelligence applications on the web: the RSN (Rhetorical-Semantic Networks. We bring into focus the RSR application in the field of elearning, by defining Didactic Networks as a new set of semantic patterns oriented to the development of elearning applications. The different lines we offer in our research fall mainly into three levels: (1 The most basic one is in the field of computational linguistics and related to Logical Operations on RSR (RSR Inverses and plurals, RSR combinations, etc, once they have been created. The application of Walter Bosma's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here. (2 In parallel, we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR. (3 The third strategic line is in the meso-level, the formulation of a molecular structure of knowledge based on the most frequently used patterns. The main contribution at this level is the set of Fundamental Cognitive Networks (FCN as an application of Novak's mental maps proposal. This paper is part of this third intermediate level, and the Fundamental Didactic Networks (FDN are the result of the application of rhetorical theory procedures to the instructional theory. We have formulated a general set of RSR capable of building discourse, making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. The Instructional knowledge can then be elaborated in the same way. This network structure expressing the instructional knowledge in terms of RSR makes the objective of developing web
On Network Coded Filesystem Shim

DEFF Research Database (Denmark)

Sørensen, Chres Wiant; Roetter, Daniel Enrique Lucani; Médard, Muriel

2017-01-01

Although network coding has shown the potential to revolutionize networking and storage, its deployment has faced a number of challenges. Usual proposals involve two approaches. First, deploying a new protocol (e.g., Multipath Coded TCP), or retrofitting another one (e.g., TCP/NC) to deliver bene...
Pareto distance for multi-layer network analysis

DEFF Research Database (Denmark)

Magnani, Matteo; Rossi, Luca

2013-01-01

services, e.g., Facebook, Twitter, LinkedIn and Foursquare. As a result, the analysis of on-line social networks requires a wider scope and, more technically speaking, models for the representation of this fragmented scenario. The recent introduction of more realistic layered models has however determined......Social Network Analysis has been historically applied to single networks, e.g., interaction networks between co-workers. However, the advent of on-line social network sites has emphasized the stratified structure of our social experience. Individuals usually spread their identities over multiple...
Frequency Specific Effects of ApoE ε4 Allele on Resting-State Networks in Nondemented Elders

Directory of Open Access Journals (Sweden)

Ying Liang

2017-01-01

Full Text Available We applied resting-state functional magnetic resonance imaging (fMRI to examine the Apolipoprotein E (ApoE ε4 allele effects on functional connectivity of the default mode network (DMN and the salience network (SN. Considering the frequency specific effects of functional connectivity, we decomposed the brain network time courses into two bands: 0.01–0.027 Hz and 0.027–0.08 Hz. All scans were acquired by the Alzheimer’s Disease Neuroscience Initiative (ADNI. Thirty-two nondemented subjects were divided into two groups based on the presence (n=16 or absence (n=16 of the ApoE ε4 allele. We explored the frequency specific effects of ApoE ε4 allele on the default mode network (DMN and the salience network (SN functional connectivity. Compared to ε4 noncarriers, the DMN functional connectivity of ε4 carriers was significantly decreased while the SN functional connectivity of ε4 carriers was significantly increased. Many functional connectivities showed significant differences at the lower frequency band of 0.01–0.027 Hz or the higher frequency band of 0.027–0.08 Hz instead of the typical range of 0.01–0.08 Hz. The results indicated a frequency dependent effect of resting-state signals when investigating RSNs functional connectivity.
Green heterogeneous wireless networks

CERN Document Server

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

2016-01-01

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

CERN Document Server

Rubi, Miguel; Diaz-Guilera, Albert

2003-01-01

Networks can provide a useful model and graphic image useful for the description of a wide variety of web-like structures in the physical and man-made realms, e.g. protein networks, food webs and the Internet. The contributions gathered in the present volume provide both an introduction to, and an overview of, the multifaceted phenomenology of complex networks. Statistical Mechanics of Complex Networks also provides a state-of-the-art picture of current theoretical methods and approaches.
Depth of treatment sensitive noise resistant dynamic artificial neural networks model of recall in people with prosopagnosia.

Science.gov (United States)

Morissette, Laurence; Chartier, Sylvain; Vandermeulen, Robyn; Watier, Nicholas

2012-08-01

The Fusiform Face Area (FFA) is the brain region considered to be responsible for face recognition. Prosopagnosia is a brain disorder causing the inability to a recognise faces that is said to mainly affect the FFA. We put forward a model that simulates the capacity to retrieve label associated with faces and objects depending on the depth of treatment of the information. Akin to prosopagnosia, various localised "lesions" were inserted into the network in order to evaluate the degradation of performance. The network is first composed of a Feature Extracting Bidirectional Associative Memory (FEBAM-SOM) to represent the topological maps allowing the categorisation of all faces. The second component of the network is a Bidirectional Heteroassociative Memory (BHM) that links those representations to their semantic label. For the latter, specific semantic labels were used as well as more general ones. The inputs were images representing faces and various objects. Just like in the visual perceptual system, the images were pre-processed using a low-pass filter. Results showed that the network is able to associate the extracted map with the correct label information. The network is able to generalise and is robust to noise. Moreover, results showed that the recall performance of names associated with faces decrease with the size of lesion without affecting the performance of the objects. Finally, results obtained with the network are also consistent with human ones in that higher level, more general labels are more robust to lesion compared to low level, specific labels. Copyright © 2012 Elsevier Ltd. All rights reserved.
Development of surrogate models using artificial neural network for building shell energy labelling

International Nuclear Information System (INIS)

Melo, A.P.; Cóstola, D.; Lamberts, R.; Hensen, J.L.M.

2014-01-01

Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of surrogate models for labelling purposes. An ANN was applied to model the building stock of a city in Brazil, based on the results of extensive simulations using the high-resolution building energy simulation program EnergyPlus. Sensitivity and uncertainty analyses were carried out to evaluate the behaviour of the ANN model, and the variations in the best and worst performance for several typologies were analysed in relation to variations in the input parameters and building characteristics. The results obtained indicate that an ANN can represent the interaction between input and output data for a vast and diverse building stock. Sensitivity analysis showed that no single input parameter can be identified as the main factor responsible for the building energy performance. The uncertainty associated with several parameters plays a major role in assessing building energy performance, together with the facade area and the shell-to-floor ratio. The results of this study may have a profound impact as ANNs could be applied in the future to define regulations in many countries, with positive effects on optimizing the energy consumption. - Highlights: • We model several typologies which have variation in input parameters. • We evaluate the accuracy of surrogate models for labelling purposes. • ANN is applied to model the building stock. • Uncertainty in building plays a major role in the building energy performance. • Results show that ANN could help to develop building energy labelling systems

Communication Network Architectures Based on Ethernet Passive Optical Network for Offshore Wind Power Farms

Directory of Open Access Journals (Sweden)

Mohamed A. Ahmed

2016-03-01

Full Text Available Nowadays, with large-scale offshore wind power farms (WPFs becoming a reality, more efforts are needed to maintain a reliable communication network for WPF monitoring. Deployment topologies, redundancy, and network availability are the main items to enhance the communication reliability between wind turbines (WTs and control centers. Traditional communication networks for monitoring and control (i.e., supervisory control and data acquisition (SCADA systems using switched gigabit Ethernet will not be sufficient for the huge amount of data passing through the network. In this paper, the optical power budget, optical path loss, reliability, and network cost of the proposed Ethernet Passive Optical Network (EPON-based communication network for small-size offshore WPFs have been evaluated for five different network architectures. The proposed network model consists of an optical network unit device (ONU deployed on the WT side for collecting data from different internal networks. All ONUs from different WTs are connected to a central optical line terminal (OLT, placed in the control center. There are no active electronic elements used between the ONUs and the OLT, which reduces the costs and complexity of maintenance and deployment. As fiber access networks without any protection are characterized by poor reliability, three different protection schemes have been configured, explained, and discussed. Considering the cost of network components, the total implementation expense of different architectures with, or without, protection have been calculated and compared. The proposed network model can significantly contribute to the communication network architecture for next generation WPFs.
Measuring distances between complex networks

International Nuclear Information System (INIS)

Andrade, Roberto F.S.; Miranda, Jose G.V.; Pinho, Suani T.R.; Lobao, Thierry Petit

2008-01-01

A previously introduced concept of higher order neighborhoods in complex networks, [R.F.S. Andrade, J.G.V. Miranda, T.P. Lobao, Phys. Rev. E 73 (2006) 046101] is used to define a distance between networks with the same number of nodes. With such measure, expressed in terms of the matrix elements of the neighborhood matrices of each network, it is possible to compare, in a quantitative way, how far apart in the space of neighborhood matrices two networks are. The distance between these matrices depends on both the network topologies and the adopted node numberings. While the numbering of one network is fixed, a Monte Carlo algorithm is used to find the best numbering of the other network, in the sense that it minimizes the distance between the matrices. The minimal value found for the distance reflects differences in the neighborhood structures of the two networks that arise only from distinct topologies. This procedure ends up by providing a projection of the first network on the pattern of the second one. Examples are worked out allowing for a quantitative comparison for distances among distinct networks, as well as among distinct realizations of random networks
Towards Optimal Transport Networks

Directory of Open Access Journals (Sweden)

Erik P. Vargo

2010-08-01

Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly aﬀect the network dynamics. First, we present simulation results which support a connection between maximizing the ﬁrst non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semideﬁnite programming (SDP relaxations. A modiﬁcation of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.
Association and Centrality in Criminal Networks

DEFF Research Database (Denmark)

Petersen, Rasmus Rosenqvist

Network-based techniques are widely used in criminal investigations because patterns of association are actionable and understandable. Existing network models with nodes as first class entities and their related measures (e.g., social networks and centrality measures) are unable to capture...
Brain tumor segmentation with Deep Neural Networks.

Science.gov (United States)

Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

2017-01-01

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.
Force10 networks performance in world's first transcontinental 10 gigabit ethernet network verified by Ixia

CERN Multimedia

2003-01-01

Force10 Networks, Inc., today announced that the performance of the Force10 E-Series switch/routers deployed in a transcontinental network has been verified as line-rate 10 GE throughput by Ixia, a leading provider of high-speed, network performance and conformance analysis systems. The network, the world's first transcontinental 10 GE wide area network, consists of a SURFnet OC-192 lambda between Geneva and the StarLight facility in Chicago via Amsterdam and another OC-192 lambda between this same facility in Chicago and Carleton University in Ottawa, Canada provided by CANARIE and ORANO (1/2 page).
A critical analysis of the implementation of social networking as an e-recruitment tool within a security enterprise

Directory of Open Access Journals (Sweden)

Anthony Lewis

2015-12-01

Full Text Available Many enterprises are operating in complex and competitive environments, and changes in the internal and external environment have prompted them to engage in better ways of doing business. In order to respond to these changes, and survive in today’s volatile business environment, enterprises need to change their strategies. Human Resource departments are under pressure to keep operating costs low whilst also ensuring they are attracting, recruiting, and retaining talent within the enterprise. To achieve this, an increasing number of enterprises have adopted social networking into their recruitment strategy. This research aims to critically analyze the implementation of social networking as an e-recruitment tool within a Security Enterprise. The research key objective is to examine the importance of attracting Generation Y through the use of social networking sites and also to develop an understanding of the advantages and disadvantages of using social networking as an e-recruitment tool. The research also looks at contemporary examples of enterprises that have implemented social networking into their recruitment strategy. A further objective of the research is to gain an understanding of the attitudes and perceptions of the use of social networking as an e-recruitment tool. To achieve this, the research has taken a mixed-methods approach whilst focusing on an interpretivist stance. Data was gathered through an interview with the HR Manager at the Security Enterprise and a questionnaire was distributed to 22 employees within the enterprise and 84 respondents on social networking sites. The overall attitudes and perceptions of respondents showed that social networking can be effectively used as an e-recruitment tool as long as a traditional recruitment method is also used.
Implicitly Defined Neural Networks for Sequence Labeling

Science.gov (United States)

2017-07-31

ularity has soared for the Long Short - Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) and vari- ants such as Gated Recurrent Unit (GRU) (Cho et...610. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short - term memory . Neural computation 9(8):1735– 1780. Zhiheng Huang, Wei Xu, and Kai Yu. 2015...network are coupled together, in order to improve perfor- mance on complex, long -range dependencies in either direction of a sequence. We contrast our
TTEthernet for Integrated Spacecraft Networks

Science.gov (United States)

Loveless, Andrew

2015-01-01

Aerospace projects have traditionally employed federated avionics architectures, in which each computer system is designed to perform one specific function (e.g. navigation). There are obvious downsides to this approach, including excessive weight (from so much computing hardware), and inefficient processor utilization (since modern processors are capable of performing multiple tasks). There has therefore been a push for integrated modular avionics (IMA), in which common computing platforms can be leveraged for different purposes. This consolidation of multiple vehicle functions to shared computing platforms can significantly reduce spacecraft cost, weight, and design complexity. However, the application of IMA principles introduces significant challenges, as the data network must accommodate traffic of mixed criticality and performance levels - potentially all related to the same shared computer hardware. Because individual network technologies are rarely so competent, the development of truly integrated network architectures often proves unreasonable. Several different types of networks are utilized - each suited to support a specific vehicle function. Critical functions are typically driven by precise timing loops, requiring networks with strict guarantees regarding message latency (i.e. determinism) and fault-tolerance. Alternatively, non-critical systems generally employ data networks prioritizing flexibility and high performance over reliable operation. Switched Ethernet has seen widespread success filling this role in terrestrial applications. Its high speed, flexibility, and the availability of inexpensive commercial off-the-shelf (COTS) components make it desirable for inclusion in spacecraft platforms. Basic Ethernet configurations have been incorporated into several preexisting aerospace projects, including both the Space Shuttle and International Space Station (ISS). However, classical switched Ethernet cannot provide the high level of network
Immunization of networks with community structure

International Nuclear Information System (INIS)

Masuda, Naoki

2009-01-01

In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.
Alpha spectral analysis via artificial neural networks

International Nuclear Information System (INIS)

Kangas, L.J.; Hashem, S.; Keller, P.E.; Kouzes, R.T.; Troyer, G.L.

1994-10-01

An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. The investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system
Bimodal Networks as Candidates for Electroactive Polymers

DEFF Research Database (Denmark)

Bahrt, Frederikke; Daugaard, Anders Egede; Bejenariu, Anca Gabriela

An alternative network formulation method was adopted in order to obtain a different type of silicone based elastomeric systems - the so-called bimodal networks - using two vinyl-terminated polydimethyl siloxanes (PDMS) of different molecular weight, a labelled crosslinker (3 or 4-functional), an...... themselves between the long chains and show how this leads to unexpectedly good properties for DEAP purposes due both to the low extensibility of the short chains that attach strongly the long chains and to the extensibility of the last ones that retards the rupture process....
Official Labeling, Criminal Embeddedness, and Subsequent Delinquency: A Longitudinal Test of Labeling Theory

Science.gov (United States)

Bernburg, Jon Gunnar; Krohn, Marvin D.; Rivera, Craig J.

2006-01-01

This article examines the short-term impact of formal criminal labeling on involvement in deviant social networks and increased likelihood of subsequent delinquency. According to labeling theory, formal criminal intervention should affect the individual's immediate social networks. In many cases, the stigma of the criminal status may increase the…
Power-Hop: A Pervasive Observation for Real Complex Networks

Science.gov (United States)

2016-03-14

e.g., power grid, the Internet and the web-graph), social (e.g., friendship networks — Facebook , Gowalla—and co- authorship networks ), urban (e.g...Mislove A., Cha M. and Gummadi K.P. On the evolution of user interaction in Facebook . In Proc. Workshop on Online Social Networks 2009. doi...scale-free distribution is pervasive and describes a large variety of networks , ranging from social and urban to technological and biological networks
Predicting cryptic links in host-parasite networks.

Directory of Open Access Journals (Sweden)

Tad Dallas

2017-05-01

Full Text Available Networks are a way to represent interactions among one (e.g., social networks or more (e.g., plant-pollinator networks classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.
The Genome-Scale Integrated Networks in Microorganisms

Directory of Open Access Journals (Sweden)

Tong Hao

2018-02-01

Full Text Available The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types of the molecular networks, for example, genome-scale metabolic network (GMN, transcriptional regulatory network (TRN, and signal transduction network (STN. It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.
Spreading dynamics of an e-commerce preferential information model on scale-free networks

Science.gov (United States)

Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding

2017-02-01

In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.
Robustness of airline alliance route networks

Science.gov (United States)

Lordan, Oriol; Sallan, Jose M.; Simo, Pep; Gonzalez-Prieto, David

2015-05-01

The aim of this study is to analyze the robustness of the three major airline alliances' (i.e., Star Alliance, oneworld and SkyTeam) route networks. Firstly, the normalization of a multi-scale measure of vulnerability is proposed in order to perform the analysis in networks with different sizes, i.e., number of nodes. An alternative node selection criterion is also proposed in order to study robustness and vulnerability of such complex networks, based on network efficiency. And lastly, a new procedure - the inverted adaptive strategy - is presented to sort the nodes in order to anticipate network breakdown. Finally, the robustness of the three alliance networks are analyzed with (1) a normalized multi-scale measure of vulnerability, (2) an adaptive strategy based on four different criteria and (3) an inverted adaptive strategy based on the efficiency criterion. The results show that Star Alliance has the most resilient route network, followed by SkyTeam and then oneworld. It was also shown that the inverted adaptive strategy based on the efficiency criterion - inverted efficiency - shows a great success in quickly breaking networks similar to that found with betweenness criterion but with even better results.
Eight challenges for network epidemic models

Directory of Open Access Journals (Sweden)

Lorenzo Pellis

2015-03-01

Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.
Integrating networks with Mathematica

NARCIS (Netherlands)

Strijkers, R.J.; Meijer, R.J.

2008-01-01

We have developed a concept that considers network behavior as a collection of software objects, which can be used or modified in computer programs. The interfaces of these software objects are exposed as web services and enable applications to analyze and manipulate networks, e.g. to find

Classification of Urban Aerial Data Based on Pixel Labelling with Deep Convolutional Neural Networks and Logistic Regression

Science.gov (United States)

Yao, W.; Poleswki, P.; Krzystek, P.

2016-06-01

The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN's texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.
Building AN International Polar Data Coordination Network

Science.gov (United States)

Pulsifer, P. L.; Yarmey, L.; Manley, W. F.; Gaylord, A. G.; Tweedie, C. E.

2013-12-01

In the spirit of the World Data Center system developed to manage data resulting from the International Geophysical Year of 1957-58, the International Polar Year 2007-2009 (IPY) resulted in significant progress towards establishing an international polar data management network. However, a sustained international network is still evolving. In this paper we argue that the fundamental building blocks for such a network exist and that the time is right to move forward. We focus on the Arctic component of such a network with linkages to Antarctic network building activities. A review of an important set of Network building blocks is presented: i) the legacy of the IPY data and information service; ii) global data management services with a polar component (e.g. World Data System); iii) regional systems (e.g. Arctic Observing Viewer; iv) nationally focused programs (e.g. Arctic Observing Viewer, Advanced Cooperative Arctic Data and Information Service, Polar Data Catalogue, Inuit Knowledge Centre); v) programs focused on the local (e.g. Exchange for Local Observations and Knowledge of the Arctic, Geomatics and Cartographic Research Centre). We discuss current activities and results with respect to three priority areas needed to establish a strong and effective Network. First, a summary of network building activities reports on a series of productive meetings, including the Arctic Observing Summit and the Polar Data Forum, that have resulted in a core set of Network nodes and participants and a refined vision for the Network. Second, we recognize that interoperability for information sharing fundamentally relies on the creation and adoption of community-based data description standards and data delivery mechanisms. There is a broad range of interoperability frameworks and specifications available; however, these need to be adapted for polar community needs. Progress towards Network interoperability is reviewed, and a prototype distributed data systems is demonstrated. We
Autonomous vision networking: miniature wireless sensor networks with imaging technology

Science.gov (United States)

Messinger, Gioia; Goldberg, Giora

2006-09-01

. Image processing at the sensor node level may also be required for applications in security, asset management and process control. Due to the data bandwidth requirements posed on the network by video sensors, new networking protocols or video extensions to existing standards (e.g. Zigbee) are required. To this end, Avaak has designed and implemented an ultra-low power networking protocol designed to carry large volumes of data through the network. The low power wireless sensor nodes that will be discussed include a chemical sensor integrated with a CMOS digital camera, a controller, a DSP processor and a radio communication transceiver, which enables relaying of an alarm or image message, to a central station. In addition to the communications, identification is very desirable; hence location awareness will be later incorporated to the system in the form of Time-Of-Arrival triangulation, via wide band signaling. While the wireless imaging kernel already exists specific applications for surveillance and chemical detection are under development by Avaak, as part of a co-founded program from ONR and DARPA. Avaak is also designing vision networks for commercial applications - some of which are undergoing initial field tests.
Overlay networks toward information networking

CERN Document Server

Tarkoma, Sasu

2010-01-01

With their ability to solve problems in massive information distribution and processing, while keeping scaling costs low, overlay systems represent a rapidly growing area of R&D with important implications for the evolution of Internet architecture. Inspired by the author's articles on content based routing, Overlay Networks: Toward Information Networking provides a complete introduction to overlay networks. Examining what they are and what kind of structures they require, the text covers the key structures, protocols, and algorithms used in overlay networks. It reviews the current state of th
Network-Friendly Gossiping

Science.gov (United States)

Serbu, Sabina; Rivière, Étienne; Felber, Pascal

The emergence of large-scale distributed applications based on many-to-many communication models, e.g., broadcast and decentralized group communication, has an important impact on the underlying layers, notably the Internet routing infrastructure. To make an effective use of network resources, protocols should both limit the stress (amount of messages) on each infrastructure entity like routers and links, and balance as much as possible the load in the network. Most protocols use application-level metrics such as delays to improve efficiency of content dissemination or routing, but the extend to which such application-centric optimizations help reduce and balance the load imposed to the infrastructure is unclear. In this paper, we elaborate on the design of such network-friendly protocols and associated metrics. More specifically, we investigate random-based gossip dissemination. We propose and evaluate different ways of making this representative protocol network-friendly while keeping its desirable properties (robustness and low delays). Simulations of the proposed methods using synthetic and real network topologies convey and compare their abilities to reduce and balance the load while keeping good performance.
Exploring network operations for data and information networks

Science.gov (United States)

Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.
Speaker diarization system using HXLPS and deep neural network

Directory of Open Access Journals (Sweden)

V. Subba Ramaiah

2018-03-01

Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER
Research on NGN network control technology

Science.gov (United States)

Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

2004-04-01

Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.
Concurrent conditional clustering of multiple networks: COCONETS.

Directory of Open Access Journals (Sweden)

Sabrina Kleessen

Full Text Available The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components. We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by
Edge union of networks on the same vertex set

International Nuclear Information System (INIS)

Loe, Chuan Wen; Jensen, Henrik Jeldtoft

2013-01-01

Random network generators such as Erdős–Rényi, Watts–Strogatz and Barabási–Albert models are used as models to study real-world networks. Let G 1 (V, E 1 ) and G 2 (V, E 2 ) be two such networks on the same vertex set V. This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V, E 1 ∪E 2 ). (paper)
Edge union of networks on the same vertex set

Science.gov (United States)

Loe, Chuan Wen; Jeldtoft Jensen, Henrik

2013-06-01

Random network generators such as Erdős-Rényi, Watts-Strogatz and Barabási-Albert models are used as models to study real-world networks. Let G1(V, E1) and G2(V, E2) be two such networks on the same vertex set V. This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V, E1∪E2).
Developing convolutional neural networks for measuring climate change opinions from social media data

Science.gov (United States)

Mao, H.; Bhaduri, B. L.

2016-12-01

Understanding public opinions on climate change is important for policy making. Public opinion, however, is typically measured with national surveys, which are often too expensive and thus being updated at a low frequency. Twitter has become a major platform for people to express their opinions on social and political issues. Our work attempts to understand if Twitter data can provide complimentary insights about climate change perceptions. Since the nature of social media is real-time, this data source can especially help us understand how public opinion changes over time in response to climate events and hazards, which though is very difficult to be captured by manual surveys. We use the Twitter Streaming API to collect tweets that contain keywords, "climate change" or "#climatechange". Traditional machine-learning based opinion mining algorithms require a significant amount of labeled data. Data labeling is notoriously time consuming. To address this problem, we use hashtags (a significant feature used to mark topics of tweets) to annotate tweets automatically. For example, hashtags, #climatedenial and #climatescam, are negative opinion labels, while #actonclimate and #climateaction are positive. Following this method, we can obtain a large amount of training data without human labor. This labeled dataset is used to train a deep convolutional neural network that classifies tweets into positive (i.e. believe in climate change) and negative (i.e. do not believe). Based on the positive/negative tweets obtained, we will further analyze risk perceptions and opinions towards policy support. In addition, we analyze twitter user profiles to understand the demographics of proponents and opponents of climate change. Deep learning techniques, especially convolutional deep neural networks, have achieved much success in computer vision. In this work, we propose a convolutional neural network architecture for understanding opinions within text. This method is compared with
Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

Science.gov (United States)

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

2017-01-01

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

International Nuclear Information System (INIS)

Pan Guijun; Yan Xiaoqing; Huang Zhongbing; Ma Weichuan

2011-01-01

Uncorrelated random scale-free (URSF) networks are useful null models for checking the effects of scale-free topology on network-based dynamical processes. Here, we present a comparative study of the jamming level of gradient networks based on URSF networks and Erdos-Renyi (ER) random networks. We find that the URSF networks are less congested than ER random networks for the average degree (k)>k c (k c ∼ 2 denotes a critical connectivity). In addition, by investigating the topological properties of the two kinds of gradient networks, we discuss the relations between the topological structure and the transport efficiency of the gradient networks. These findings show that the uncorrelated scale-free structure might allow more efficient transport than the random structure.
Current redistribution in resistor networks: Fat-tail statistics in regular and small-world networks.

Science.gov (United States)

Lehmann, Jörg; Bernasconi, Jakob

2017-03-01

The redistribution of electrical currents in resistor networks after single-bond failures is analyzed in terms of current-redistribution factors that are shown to depend only on the topology of the network and on the values of the bond resistances. We investigate the properties of these current-redistribution factors for regular network topologies (e.g., d-dimensional hypercubic lattices) as well as for small-world networks. In particular, we find that the statistics of the current redistribution factors exhibits a fat-tail behavior, which reflects the long-range nature of the current redistribution as determined by Kirchhoff's circuit laws.
Reconfigurable network systems and software-defined networking

OpenAIRE

Zilberman, N.; Watts, P. M.; Rotsos, C.; Moore, A. W.

2015-01-01

Modern high-speed networks have evolved from relatively static networks to highly adaptive networks facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability and configuration flexibility. This influence has extended from the lowest level of physical hardware interfaces to the highest level of network management by software. A key representative of this evolution is the emergence of software-defined n...
Improving network management with Software Defined Networking

International Nuclear Information System (INIS)

Dzhunev, Pavel

2013-01-01

Software-defined networking (SDN) is developed as an alternative to closed networks in centers for data processing by providing a means to separate the control layer data layer switches, and routers. SDN introduces new possibilities for network management and configuration methods. In this article, we identify problems with the current state-of-the-art network configuration and management mechanisms and introduce mechanisms to improve various aspects of network management
Optimized energy-delay sub-network routing protocol development and implementation for wireless sensor networks

International Nuclear Information System (INIS)

Fonda, James W; Zawodniok, Maciej; Jagannathan, S; Watkins, Steve E

2008-01-01

The development and the implementation issues of a reactive optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) are introduced and its performance is contrasted with the popular ad hoc on-demand distance vector (AODV) routing protocol. Analytical results illustrate the performance of the proposed OEDSR protocol, while experimental results utilizing a hardware testbed under various scenarios demonstrate improvements in energy efficiency of the OEDSR protocol. A hardware platform constructed at the University of Missouri-Rolla (UMR), now the Missouri University of Science and Technology (MST), based on the Generation 4 Smart Sensor Node (G4-SSN) prototyping platform is also described. Performance improvements are shown in terms of end-to-end (E2E) delay, throughput, route-set-up time and drop rates and energy usage is given for three topologies, including a mobile topology. Additionally, results from the hardware testbed provide valuable lessons for network deployments. Under testing OEDSR provides a factor of ten improvement in the energy used in the routing session and extends network lifetime compared to AODV. Depletion experiments show that the time until the first node failure is extended by a factor of three with the network depleting and network lifetime is extended by 6.7%
Network Restoration for Next-Generation Communication and Computing Networks

Directory of Open Access Journals (Sweden)

B. S. Awoyemi

2018-01-01

Full Text Available Network failures are undesirable but inevitable occurrences for most modern communication and computing networks. A good network design must be robust enough to handle sudden failures, maintain traffic flow, and restore failed parts of the network within a permissible time frame, at the lowest cost achievable and with as little extra complexity in the network as possible. Emerging next-generation (xG communication and computing networks such as fifth-generation networks, software-defined networks, and internet-of-things networks have promises of fast speeds, impressive data rates, and remarkable reliability. To achieve these promises, these complex and dynamic xG networks must be built with low failure possibilities, high network restoration capacity, and quick failure recovery capabilities. Hence, improved network restoration models have to be developed and incorporated in their design. In this paper, a comprehensive study on network restoration mechanisms that are being developed for addressing network failures in current and emerging xG networks is carried out. Open-ended problems are identified, while invaluable ideas for better adaptation of network restoration to evolving xG communication and computing paradigms are discussed.
Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data

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Svyatoslav Vergun

2016-09-01

Full Text Available Functional magnetic resonance imaging studies have significantly expanded the field’s understanding of functional brain activity of healthy and patient populations. Resting state (rs- fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RNS labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor and executive control resting state networks from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects. ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron. The method’s utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians’ expert interpretation.

Optical Access Networks

Science.gov (United States)

Zheng, Jun; Ansari, Nirwan

2005-01-01

have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. Scope of Contributions This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to: Optical access network architectures and protocols Passive optical networks (BPON, EPON, GPON, etc.) Active optical networks Multiple access control Multiservices and QoS provisioning Network survivability Field trials and standards Performance modeling and analysis Manuscript Submission To submit to this special issue, follow the normal procedure for submission to JON, indicating ``Optical Access Networks feature' in the ``Comments' field of the online submission form. For all other questions relating to this feature issue, please send an e-mail to jon@osa.org, subject line ``Optical Access Networks' Additional information can be found on the JON website: http://www.osa-jon.org/submission/. Submission Deadline: 1 June 2005
The Effect of Social Network Diagrams on a Virtual Network of Practice: A Korean Case

Science.gov (United States)

Jo, Il-Hyun

2009-01-01

This study investigates the effect of the presentation of social network diagrams on virtual team members' interaction behavior via e-mail. E-mail transaction data from 22 software developers in a Korean IT company was analyzed and depicted as diagrams by social network analysis (SNA), and presented to the members as an intervention. Results…
Routing in Optical and Stochastic Networks

NARCIS (Netherlands)

Yang, S.

2015-01-01

In most types of networks (e.g., optical or transportation networks), finding one or more best paths from a source to a destination, is one of the biggest concerns of network users and providers. This process is known as routing. The routing problems differ accordingly depending on different
An Application of Social Network Analysis on Military Strategy, System Networks and the Phases of War

Science.gov (United States)

2015-03-26

power grid network also used by Watts and Strogatz [53]. A summary of all exemplar networks is located in Table 9 below: Table 9: Full Exemplar...53] D. J. Watts and S. H. Strogatz , “Collective Dynamics of “Small World” networks,” Nature, no. 393, pp. 440-442, 1998. [54] M. E. Porter
Improved Vehicular Information Network Architecture Using Fuzzy Based Named Data NetworkingNDN

Directory of Open Access Journals (Sweden)

Kanwalpreet Kaur

2015-08-01

Full Text Available Vehicular Ad-hoc System VANETs is really a component with smart transport systems. It has ability to prevent accidents and the road congestion issues on highways but it suffers from the accomplishment and scalability issues. To handle these difficulties from the Inter Vehicular Communication IVC we apply Name Data Networking NDN. All though in NDN the users are only concerned about necessary data and give no attention on the number of locations from where the data is coming. The NDN layout is usually much more worthy for IVC circumstance getting the ordered material labeling design as well as amp64258exible material retrieval. In this report we propose vehicular network dependent on fuzzy membership function which offers the fundamental NDN style to improve support location dependent forwarding content aggregation and distributed mobility management. This paper finally winds up the several boundaries regarding earlier approaches.
Breakdown of interdependent directed networks.

Science.gov (United States)

Liu, Xueming; Stanley, H Eugene; Gao, Jianxi

2016-02-02

Increasing evidence shows that real-world systems interact with one another via dependency connectivities. Failing connectivities are the mechanism behind the breakdown of interacting complex systems, e.g., blackouts caused by the interdependence of power grids and communication networks. Previous research analyzing the robustness of interdependent networks has been limited to undirected networks. However, most real-world networks are directed, their in-degrees and out-degrees may be correlated, and they are often coupled to one another as interdependent directed networks. To understand the breakdown and robustness of interdependent directed networks, we develop a theoretical framework based on generating functions and percolation theory. We find that for interdependent Erdős-Rényi networks the directionality within each network increases their vulnerability and exhibits hybrid phase transitions. We also find that the percolation behavior of interdependent directed scale-free networks with and without degree correlations is so complex that two criteria are needed to quantify and compare their robustness: the percolation threshold and the integrated size of the giant component during an entire attack process. Interestingly, we find that the in-degree and out-degree correlations in each network layer increase the robustness of interdependent degree heterogeneous networks that most real networks are, but decrease the robustness of interdependent networks with homogeneous degree distribution and with strong coupling strengths. Moreover, by applying our theoretical analysis to real interdependent international trade networks, we find that the robustness of these real-world systems increases with the in-degree and out-degree correlations, confirming our theoretical analysis.
Ad-hoc networking towards seamless communications

CERN Document Server

Gavrilovska, Liljana

2006-01-01

Ad-Hoc Networking Towards Seamless Communications is dedicated to an area that attracts growing interest in academia and industry and concentrates on wireless ad hoc networking paradigm. The persistent efforts to acquire the ability to establish dynamic wireless connections from anywhere to anyone with any device without prerequisite imbedded infrastructure move the communications boundaries towards ad-hoc networks. Recently, ad hoc networking has attracted growing interest due to advances in wireless communications, and developed framework for running IP based protocols. The expected degree of penetration of these networks will depend on the successful resolution of the key features. Ad-hoc networks pose many complex and open problems for researchers. Ad-Hoc Networking Towards Seamless Communications reveals the state-of-the-art in wireless ad-hoc networking and discusses some of the key research topics that are expected to promote and accelerate the commercial applications of these networks (e.g., MAC, rout...
Network-based functional enrichment

Directory of Open Access Journals (Sweden)

Poirel Christopher L

2011-11-01

Full Text Available Abstract Background Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Results Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i determine which functions are enriched in a given network, ii given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. Conclusions We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are
Advanced optical components for next-generation photonic networks

Science.gov (United States)

Yoo, S. J. B.

2003-08-01

Future networks will require very high throughput, carrying dominantly data-centric traffic. The role of Photonic Networks employing all-optical systems will become increasingly important in providing scalable bandwidth, agile reconfigurability, and low-power consumptions in the future. In particular, the self-similar nature of data traffic indicates that packet switching and burst switching will be beneficial in the Next Generation Photonic Networks. While the natural conclusion is to pursue Photonic Packet Switching and Photonic Burst Switching systems, there are significant challenges in realizing such a system due to practical limitations in optical component technologies. Lack of a viable all-optical memory technology will continue to drive us towards exploring rapid reconfigurability in the wavelength domain. We will introduce and discuss the advanced optical component technologies behind the Photonic Packet Routing system designed and demonstrated at UC Davis. The system is capable of packet switching and burst switching, as well as circuit switching with 600 psec switching speed and scalability to 42 petabit/sec aggregated switching capacity. By utilizing a combination of rapidly tunable wavelength conversion and a uniform-loss cyclic frequency (ULCF) arrayed waveguide grating router (AWGR), the system is capable of rapidly switching the packets in wavelength, time, and space domains. The label swapping module inside the Photonic Packet Routing system containing a Mach-Zehnder wavelength converter and a narrow-band fiber Bragg-grating achieves all-optical label swapping with optical 2R (potentially 3R) regeneration while maintaining optical transparency for the data payload. By utilizing the advanced optical component technologies, the Photonic Packet Routing system successfully demonstrated error-free, cascaded, multi-hop photonic packet switching and routing with optical-label swapping. This paper will review the advanced optical component technologies
Computer networks ISE a systems approach

CERN Document Server

Peterson, Larry L

2007-01-01

Computer Networks, 4E is the only introductory computer networking book written by authors who have had first-hand experience with many of the protocols discussed in the book, who have actually designed some of them as well, and who are still actively designing the computer networks today. This newly revised edition continues to provide an enduring, practical understanding of networks and their building blocks through rich, example-based instruction. The authors' focus is on the why of network design, not just the specifications comprising today's systems but how key technologies and p
Learning Networks, Networked Learning

NARCIS (Netherlands)

Sloep, Peter; Berlanga, Adriana

2010-01-01

Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05
Impact of constrained rewiring on network structure and node dynamics

Science.gov (United States)

Rattana, P.; Berthouze, L.; Kiss, I. Z.

2014-11-01

In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.
Research Award: Informaon and Networks

International Development Research Centre (IDRC) Digital Library (Canada)

Corey Piccioni

2013-08-07

Aug 7, 2013 ... IDRC's Informaon and Networks (I&N) program is seeking a Research ... The growth of networked technologies has created new opportunies for ... What role do collaborave technologies (e.g., social media) play in social ...
Network cosmology.

Science.gov (United States)

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.
Network marketing on a small-world network

Science.gov (United States)

Kim, Beom Jun; Jun, Tackseung; Kim, Jeong-Yoo; Choi, M. Y.

2006-02-01

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts-Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.
An Intelligent Network Proposed for Assessing Seismic Vulnerability Index of Sewerage Networks within a GIS Framework (A Case Study of Shahr-e-Kord

Directory of Open Access Journals (Sweden)

Mohamadali Rahgozar

2016-01-01

Full Text Available Due to their vast spread, sewerage networks are exposed to considerable damages during severe earthquakes, which may lead to catastrophic environmental contamination. Multiple repairs in the pipelines, including pipe and joint fractures, could be costly and time-consuming. In seismic risk management, it is of utmost importance to have an intelligent tool for assessing seismic vulnerability index at any given point in time for such important utilities as sewerage networks. This study uses a weight-factor methodology and proposes an online GIS-based intelligent algorithm to evaluate the seismic vulnerability index (VI for metropolitan sewerage networks. The proposed intelligent tool is capable of updating VI as the sewerage network conditions may change with time and at different locations. The city of Shahr-e-Kord located on the high risk seismic belt is selected for a case study to which the proposed methodology is applied for zoning the vulnerability index in GIS. Results show that the overall seismic vulnerability index for the selected study area ranges from low to medium but that it increases in the southern parts of the city, especially in the old town where brittle pipes have been laid
Tie Content in Professional Networks

DEFF Research Database (Denmark)

Zarzecka, Olga

in resource exchanges and the effect of these differences on the number of, and extent to which, resources are provided by a network tie. Chapter 3 explores how firm underperfomance and social identity with corporate elite alter types of resources a network tie provides. Chapter 4 focuses on a tie’s internal......Professional networks of senior managers have indisputable value for them as well as for their organizations. In recent years, much attention has been given to the structure of these networks as it reflects senior managers’ opportunity to access valuable resources. Surprisingly, the actual...... resources that senior managers acquire through their network ties, i.e. the tie content, remain heavily understudied. Hence, the purpose of this dissertation is to answer the following question: What resources flow through informal ties in senior managers’ professional networks, and why? The first chapter...
Weighted Scale-Free Network Properties of Ecological Network

International Nuclear Information System (INIS)

Lee, Jae Woo; Maeng, Seong Eun

2013-01-01

We investigate the scale-free network properties of the bipartite ecological network, in particular, the plant-pollinator network. In plant-pollinator network, the pollinators visit the plant to get the nectars. In contrast to the other complex network, the plant-pollinator network has not only the trophic relationships among the interacting partners but also the complexities of the coevolutionary effects. The interactions between the plant and pollinators are beneficial relations. The plant-pollinator network is a bipartite and weighted network. The networks have two types of the nodes: plant and pollinator. We consider the visiting frequency of a pollinator to a plant as the weighting value of the link. We defined the strength of a node as the sum of the weighting value of the links. We reported the cumulative distribution function (CDF) of the degree and the strength of the plant-pollinator network. The CDF of the plants followed stretched exponential functions for both degree and strength, but the CDF of the pollinators showed the power law for both degree and strength. The average strength of the links showed the nonlinear dependence on the degree of the networks.
Robust classification using mixtures of dependency networks

DEFF Research Database (Denmark)

Gámez, José A.; Mateo, Juan L.; Nielsen, Thomas Dyhre

2008-01-01

Dependency networks have previously been proposed as alternatives to e.g. Bayesian networks by supporting fast algorithms for automatic learning. Recently dependency networks have also been proposed as classiﬁcation models, but as with e.g. general probabilistic inference, the reported speed......-ups are often obtained at the expense of accuracy. In this paper we try to address this issue through the use of mixtures of dependency networks. To reduce learning time and improve robustness when dealing with data sparse classes, we outline methods for reusing calculations across mixture components. Finally...
Ranking Entities in Networks via Lefschetz Duality

DEFF Research Database (Denmark)

Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Poulsen, Bjarne

2014-01-01

then be ranked according to how essential their positions are in the network by considering the effect of their respective absences. Defining a ranking of a network which takes the individual position of each entity into account has the purpose of assigning different roles to the entities, e.g. agents......, in the network. In this paper it is shown that the topology of a given network induces a ranking of the entities in the network. Further, it is demonstrated how to calculate this ranking and thus how to identify weak sub-networks in any given network....

Super-resolution optical microscopy resolves network morphology of smart colloidal microgels.

Science.gov (United States)

Bergmann, Stephan; Wrede, Oliver; Huser, Thomas; Hellweg, Thomas

2018-02-14

We present a new method to resolve the network morphology of colloidal particles in an aqueous environment via super-resolution microscopy. By localization of freely diffusing fluorophores inside the particle network we can resolve the three dimensional structure of one species of colloidal particles (thermoresponsive microgels) without altering their chemical composition through copolymerization with fluorescent monomers. Our approach utilizes the interaction of the fluorescent dye rhodamine 6G with the polymer network to achieve an indirect labeling. We calculate the 3D structure from the 2D images and compare the structure to previously published models for the microgel morphology, e.g. the fuzzy sphere model. To describe the differences in the data an extension of this model is suggested. Our method enables the tailor-made fabrication of colloidal particles which are used in various applications, such as paints or cosmetics, and are promising candidates for drug delivery, smart surface coatings, and nanocatalysis. With the precise knowledge of the particle morphology an understanding of the underlying structure-property relationships for various colloidal systems is possible.
E3 Success Story - Working Together: E3 Ohio and the Ohio By-Product Synergy Network

Science.gov (United States)

The Mid-Ohio Regional Planning Commission (MORPC) received funding to support the integration of the national E3 sustainability initiative with the Ohio By-Product Synergy (BPS) Network to create an efficient and replicable model for reducing GHGs.
Frontal parietal control network regulates the anti-correlated default and dorsal attention networks.

Science.gov (United States)

Gao, Wei; Lin, Weili

2012-01-01

Recent reports demonstrate the anti-correlated behaviors between the default (DF) and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks-FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. Copyright © 2011 Wiley Periodicals, Inc.
Advanced business process management in networked E-business scenarios

NARCIS (Netherlands)

Grefen, P.W.P.J.; Türetken, O.

2017-01-01

In the modern economy, we see a shift towards networked business scenarios. In many contemporary situations, the operation of multiple organizations is tightly coupled in collaborative business networks. To allow this tightly coupled collaboration, business process management (BPM) in these
Network stigma towards people living with HIV/AIDS and their caregivers: An egocentric network study.

Science.gov (United States)

Wu, Fei; He, Xin; Guida, Jennifer; Xu, Yongfang; Liu, Hongjie

2015-10-01

HIV stigma occurs among peers in social networks. However, the features of social networks that drive HIV stigma are not well understood. The objective of this study is to investigate anticipated HIV stigma within the social networks of people living with HIV/AIDS (PLWHA) (N = 147) and the social networks of PLWHA's caregivers (N = 148). The egocentric social network data were collected in Guangxi, China. More than half of PLWHA (58%) and their caregivers (53%) anticipated HIV stigma from their network peers. Both PLWHA and their caregivers anticipated that spouses or other family members were less likely to stigmatise them, compared to friend peers or other relationships. Married network peers were believed to stigmatise caregivers more than unmarried peers. The association between frequent contacts and anticipated stigma was negative among caregivers. Being in a close relationship with PLWHA or caregivers (e.g., a spouse or other family member) was associated with less anticipated stigma. Lower network density was associated with higher anticipated stigma among PLWHA's alters, but not among caregivers' alters. Findings may shed light on innovative stigma reduction interventions at the social network level and therefore improve HIV/AIDS treatment utilisation.
Network dynamics and its relationships to topology and coupling structure in excitable complex networks

International Nuclear Information System (INIS)

Zhang Li-Sheng; Mi Yuan-Yuan; Gu Wei-Feng; Hu Gang

2014-01-01

All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend on network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks (ECNs) and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns (e.g., periodic, quasiperiodic, and chaotic). In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies (random or scale-free topologies) and different interaction structures (symmetric or asymmetric couplings). The mechanisms behind these differences are explained physically. (interdisciplinary physics and related areas of science and technology)
Thermoelectric properties of semiconductor nanowire networks

Science.gov (United States)

Roslyak, Oleksiy; Piryatinski, Andrei

2016-03-01

To examine the thermoelectric (TE) properties of a semiconductor nanowire (NW) network, we propose a theoretical approach mapping the TE network on a two-port network. In contrast to a conventional single-port (i.e., resistor) network model, our model allows for large scale calculations showing convergence of TE figure of merit, ZT, with an increasing number of junctions. Using this model, numerical simulations are performed for the Bi2Te3 branched nanowire (BNW) and Cayley tree NW (CTNW) network. We find that the phonon scattering at the network junctions plays a dominant role in enhancing the network ZT. Specifically, disordered BNW and CTNW demonstrate an order of magnitude higher ZT enhancement compared to their ordered counterparts. Formation of preferential TE pathways in CTNW makes the network effectively behave as its BNW counterpart. We provide formalism for simulating large scale nanowire networks hinged upon experimentally measurable TE parameters of a single T-junction.
A Network Traffic Control Enhancement Approach over Bluetooth Networks

DEFF Research Database (Denmark)

Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

2003-01-01

This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated solu...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well......This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...
Network structure exploration in networks with node attributes

Science.gov (United States)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.
LHCb DAQ network upgrade tests

CERN Document Server

Pisani, Flavio

2013-01-01

My project concerned the evaluation of new technologies for the DAQ network upgrade of LHCb. The first part consisted in developing and Open Flow-based Clos network. This new technology is very interesting and powerful but, as shown by the results, it still needs further improvements. The second part consisted in testing and benchmarking 40GbE network equipment: Mellanox MT27500, Chelsio T580 and Huawei Cloud Engine 12804. An event-building simulation is currently been performed in order to check the feasibility of the DAQ network upgrade in LS2. The first results are promising.
Social Networks and Sales Performance

Directory of Open Access Journals (Sweden)

Danny Pimentel Claro

2011-05-01

Full Text Available This paper argues that an informal network can itself be a basis for the increase in a sales manager’s performance. Informal networks create a structure that surpasses the formal hierarchical structure defined by the firm. We concentrated on the advice network and considered two different views of network structure that claim to have impact on performance. To explore this claim, we examined whether sales managers develop either a highly cohesive network structure (i.e. Coleman’s view or one containing structural holes (i.e. Burt’s view in order to achieve higher sales. We also investigated the matter of tie strength put forward by Granovetter in his seminal 1973 work. Census data was collected from about 500 personnel from an agricultural input retailer having 23 divisions. Estimates from a sample of 101 sales managers showed the importance of a highly cohesive structure (degree centrality for the three measures of sales manager’s performance. The strong ties have a positive impact on performance, suggesting the importance of building up strong bonds with network contacts. Sales managers’ age, time within the retailer and education also influence performance. These results imply that firms should stimulate contacts among personnel to spread technical and commercial information.
Decreased triple network connectivity in patients with post-traumatic stress disorder

Science.gov (United States)

Liu, Yang; Li, Liang; Li, Baojuan; Zhang, Xi; Lu, Hongbing

2017-03-01

The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With arterial spin labeling sequence, three networks were identified using independent component analysis in 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus was identified to associate with clinical severity. These results indicated the decreased triple network connectivity, which not only supported the proposal of the triple network model, but also prompted possible neurobiology mechanism of cognitive dysfunction for this kind of PTSD.
The Fragility of Interdependency: Coupled Networks Switching Phenomena

Science.gov (United States)

Stanley, H. Eugene

2013-03-01

Recent disasters ranging from abrupt financial ``flash crashes'' and large-scale power outages to sudden death among the elderly dramatically exemplify the fact that the most dangerous vulnerability is hiding in the many interdependencies among different networks. In the past year, we have quantified failures in model of interconnected networks, and demonstrated the need to consider mutually dependent network properties in designing resilient systems. Specifically, we have uncovered new laws governing the nature of switching phenomena in coupled networks, and found that phenomena that are continuous ``second order'' phase transitions in isolated networks become discontinuous abrupt ``first order'' transitions in interdependent networks [S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, and S. Havlin, ``Catastrophic Cascade of Failures in Interdependent Networks,'' Nature 464, 1025 (2010); J. Gao, S. V. Buldyrev, H. E. Stanley, and S. Havlin, ``Novel Behavior of Networks Formed from Interdependent Networks,'' Nature Physics 8, 40 (2012). We conclude by discussing the network basis for understanding sudden death in the elderly, and the possibility that financial ``flash crashes'' are not unlike the catastrophic first-order failure incidents occurring in coupled networks. Specifically, we study the coupled networks that are responsible for financial fluctuations. It appears that ``trend switching phenomena'' that we uncover are remarkably independent of the scale over which they are analyzed. For example, we find that the same laws governing the formation and bursting of the largest financial bubbles also govern the tiniest finance bubbles, over a factor of 1,000,000,000 in time scale [T. Preis, J. Schneider, and H. E. Stanley, ``Switching Processes in Financial Markets,'' Proc. Natl. Acad. Sci. USA 108, 7674 (2011); T. Preis and H. E. Stanley, ``Bubble Trouble: Can a Law Describe Bubbles and Crashes in Financial Markets?'' Physics World 24, No. 5, 29 (May 2011
Optimal urban networks via mass transportation

CERN Document Server

Buttazzo, Giuseppe; Stepanov, Eugene; Solimini, Sergio

2009-01-01

Recently much attention has been devoted to the optimization of transportation networks in a given geographic area. One assumes the distributions of population and of services/workplaces (i.e. the network's sources and sinks) are known, as well as the costs of movement with/without the network, and the cost of constructing/maintaining it. Both the long-term optimization and the short-term, "who goes where" optimization are considered. These models can also be adapted for the optimization of other types of networks, such as telecommunications, pipeline or drainage networks. In the monograph we study the most general problem settings, namely, when neither the shape nor even the topology of the network to be constructed is known a priori.
Report on Asian Environment Information Network; 'Asia kankyo joho network' ni kansuru hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-03-01

The goal is the construction of Asian Environment Information Network (AEInet) in accordance with a contract signed between Indonesia's LIPI (Indonesian Institute of Science) and NEDO under NEDO's Research Cooperation Project Concerning the Development of Environment Measuring Laser Radar (LR). The network is so designed and constituted as to operate on a private line between Indonesia and Japan via IP (Internet protocol) and to enable the exchange on the Internet network of the data collected/analyzed by the Indonesian LR system and of articles of e-mail between scientists of the two countries. The AEInet will be utilized for the collection/analysis of LR-collected data; exchange of observed data and the result of processing; provision of support to environment information scientists in exchanging e-mail and information; and the search of databases for the implementation of the project. In this paper, the outline and functions of the system, network system design, WWW server construction, network operating status, joint researches with Indonesia, etc., are described. (NEDO)
Networked Microgrids Scoping Study

Energy Technology Data Exchange (ETDEWEB)

Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Dobriansky, Larisa [General MicroGrids, San Diego, CA (United States); Glover, Steve [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Liu, Chen-Ching [Washington State Univ., Pullman, WA (United States); Looney, Patrick [Brookhaven National Lab. (BNL), Upton, NY (United States); Mashayekh, Salman [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Pratt, Annabelle [National Renewable Energy Lab. (NREL), Golden, CO (United States); Schneider, Kevin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Starke, Michael [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States); Yue, Meng [Brookhaven National Lab. (BNL), Upton, NY (United States)

2016-12-05

Much like individual microgrids, the range of opportunities and potential architectures of networked microgrids is very diverse. The goals of this scoping study are to provide an early assessment of research and development needs by examining the benefits of, risks created by, and risks to networked microgrids. At this time there are very few, if any, examples of deployed microgrid networks. In addition, there are very few tools to simulate or otherwise analyze the behavior of networked microgrids. In this setting, it is very difficult to evaluate networked microgrids systematically or quantitatively. At this early stage, this study is relying on inputs, estimations, and literature reviews by subject matter experts who are engaged in individual microgrid research and development projects, i.e., the authors of this study The initial step of the study gathered input about the potential opportunities provided by networked microgrids from these subject matter experts. These opportunities were divided between the subject matter experts for further review. Part 2 of this study is comprised of these reviews. Part 1 of this study is a summary of the benefits and risks identified in the reviews in Part 2 and synthesis of the research needs required to enable networked microgrids.
Declarative Networking

CERN Document Server

Loo, Boon Thau

2012-01-01

Declarative Networking is a programming methodology that enables developers to concisely specify network protocols and services, which are directly compiled to a dataflow framework that executes the specifications. Declarative networking proposes the use of a declarative query language for specifying and implementing network protocols, and employs a dataflow framework at runtime for communication and maintenance of network state. The primary goal of declarative networking is to greatly simplify the process of specifying, implementing, deploying and evolving a network design. In addition, decla
Trimaran Resistance Artificial Neural Network

Science.gov (United States)

2011-01-01

11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to
Optical packet switched networks

DEFF Research Database (Denmark)

Hansen, Peter Bukhave

1999-01-01

Optical packet switched networks are investigated with emphasis on the performance of the packet switch blocks. Initially, the network context of the optical packet switched network is described showing that a packet network will provide transparency, flexibility and bridge the granularity gap...... in interferometric wavelength converters is investigated showing that a 10 Gbit/s 19 4x4 swich blocks can be cascaded at a BER of 10-14. An analytical traffic model enables the calculation of the traffice performance of a WDM packet network. Hereby the importance of WDM and wavelegth conversion in the switch blocks...... is established as a flexible means to reduce the optical buffer, e.g., the number of fibre delay lines for a 16x16 switch block is reduced from 23 to 6 by going from 2 to 8 wavelength channels pr. inlet. Additionally, a component count analysis is carried out to illustrate the trade-offs in the switch block...
CLASSIFICATION OF URBAN AERIAL DATA BASED ON PIXEL LABELLING WITH DEEP CONVOLUTIONAL NEURAL NETWORKS AND LOGISTIC REGRESSION

Directory of Open Access Journals (Sweden)

W. Yao

2016-06-01

Full Text Available The recent success of deep convolutional neural networks (CNN on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN’s texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.

Optimizing Low Speed VoIP Network for Rural Next Generation Network (R-NGN

Directory of Open Access Journals (Sweden)

Yoanes Bandung

2007-11-01

Full Text Available In this research, we propose an optimization method based-on E-Model for designing an efficient low speed VoIP network for Rural Next Generation Network (R-NGN. We are choosing 128 kbps and 256 kbps bandwidth as the typical community link to be used in the designing of R-NGN infrastructure. The method is based on selection of some VoIP network parameters such as voice coder, communication protocol, packet loss level, network utilization and resource allocation. We draw analytic approach for achieving rating value (R of E-model that represent level of quality of service. In this approach, we focus on delay and packet loss calculation to find the rating value. We state the rating value = 70 as minimum level of quality of service for each call, equivalent to 3.6 of Mean Opinion Score (MOS. In our experiments, either G.723.1 5.3 kbps or G.729 is chosen for maximizing the number of VoIP calls, it depends on link utilization and level of packet loss.
A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

Directory of Open Access Journals (Sweden)

Ke-yan Liu

2017-05-01

Full Text Available This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO. Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA. In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.
The effect of aging on network structure

OpenAIRE

Zhu, Han; Wang, Xin-Ran; Zhu, Jian-Yang

2003-01-01

In network evolution, the effect of aging is universal: in scientific collaboration network, scientists have a finite time span of being active; in movie actors network, once popular stars are retiring from stage; devices on the Internet may become outmoded with techniques developing so rapidly. Here we find in citation networks that this effect can be represented by an exponential decay factor, $e^{-\\beta \\tau}$, where $\\tau $ is the node age, while other evolving networks (the Internet for ...
vhv supply networks, problems of network structure

Energy Technology Data Exchange (ETDEWEB)

Raimbault, J

1966-04-01

The present and future power requirements of the Paris area and the structure of the existing networks are discussed. The various limitations that will have to be allowed for to lay down the structure of a regional transmission network leading in the power of the large national transmission network to within the Paris built up area are described. The theoretical solution that has been adopted, and the features of its final achievement, which is planned for about the year 2000, and the intermediate stages are given. The problem of the structure of the National Power Transmission network which is to supply the regional network was studied. To solve this problem, a 730 kV voltage network will have to be introduced.
Innovation in Multiple Networks and Networks of Networks: The Case of the Fruit Sector in Emilia‐Romagna

Directory of Open Access Journals (Sweden)

Davide Viaggi

2013-02-01

Full Text Available In the paper we examine the issue of food systems in which farms participate in multiple networks that, for their part, tend also to be members of networks of networks. The issue is addressed through a descriptive analysis of the fruit sector in Emilia‐Romagna (Italy. The farms in the area tend to join a different network for each product/product type. Innovation networks are embedded in commercialization or input provider networks, but separate (parallel networks also exist, particularly for basic research activities. Networks of networks are largely a product of the cooperative system. The paper concludes by emphasising the need for further research in multiple networking strategies and the connection betweencommercialisation networks and innovation.
Synchronization criterion for Lur'e type complex dynamical networks with time-varying delay

International Nuclear Information System (INIS)

Ji, D.H.; Park, Ju H.; Yoo, W.J.; Won, S.C.; Lee, S.M.

2010-01-01

In this Letter, the synchronization problem for a class of complex dynamical networks in which every identical node is a Lur'e system with time-varying delay is considered. A delay-dependent synchronization criterion is derived for the synchronization of complex dynamical network that represented by Lur'e system with sector restricted nonlinearities. The derived criterion is a sufficient condition for absolute stability of error dynamics between the each nodes and the isolated node. Using a convex representation of the nonlinearity for error dynamics, the stability condition based on the discretized Lyapunov-Krasovskii functional is obtained via LMI formulation. The proposed delay-dependent synchronization criterion is less conservative than the existing ones. The effectiveness of our work is verified through numerical examples.
Knowledge Strategies in Using Social Networks

Directory of Open Access Journals (Sweden)

Contantin BRĂTIANU

2013-05-01

Full Text Available Knowledge strategy selection is a multiple criteria decision-making (MCDM problem, and requires adequate methods to solve it appropriately. Knowledge strategies are also intrinsically linked to individuals and their ability to comprehend the world and leverage their intellectual assets to respond e!ectively to a fast changing environment. the essential features of social networking sites include but are not limited to: blogging, grouping, networking and instant messaging. Since the social networks facilitate communication and interaction among users, there is a continuous need of researches to examine what are the motives that a!ect the acceptance of usage of the social networks. This study aims at examining the role of the knowledge strategies that individuals employ in using social networks with respect to the overall objective of increasing the knowledge level. For this purpose we have used the Analytic Hierarchy Process (AHP mathematical model since it allows us a structuring of the overall objective on the main components. For the present research we considered a structure composed of three levels: L1 – the purpose of networking, L2 – strategies used to achieve that purpose, and L3 – activities needed for strategies implementation. At the upper level (L1, the main objective of a person in using social networks is to increase its knowledge level. To obtain the aforementioned objective we considered for the second level (L2 the following strategies: S1 – to learn from other persons; S2 – to make new friends; S3 – to increase the personal experience and visibility. the implementation of these strategies is realized through the following activities considered at the third hierarchy level (L3: A1– joining general social networks (e.g. Facebook, Google+, MySpace, Hi5 etc.; A2– joining professional social networks (e.g. LinkedIn etc.; A3– creating a personal blog (e.g. Blogster, Wordpress etc.; A4– joining online communities of
Translation-aware semantic segmentation via conditional least-square generative adversarial networks

Science.gov (United States)

Zhang, Mi; Hu, Xiangyun; Zhao, Like; Pang, Shiyan; Gong, Jinqi; Luo, Min

2017-10-01

Semantic segmentation has recently made rapid progress in the field of remote sensing and computer vision. However, many leading approaches cannot simultaneously translate label maps to possible source images with a limited number of training images. The core issue is insufficient adversarial information to interpret the inverse process and proper objective loss function to overcome the vanishing gradient problem. We propose the use of conditional least squares generative adversarial networks (CLS-GAN) to delineate visual objects and solve these problems. We trained the CLS-GAN network for semantic segmentation to discriminate dense prediction information either from training images or generative networks. We show that the optimal objective function of CLS-GAN is a special class of f-divergence and yields a generator that lies on the decision boundary of discriminator that reduces possible vanished gradient. We also demonstrate the effectiveness of the proposed architecture at translating images from label maps in the learning process. Experiments on a limited number of high resolution images, including close-range and remote sensing datasets, indicate that the proposed method leads to the improved semantic segmentation accuracy and can simultaneously generate high quality images from label maps.
Network cohesion

OpenAIRE

Cavalcanti, Tiago Vanderlei; Giannitsarou, Chrysi; Johnson, CR

2017-01-01

We define a measure of network cohesion and show how it arises naturally in a broad class of dynamic models of endogenous perpetual growth with network externalities. Via a standard growth model, we show why network cohesion is crucial for conditional convergence and explain that as cohesion increases, convergence is faster. We prove properties of network cohesion and define a network aggregator that preserves network cohesion.
Spatial networks

Science.gov (United States)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.
Probing many-body localization with neural networks

Science.gov (United States)

Schindler, Frank; Regnault, Nicolas; Neupert, Titus

2017-06-01

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.
Network exploitation using WAMI tracks

Science.gov (United States)

Rimey, Ray; Record, Jim; Keefe, Dan; Kennedy, Levi; Cramer, Chris

2011-06-01

Creating and exploiting network models from wide area motion imagery (WAMI) is an important task for intelligence analysis. Tracks of entities observed moving in the WAMI sensor data are extracted, then large numbers of tracks are studied over long time intervals to determine specific locations that are visited (e.g., buildings in an urban environment), what locations are related to other locations, and the function of each location. This paper describes several parts of the network detection/exploitation problem, and summarizes a solution technique for each: (a) Detecting nodes; (b) Detecting links between known nodes; (c) Node attributes to characterize a node; (d) Link attributes to characterize each link; (e) Link structure inferred from node attributes and vice versa; and (f) Decomposing a detected network into smaller networks. Experimental results are presented for each solution technique, and those are used to discuss issues for each problem part and its solution technique.
Self-organized topology of recurrence-based complex networks

International Nuclear Information System (INIS)

Yang, Hui; Liu, Gang

2013-01-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks
Large scale network management. Condition indicators for network stations, high voltage power conductions and cables

International Nuclear Information System (INIS)

Eggen, Arnt Ove; Rolfseng, Lars; Langdal, Bjoern Inge

2006-02-01

In the Strategic Institute Programme (SIP) 'Electricity Business enters e-business (eBee)' SINTEF Energy research has developed competency that can help the energy business employ ICT systems and computer technology in an improved way. Large scale network management is now a reality, and it is characterized by large entities with increasing demands on efficiency and quality. These are goals that can only be reached by using ICT systems and computer technology in a more clever way than what is the case today. At the same time it is important that knowledge held by experienced co-workers is consulted when formal rules for evaluations and decisions in ICT systems are developed. In this project an analytical concept for evaluation of networks based information in different ICT systems has been developed. The method estimating the indicators to describe different conditions in a network is general, and indicators can be made to fit different levels of decision and network levels, for example network station, transformer circuit, distribution network and regional network. Moreover, the indicators can contain information about technical aspects, economy and HSE. An indicator consists of an indicator name, an indicator value, and an indicator colour based on a traffic-light analogy to indicate a condition or a quality for the indicator. Values on one or more indicators give an impression of important conditions in the network, and make up the basis for knowing where more detailed evaluations have to be conducted before a final decision on for example maintenance or renewal is made. A prototype has been developed for testing the new method. The prototype has been developed in Excel, and especially designed for analysing transformer circuits in a distribution network. However, the method is a general one, and well suited for implementation in a commercial computer system (ml)
Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

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Zhi He

2017-10-01

Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.
Link Label Prediction in Signed Citation Network

KAUST Repository

Akujuobi, Uchenna Thankgod

2016-01-01

such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other
Designing Networks that are Capable of Self-Healing and Adapting

Science.gov (United States)

2017-04-01

battlefield, and utility infrastructure in cities . Networks that are easy to repair after many breaks. The origi- nal networks (left), the networks after...8725 John J. Kingman Road, MS 6201 Fort Belvoir, VA 22060-6201 T E C H N IC A L R E P O R T DTRA-TR-15-78 Designing Networks that are...from statistical mechanics, combinatorics, boolean networks , and numerical simulations, and inspired by design principles from biological networks , we
DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.
RenderGAN: Generating Realistic Labeled Data

Directory of Open Access Journals (Sweden)

Leon Sixt

2018-06-01

Full Text Available Deep Convolutional Neuronal Networks (DCNNs are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.
Network Ambivalence

Directory of Open Access Journals (Sweden)

Patrick Jagoda

2015-08-01

Full Text Available The language of networks now describes everything from the Internet to the economy to terrorist organizations. In distinction to a common view of networks as a universal, originary, or necessary form that promises to explain everything from neural structures to online traffic, this essay emphasizes the contingency of the network imaginary. Network form, in its role as our current cultural dominant, makes scarcely imaginable the possibility of an alternative or an outside uninflected by networks. If so many things and relationships are figured as networks, however, then what is not a network? If a network points towards particular logics and qualities of relation in our historical present, what others might we envision in the future? In many ways, these questions are unanswerable from within the contemporary moment. Instead of seeking an avant-garde approach (to move beyond networks or opting out of networks (in some cases, to recover elements of pre-networked existence, this essay proposes a third orientation: one of ambivalence that operates as a mode of extreme presence. I propose the concept of "network aesthetics," which can be tracked across artistic media and cultural forms, as a model, style, and pedagogy for approaching interconnection in the twenty-first century. The following essay is excerpted from Network Ambivalence (Forthcoming from University of Chicago Press.

NOSArmor: Building a Secure Network Operating System

Directory of Open Access Journals (Sweden)

Hyeonseong Jo

2018-01-01

Full Text Available Software-Defined Networking (SDN, controlling underlying network devices (i.e., data plane in a logically centralized manner, is now actively adopted in many real world networking environments. It is clear that a network administrator can easily understand and manage his networking environments with the help of SDN. In SDN, a network operating system (NOS, also known as an SDN controller, is the most critical component because it should be involved in all transactions for controlling network devices, and thus the security of NOS cannot be highly exaggerated. However, in spite of its importance, no previous works have thoroughly investigated the security of NOS. In this work, to address this problem, we present the NOSArmor, which integrates several security mechanisms, named as security building block (SBB, into a consolidated SDN controller. NOSArmor consists of eight SBBs and each of them addresses different security principles of network assets. For example, while role-based authorization focuses on securing confidentiality of internal storage from malicious applications, OpenFlow protocol verifier protects availability of core service in the controller from malformed control messages received from switches. In addition, NOSArmor shows competitive performance compared to existing other controllers (i.e., ONOS, Floodlight with secureness of network assets.
Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartite networks.

Science.gov (United States)

Astegiano, Julia; Altermatt, Florian; Massol, François

2017-11-13

Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.
Inferring general relations between network characteristics from specific network ensembles.

Science.gov (United States)

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.
Advances and Challenges in Convergent Communication Networks

DEFF Research Database (Denmark)

Toral-Cruz, Homero; Mihovska, Albena

2017-01-01

Welcome to this special issue of Wireless Personal Communications on Advances and Challenges in Convergent Communication Networks. The main purpose of this special issue is to present new progresses and challenges in convergent networks. Communication networks play an important role in our daily...... life because they allow communicating and sharing contents between heterogeneous nodes around the globe. The emergence of multiple network architectures and emerging technologies have resulted in new applications and services over a heterogeneous network. This heterogeneous network has undergone...... significant challenges in recent years, such as the evolution to a converged network with the capability to support multiple services, while maintaining a satisfactory level of QoE/QoS, security, efficiency and trust. The special issue on Advances and Challenges in Convergent Communication Networks...
Network-topology-adaptive quantum conference protocols

International Nuclear Information System (INIS)

Zhang Sheng; Wang Jian; Tang Chao-Jing; Zhang Quan

2011-01-01

As an important application of the quantum network communication, quantum multiparty conference has made multiparty secret communication possible. Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology. However, the topology of the quantum network significantly affects the communication efficiency, e.g., parallel transmission in a channel with limited bandwidth. We have proposed two distinctive protocols, which work in two basic network topologies with efficiency higher than the existing ones. We first present a protocol which works in the reticulate network using Greeberger—Horne—Zeilinger states and entanglement swapping. Another protocol, based on quantum multicasting with quantum data compression, which can improve the efficiency of the network, works in the star-like network. The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption. In general, the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols. (general)
Speech networks at rest and in action: interactions between functional brain networks controlling speech production

Science.gov (United States)

Fuertinger, Stefan

2015-01-01

Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742
Tuning chaos in network sharing common nonlinearity

Science.gov (United States)

Paul Asir, M.; Jeevarekha, A.; Philominathan, P.

2016-06-01

In this paper, a novel type of network called network sharing common nonlinearity comprising both autonomous and non-autonomous oscillators have been investigated. We propose that these networks are robust for operating at desired modes i.e., chaotic or periodic by altering the v-i characteristics of common nonlinear element alone. The dynamics of these networks were examined through numerical, analytical, experimental and Multisim simulations.
Network class superposition analyses.

Directory of Open Access Journals (Sweden)

Carl A B Pearson

Full Text Available Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., ≈ 10(30 for the yeast cell cycle process, considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix T, which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for T derived from boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying T to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with T. We show how to generate Derrida plots based on T. We show that T-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on T. We motivate all of these results in terms of a popular molecular biology boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for T, for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.
Training strategy for convolutional neural networks in pedestrian gender classification

Science.gov (United States)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.
Maximum entropy networks are more controllable than preferential attachment networks

International Nuclear Information System (INIS)

Hou, Lvlin; Small, Michael; Lao, Songyang

2014-01-01

A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution. - Highlights: • The controllability of maximum entropy (ME) and Barabási–Albert (BA) networks is investigated. • ME networks are significantly more easily controlled than BA networks of the same degree distribution. • The properties of the neighbors of driver node sensitively affect the network controllability. • Subtle and important structural differences exist between BA networks and typical scale-free networks
Bipartite quantum states and random complex networks

International Nuclear Information System (INIS)

Garnerone, Silvano; Zanardi, Paolo; Giorda, Paolo

2012-01-01

We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of random graphs known as complex networks. In the case of classical random graphs, we derive an analytic expression for the averaged entanglement entropy S-bar while for general complex networks we rely on numerics. For a large number of nodes n we find a scaling S-bar ∼c log n +g e where both the prefactor c and the sub-leading O(1) term g e are characteristic of the different classes of complex networks. In particular, g e encodes topological features of the graphs and is named network topological entropy. Our results suggest that quantum entanglement may provide a powerful tool for the analysis of large complex networks with non-trivial topological properties. (paper)
Enabling Wireless Cooperation in User Provided Networks

OpenAIRE

Rolla, Vitor Guerra

2015-01-01

Tese de doutoramento em Ciências e Tecnologias da Informação, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de Coimbra This doctoral thesis investigates user provided networks. Such networks have become important research subjects in the field of informatics engineering due to the recent popularity of smart phones. User provided networks are independent from traditional Internet service providers. Communication and informati...
Optimization of temporal networks under uncertainty

CERN Document Server

Wiesemann, Wolfram

2012-01-01

Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization probl
Network Learning and Innovation in SME Formal Networks

Directory of Open Access Journals (Sweden)

Jivka Deiters

2013-02-01

Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.
Interconnected networks

CERN Document Server

2016-01-01

This volume provides an introduction to and overview of the emerging field of interconnected networks which include multi layer or multiplex networks, as well as networks of networks. Such networks present structural and dynamical features quite different from those observed in isolated networks. The presence of links between different networks or layers of a network typically alters the way such interconnected networks behave – understanding the role of interconnecting links is therefore a crucial step towards a more accurate description of real-world systems. While examples of such dissimilar properties are becoming more abundant – for example regarding diffusion, robustness and competition – the root of such differences remains to be elucidated. Each chapter in this topical collection is self-contained and can be read on its own, thus making it also suitable as reference for experienced researchers wishing to focus on a particular topic.
Cooperative and heuristic learning in the international network e-Culturas

Directory of Open Access Journals (Sweden)

Antonio PANTOJA VALLEJO

2011-12-01

Full Text Available 0 0 1 89 495 Instituto Universitario de Ciencias de la Educación 4 1 583 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} Virtual learning environments, converted to digital platforms, encourage collaboration and interaction between users, while allowing the creative capacity will be increased considerably. On this basis the Intercultural Program e-Culturas is based, being part of the International e-Culturas Network (http://www.e-culturas.org. This is a collaborative networking project that aims to twin children of different nationalities to work a series of cross-cultural materials. In the present paper is explained the basic methodology underlying it.
Generating Realistic Labelled, Weighted Random Graphs

Directory of Open Access Journals (Sweden)

Michael Charles Davis

2015-12-01

Full Text Available Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs. Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.
Do Policy Networks lead to Network Governing?

DEFF Research Database (Denmark)

Damgaard, Bodil

This paper challenges the notion that creation of local policy networks necessarily leads to network governing. Through actor-centred case studies in the area of municipally implemented employment policy in Denmark it was found that the local governing mode is determined mainly by the municipality......’s approach to local co-governing as well as by the capacity and interest of key private actors. It is argued that national legislation requesting the creation of local policy networks was not enough to assure network governing and the case studies show that local policy networks may subsist also under...... hierarchical governing modes. Reasons why hierarchical governing modes prevail over network governing in some settings are identified pointing to both actor borne and structural factors. Output indicators of the four cases do not show that a particular governing mode is more efficient in its employment policy...
Incidence of irradiated foods in the distribution network of Prague

International Nuclear Information System (INIS)

Bohačenko, I.; Kopicova, Z.; Zamecnikova, I.

2005-01-01

The samples, 29 in total, of poultry, rabbit meat, cheese and exotic fruits were taken from the distribution network of Prague. None of the samples was declared as irradiated according to the Decree of the Ministry of Health, CR, No. 133/2004 Sb. The check of their possible exposure to irradiation was made by means of two methods, i.e. the procedure according to EN 1784 (determination of hydrocarbons generated by irradiation using gas chromatography) and the determination of non-bonded o-tyrosine by means of HPLC with electrochemical detection. Neither method brought evidence for the exposure to irradiation, i.e. the purchased foodstuffs concerned were not labelled deceitfully. (author)
Application of Butterfly Clos-Network in Network-on-Chip

Directory of Open Access Journals (Sweden)

Hui Liu

2014-01-01

Full Text Available This paper studied the topology of NoC (Network-on-Chip. By combining the characteristics of the Clos network and butterfly network, a new topology named BFC (Butterfly Clos-network network was proposed. This topology integrates several modules, which belongs to the same layer but different dimensions, into a new module. In the BFC network, a bidirectional link is used to complete information exchange, instead of information exchange between different layers in the original network. During the routing period, other nondestination nodes can be used as middle stages to transfer data packets to complete the routing mission. Therefore, this topology has the characteristic of multistage. Simulation analyses show that BFC inherits the rich path diversity of Clos network, and it has a better performance than butterfly network in throughput and delay in a quite congested traffic pattern.

Implications of the Dynamics of the New Networked Economy for E-Business Start-Ups: The Case of Philips' Access Point.

Science.gov (United States)

Tovstiga, George; Fantner, Ernest J.

2000-01-01

Examines implications of the networked economy for e-commerce business start-ups. Revisits the notion of "value" and "value creation" in a network context. Examines "value" relative to technological innovation. Looks at implications of the network environment for the organization and transformation of the enterprise's…
E-dating, identity and HIV prevention: theorising sexualities, risk and network society.

Science.gov (United States)

Davis, Mark; Hart, Graham; Bolding, Graham; Sherr, Lorraine; Elford, Jonathan

2006-05-01

This paper addresses how London gay men use the internet to meet sexual partners, or for e-dating. Based on qualitative interviews conducted face-to-face or via the internet, this research develops an account of how information technologies mediate the negotiation of identity and risk in connection with sexual practice. E-dating itself is a bricolage, or heterogeneous DIY practice of internet-based-communication (IBC). A central aspect of IBC is "filtering" in and out prospective e-dates based on the images and texts used to depict sexual identities. Interpretations and depictions of personal HIV risk management approaches in IBC are framed by the meanings of different identities, such as the stigma associated with being HIV positive. This paper argues for a sexualities perspective in a theory of network society. Further, HIV prevention in e-dating can potentially be addressed by considering the interplay of the HIV prevention imperatives associated with different HIV serostatus identities. There is a case for encouraging more explicit IBC about risk in e-dating and incorporating the expertise of e-daters in prevention activity. There is also a need to rethink traditional conceptions of risk management in HIV prevention to make space for the risk management bricolage of network society.
Enterasys Networks delivers 10-Gigabit ethernet for the enterprise with new matrix E1 switching family

CERN Multimedia

2001-01-01

Enterasys Networks Inc., today announced its new Matrix E1 family of 10-Gigabit and Gigabit Ethernet switches. The Matrix E1 Optical Access Switch (OAS) enables organizations to deliver applications at 10-Gb speeds across a single fibre optic pair. Jacques Altaber, deputy leader of IT at CERN said "High-bandwith solutions are essential to leveraging more computing power, so 10-Gb Ethernet is the next logical step for us...The Matrix E1 allows us to provide the networking support that our scientists need and gives us a certain future for bandwidth and computing expansion".
Artificial Neural Network Analysis System

Science.gov (United States)

2001-02-27

Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis
Airline network structure in competitive market

Directory of Open Access Journals (Sweden)

Babić Danica D.

2014-01-01

Full Text Available Airline's network is the key element of its business strategy and selected network structure will not have influence only on the airline's costs but could gain some advantage in revenues, too. Network designing implies that an airline has to make decisions about markets that it will serve and how to serve those markets. Network choice raises the following questions for an airline: a what markets to serve, b how to serve selected markets, c what level of service to offer, d what are the benefits/cost of the that decisions and e what is the influence of the competition. We analyzed the existing airline business models and corresponding network structure. The paper highlights the relationship between the network structures and the airline business strategies. Using a simple model we examine the relationship between the network structure and service quality in deregulated market.
Energy optimization in mobile sensor networks

Science.gov (United States)

Yu, Shengwei

Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while
A User Driven Dynamic Circuit Network Implementation

Energy Technology Data Exchange (ETDEWEB)

Guok, Chin; Robertson, David; Chaniotakis, Evangelos; Thompson, Mary; Johnston, William; Tierney, Brian

2008-10-01

The requirements for network predictability are becoming increasingly critical to the DoE science community where resources are widely distributed and collaborations are world-wide. To accommodate these emerging requirements, the Energy Sciences Network has established a Science Data Network to provide user driven guaranteed bandwidth allocations. In this paper we outline the design, implementation, and secure coordinated use of such a network, as well as some lessons learned.
Polarity related influence maximization in signed social networks.

Directory of Open Access Journals (Sweden)

Dong Li

Full Text Available Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.
KeyPathwayMiner - De-novo network enrichment by combining multiple OMICS data and biological networks

DEFF Research Database (Denmark)

Baumbach, Jan; Alcaraz, Nicolas; Pauling, Josch K.

We tackle the problem of de-novo pathway extraction. Given a biological network and a set of case-control studies, KeyPathwayMiner efficiently extracts and visualizes all maximal connected sub-networks that contain mainly genes that are dysregulated, e.g., differentially expressed, in most cases ...
Cascade of links in complex networks

Energy Technology Data Exchange (ETDEWEB)

Feng, Yeqian; Sun, Bihui [Department of Management Science, School of Government, Beijing Normal University, 100875 Beijing (China); Zeng, An, E-mail: anzeng@bnu.edu.cn [School of Systems Science, Beijing Normal University, 100875 Beijing (China)

2017-01-30

Cascading failure is an important process which has been widely used to model catastrophic events such as blackouts and financial crisis in real systems. However, so far most of the studies in the literature focus on the cascading process on nodes, leaving the possibility of link cascade overlooked. In many real cases, the catastrophic events are actually formed by the successive disappearance of links. Examples exist in the financial systems where the firms and banks (i.e. nodes) still exist but many financial trades (i.e. links) are gone during the crisis, and the air transportation systems where the airports (i.e. nodes) are still functional but many airlines (i.e. links) stop operating during bad weather. In this letter, we develop a link cascade model in complex networks. With this model, we find that both artificial and real networks tend to collapse even if a few links are initially attacked. However, the link cascading process can be effectively terminated by setting a few strong nodes in the network which do not respond to any link reduction. Finally, a simulated annealing algorithm is used to optimize the location of these strong nodes, which significantly improves the robustness of the networks against the link cascade. - Highlights: • We propose a link cascade model in complex networks. • Both artificial and real networks tend to collapse even if a few links are initially attacked. • The link cascading process can be effectively terminated by setting a few strong nodes. • A simulated annealing algorithm is used to optimize the location of these strong nodes.
Cascade of links in complex networks

International Nuclear Information System (INIS)

Feng, Yeqian; Sun, Bihui; Zeng, An

2017-01-01

Cascading failure is an important process which has been widely used to model catastrophic events such as blackouts and financial crisis in real systems. However, so far most of the studies in the literature focus on the cascading process on nodes, leaving the possibility of link cascade overlooked. In many real cases, the catastrophic events are actually formed by the successive disappearance of links. Examples exist in the financial systems where the firms and banks (i.e. nodes) still exist but many financial trades (i.e. links) are gone during the crisis, and the air transportation systems where the airports (i.e. nodes) are still functional but many airlines (i.e. links) stop operating during bad weather. In this letter, we develop a link cascade model in complex networks. With this model, we find that both artificial and real networks tend to collapse even if a few links are initially attacked. However, the link cascading process can be effectively terminated by setting a few strong nodes in the network which do not respond to any link reduction. Finally, a simulated annealing algorithm is used to optimize the location of these strong nodes, which significantly improves the robustness of the networks against the link cascade. - Highlights: • We propose a link cascade model in complex networks. • Both artificial and real networks tend to collapse even if a few links are initially attacked. • The link cascading process can be effectively terminated by setting a few strong nodes. • A simulated annealing algorithm is used to optimize the location of these strong nodes.
Mitigating Inter-Network Interference in LoRa Networks

OpenAIRE

Voigt, Thiemo; Bor, Martin; Roedig, Utz; Alonso, Juan

2017-01-01

Long Range (LoRa) is a popular technology used to construct Low-Power Wide-Area Network (LPWAN) networks. Given the popularity of LoRa it is likely that multiple independent LoRa networks are deployed in close proximity. In this situation, neighbouring networks interfere and methods have to be found to combat this interference. In this paper we investigate the use of directional antennae and the use of multiple base stations as methods of dealing with inter-network interference. Directional a...
Maximal network reliability for a stochastic power transmission network

International Nuclear Information System (INIS)

Lin, Yi-Kuei; Yeh, Cheng-Ta

2011-01-01

Many studies regarded a power transmission network as a binary-state network and constructed it with several arcs and vertices to evaluate network reliability. In practice, the power transmission network should be stochastic because each arc (transmission line) combined with several physical lines is multistate. Network reliability is the probability that the network can transmit d units of electric power from a power plant (source) to a high voltage substation at a specific area (sink). This study focuses on searching for the optimal transmission line assignment to the power transmission network such that network reliability is maximized. A genetic algorithm based method integrating the minimal paths and the Recursive Sum of Disjoint Products is developed to solve this assignment problem. A real power transmission network is adopted to demonstrate the computational efficiency of the proposed method while comparing with the random solution generation approach.
Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

Science.gov (United States)

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence
Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Directory of Open Access Journals (Sweden)

Chang Jeong-Ho

2006-06-01

Full Text Available Abstract Background To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. Results To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. Conclusion By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway
Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

Science.gov (United States)

Rußwurm, M.; Körner, M.

2017-05-01

Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.
MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

M. Rußwurm

2017-05-01

Full Text Available Land cover classification (LCC is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN, with a classical non-temporal convolutional neural network (CNN model and an additional support vector machine (SVM baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.
Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

Science.gov (United States)

Simonyan, Kristina; Fuertinger, Stefan

2015-04-01

Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.
Assembly and offset assignment scheme for self-similar traffic in optical burst switched networks

CSIR Research Space (South Africa)

Muwonge, KB

2007-10-01

Full Text Available at the Label Edge Router (LER) to buffer traffic in the electronic domain. Burst assembly and offset assignment schemes are implemented in a complementary manner to improve QoS of an OBS network. The authors show that OBS network performance is directly related...
Routing architecture and security for airborne networks

Science.gov (United States)

Deng, Hongmei; Xie, Peng; Li, Jason; Xu, Roger; Levy, Renato

2009-05-01

Airborne networks are envisioned to provide interconnectivity for terrestial and space networks by interconnecting highly mobile airborne platforms. A number of military applications are expected to be used by the operator, and all these applications require proper routing security support to establish correct route between communicating platforms in a timely manner. As airborne networks somewhat different from traditional wired and wireless networks (e.g., Internet, LAN, WLAN, MANET, etc), security aspects valid in these networks are not fully applicable to airborne networks. Designing an efficient security scheme to protect airborne networks is confronted with new requirements. In this paper, we first identify a candidate routing architecture, which works as an underlying structure for our proposed security scheme. And then we investigate the vulnerabilities and attack models against routing protocols in airborne networks. Based on these studies, we propose an integrated security solution to address routing security issues in airborne networks.

Network workshop

DEFF Research Database (Denmark)

Bruun, Jesper; Evans, Robert Harry

2014-01-01

This paper describes the background for, realisation of and author reflections on a network workshop held at ESERA2013. As a new research area in science education, networks offer a unique opportunity to visualise and find patterns and relationships in complicated social or academic network data....... These include student relations and interactions and epistemic and linguistic networks of words, concepts and actions. Network methodology has already found use in science education research. However, while networks hold the potential for new insights, they have not yet found wide use in the science education...... research community. With this workshop, participants were offered a way into network science based on authentic educational research data. The workshop was constructed as an inquiry lesson with emphasis on user autonomy. Learning activities had participants choose to work with one of two cases of networks...
On the Design of Energy Efficient Optical Networks with Software Defined Networking Control Across Core and Access Networks

DEFF Research Database (Denmark)

Wang, Jiayuan; Yan, Ying; Dittmann, Lars

2013-01-01

This paper presents a Software Defined Networking (SDN) control plane based on an overlay GMPLS control model. The SDN control platform manages optical core networks (WDM/DWDM networks) and the associated access networks (GPON networks), which makes it possible to gather global information...... and enable wider areas' energy efficiency networking. The energy related information of the networks and the types of the traffic flows are collected and utilized for the end-to-end QoS provision. Dynamic network simulation results show that by applying different routing algorithms according to the type...... of traffic in the core networks, the energy efficiency of the network is improved without compromising the quality of service....
Scaling in public transport networks

Directory of Open Access Journals (Sweden)

C. von Ferber

2005-01-01

Full Text Available We analyse the statistical properties of public transport networks. These networks are defined by a set of public transport routes (bus lines and the stations serviced by these. For larger networks these appear to possess a scale-free structure, as it is demonstrated e.g. by the Zipf law distribution of the number of routes servicing a given station or for the distribution of the number of stations which can be visited from a chosen one without changing the means of transport. Moreover, a rather particular feature of the public transport network is that many routes service common subsets of stations. We discuss the possibility of new scaling laws that govern intrinsic properties of such subsets.
Exploring biological network structure with clustered random networks

Directory of Open Access Journals (Sweden)

Bansal Shweta

2009-12-01

Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in
Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Science.gov (United States)

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Directory of Open Access Journals (Sweden)

Guihua Wen

2017-01-01

Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
Biological transportation networks: Modeling and simulation

KAUST Repository

Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

2015-01-01

We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation
Energy-aware virtual network embedding in flexi-grid networks.

Science.gov (United States)

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng

2017-11-27

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.
Resilient Disaster Network Based on Software Defined Cognitive Wireless Network Technology

Directory of Open Access Journals (Sweden)

Goshi Sato

2015-01-01

Full Text Available In order to temporally recover the information network infrastructure in disaster areas from the Great East Japan Earthquake in 2011, various wireless network technologies such as satellite IP network, 3G, and Wi-Fi were effectively used. However, since those wireless networks are individually introduced and installed but not totally integrated, some of networks were congested due to the sudden network traffic generation and unbalanced traffic distribution, and eventually the total network could not effectively function. In this paper, we propose a disaster resilient network which integrates various wireless networks into a cognitive wireless network that users can use as an access network to the Internet at the serious disaster occurrence. We designed and developed the disaster resilient network based on software defined network (SDN technology to automatically select the best network link and route among the possible access networks to the Internet by periodically monitoring their network states and evaluate those using extended AHP method. In order to verify the usefulness of our proposed system, a prototype system is constructed and its performance is evaluated.
Deploying temporary networks for upscaling of sparse network stations

Science.gov (United States)

Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

2016-10-01

Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.
NETWORK, HOW? Perceptions of business people on networking practices

Directory of Open Access Journals (Sweden)

Saskia De Klerk

2010-12-01

Full Text Available Purpose: The purpose of this paper was to contribute to literature on networking from a South African perspective. Literature on networking is mainly concentrated on the European and American contexts with homogeneous groups and traditional divisions. The business landscape of South Africa thus requires more investigation. Problem investigated: Literature regarding networking in an South African context with its dynamic business environment is limited. This article addresses the concerns of how South African business owners and managers perceive networking in their businesses and specifically focus on the South African perspective. Therefore, the focus is on the perceptions of business owners and managers on current networking practices in South Africa. Methodology: A qualitative research design to uncover the rich underlying feelings of business owners and managers was used. The qualitative enquiry consisted of five focus group discussions (n=41 participants among prominent business owners and managers in the Gauteng Province, South Africa. The Gauteng Province was selected since it is the economic and innovation hub of South Africa. Findings and implications: The main findings showed the following main themes of networking that emerged from the data, and included (1 networking as a skill versus a natural ability; (2 the motivation behind networking; (3 the loci of networking; (4 the type of relationships that determine the character of the network; and (5 the relationship characteristics of successful networking. The main contribution of this is that there seems to be different networking situations and applications for different circumstances. According to the participants, it seems that networking in the South African landscape appears to be either relationship or business based. Originality and value of the research: The value of these findings lies in the fact that they contribute to networking literature from a South African perspective
Mutational robustness of gene regulatory networks.

Directory of Open Access Journals (Sweden)

Aalt D J van Dijk

Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.
Convergence of Networks

DEFF Research Database (Denmark)

Prasad, Ramjee; Ruggieri, Marina

2008-01-01

The paper focuses on the revolutionary changes that could characterise the future of networks. Those changes involve many aspects in the conceivement and exploitation of networks: architecture, services, technologies and modeling. The convergence of wired and wireless technologies along...... with the integration of system componennts and the convergence of services (e.g. communications and navigation) are only some of the elements that shape the perpsected mosaic. Authors delineate this vision, highlighting the presence of the space and stratospheric components and the related services as building block...
Networks in Social Policy Problems

Science.gov (United States)

Vedres, Balázs; Scotti, Marco

2012-08-01

1. Introduction M. Scotti and B. Vedres; Part I. Information, Collaboration, Innovation: The Creative Power of Networks: 2. Dissemination of health information within social networks C. Dhanjal, S. Blanchemanche, S. Clemençon, A. Rona-Tas and F. Rossi; 3. Scientific teams and networks change the face of knowledge creation S. Wuchty, J. Spiro, B. F. Jones and B. Uzzi; 4. Structural folds: the innovative potential of overlapping groups B. Vedres and D. Stark; 5. Team formation and performance on nanoHub: a network selection challenge in scientific communities D. Margolin, K. Ognyanova, M. Huang, Y. Huang and N. Contractor; Part II. Influence, Capture, Corruption: Networks Perspectives on Policy Institutions: 6. Modes of coordination of collective action: what actors in policy making? M. Diani; 7. Why skewed distributions of pay for executives is the cause of much grief: puzzles and few answers so far B. Kogut and J.-S. Yang; 8. Networks of institutional capture: a case of business in the State apparatus E. Lazega and L. Mounier; 9. The social and institutional structure of corruption: some typical network configurations of corruption transactions in Hungary Z. Szántó, I. J. Tóth and S. Varga; Part III. Crisis, Extinction, World System Change: Network Dynamics on a Large Scale: 10. How creative elements help the recovery of networks after crisis: lessons from biology A. Mihalik, A. S. Kaposi, I. A. Kovács, T. Nánási, R. Palotai, Á. Rák, M. S. Szalay-Beko and P. Csermely; 11. Networks and globalization policies D. R. White; 12. Network science in ecology: the structure of ecological communities and the biodiversity question A. Bodini, S. Allesina and C. Bondavalli; 13. Supply security in the European natural gas pipeline network M. Scotti and B. Vedres; 14. Conclusions and outlook A.-L. Barabási; Index.
How do German veterinarians use social networks? A study, using the example of the 'NOVICE' veterinary medicine network.

Science.gov (United States)

Schaper, Elisabeth; Forrest, Neil D; Tipold, Andrea; Ehlers, Jan P

2013-01-01

NOVICE (Network Of Veterinary ICT in Education, http://www.noviceproject.eu/), is a professional online social network for veterinarians, lecturers and students of veterinary medicine as well as for e-Learning advisers and others working in establishments that teach veterinary medicine. This study sets out to investigate to what extent German veterinarians, lecturers, students of veterinary medicine and e-Learning representatives would accept a specialist network, what requirements would have to be met by an online social network, how to use web 2.0 tools [21], [30] and what advantages a specialist network could offer. The investigation was carried out by analysing data from the Elgg platform database as well as using Google Analytics. Annual focus group surveys and individual interviews were carried out in order to perform an analysis of acceptance among network users. 1961 users from 73 different countries registered on the NOVICE site between 1 September 2010 and 21 March 2012. Germany represents the biggest user group, with 565 users (28.81%). During this period, most individual hits on the website came from Germany too. In total, 24.83% of all members are active, while 19.22% of German members participate actively. In terms of gender, there are significantly more female members than male members, both in the NOVICE network as a whole as well as in Germany. The most used web 2.0 tools are chat and email messaging services as well as writing wikis and contributing to forum discussions. The focus group surveys showed that respondents generally make use of other online communities too. Active members generally use more web 2.0 tools than in other networks, while passive members are generally more reluctant in all networks. All participants of the survey welcomed the idea of having a network specifically set up for the profession and believe that it could be very useful for veterinary medicine. The network and its membership figures developed very positively during
Medical image segmentation by a constraint satisfaction neural network

International Nuclear Information System (INIS)

Chen, C.T.; Tsao, E.C.K.; Lin, W.C.

1991-01-01

This paper proposes a class of Constraint Satisfaction Neural Networks (CSNNs) for solving the problem of medical image segmentation which can be formulated as a Constraint Satisfaction Problem (CSP). A CSNN consists of a set of objects, a set of labels for each object, a collection of constraint relations linking the labels of neighboring objects, and a topological constraint describing the neighborhood relationship among various objects. Each label for a particular object indicates one possible interpretation for that object. The CSNN can be viewed as a collection of neurons that interconnect with each other. The connections and the topology of a CSNN are used to represent the constraints in a CSP. The mechanism of the neural network is to find a solution that satisfies all the constraints in order to achieve a global consistency. The final solution outlines segmented areas and simultaneously satisfies all the constraints. This technique has been applied to medical images and the results show that this CSNN method is a very promising approach for image segmentation
Creative Network Communities in the Translocal Space of Digital Networks

Directory of Open Access Journals (Sweden)

Rasa Smite

2013-01-01

Full Text Available What should sociological research be in the age of Web 2.0? Considering that the task of “network sociology” is not only empirical research but also the interpretation of tendencies of the network culture, this research explores the rise of network communities within Eastern and Western Europe in the early Internet era. I coined the term creative networks to distinguish these early creative and social activities from today’s popular social networking. Thus I aimed to interpret the meaning of social action; the motivation of creative community actors, their main fields of activities and social organization forms; and the potential that these early developments contain for the future sustainability of networks. Data comprise interviews with networking experts and founders and members of various networks. Investigating respondents’ motivations for creating online networks and communities, and interpreting those terms, allows for comparing the creative networks of the 1990s with today’s social networks and for drawing conclusions.
A Network Thermodynamic Approach to Compartmental Analysis

Science.gov (United States)

Mikulecky, D. C.; Huf, E. G.; Thomas, S. R.

1979-01-01

We introduce a general network thermodynamic method for compartmental analysis which uses a compartmental model of sodium flows through frog skin as an illustrative example (Huf and Howell, 1974a). We use network thermodynamics (Mikulecky et al., 1977b) to formulate the problem, and a circuit simulation program (ASTEC 2, SPICE2, or PCAP) for computation. In this way, the compartment concentrations and net fluxes between compartments are readily obtained for a set of experimental conditions involving a square-wave pulse of labeled sodium at the outer surface of the skin. Qualitative features of the influx at the outer surface correlate very well with those observed for the short circuit current under another similar set of conditions by Morel and LeBlanc (1975). In related work, the compartmental model is used as a basis for simulation of the short circuit current and sodium flows simultaneously using a two-port network (Mikulecky et al., 1977a, and Mikulecky et al., A network thermodynamic model for short circuit current transients in frog skin. Manuscript in preparation; Gary-Bobo et al., 1978). The network approach lends itself to computation of classic compartmental problems in a simple manner using circuit simulation programs (Chua and Lin, 1975), and it further extends the compartmental models to more complicated situations involving coupled flows and non-linearities such as concentration dependencies, chemical reaction kinetics, etc. PMID:262387
Complex network analysis of state spaces for random Boolean networks

Energy Technology Data Exchange (ETDEWEB)

Shreim, Amer [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Berdahl, Andrew [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Sood, Vishal [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Grassberger, Peter [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada); Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4 (Canada)

2008-01-15

We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains N Boolean elements each with K inputs. A directed state space network (SSN) is constructed by linking each dynamical state, represented as a node, to its temporal successor. We study the heterogeneity of these SSNs at both local and global scales, as well as sample to-sample fluctuations within an ensemble of SSNs. We use in-degrees of nodes as a local topological measure, and the path diversity (Shreim A et al 2007 Phys. Rev. Lett. 98 198701) of an SSN as a global topological measure. RBNs with 2 {<=} K {<=} 5 exhibit non-trivial fluctuations at both local and global scales, while K = 2 exhibits the largest sample-to-sample (possibly non-self-averaging) fluctuations. We interpret the observed 'multi scale' fluctuations in the SSNs as indicative of the criticality and complexity of K = 2 RBNs. 'Garden of Eden' (GoE) states are nodes on an SSN that have in-degree zero. While in-degrees of non-GoE nodes for K > 1 SSNs can assume any integer value between 0 and 2{sup N}, for K = 1 all the non-GoE nodes in a given SSN have the same in-degree which is always a power of two.
Complex network analysis of state spaces for random Boolean networks

International Nuclear Information System (INIS)

Shreim, Amer; Berdahl, Andrew; Sood, Vishal; Grassberger, Peter; Paczuski, Maya

2008-01-01

We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains N Boolean elements each with K inputs. A directed state space network (SSN) is constructed by linking each dynamical state, represented as a node, to its temporal successor. We study the heterogeneity of these SSNs at both local and global scales, as well as sample to-sample fluctuations within an ensemble of SSNs. We use in-degrees of nodes as a local topological measure, and the path diversity (Shreim A et al 2007 Phys. Rev. Lett. 98 198701) of an SSN as a global topological measure. RBNs with 2 ≤ K ≤ 5 exhibit non-trivial fluctuations at both local and global scales, while K = 2 exhibits the largest sample-to-sample (possibly non-self-averaging) fluctuations. We interpret the observed 'multi scale' fluctuations in the SSNs as indicative of the criticality and complexity of K = 2 RBNs. 'Garden of Eden' (GoE) states are nodes on an SSN that have in-degree zero. While in-degrees of non-GoE nodes for K > 1 SSNs can assume any integer value between 0 and 2 N , for K = 1 all the non-GoE nodes in a given SSN have the same in-degree which is always a power of two

Comparison analysis on vulnerability of metro networks based on complex network

Science.gov (United States)

Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

2018-04-01

This paper analyzes the networked characteristics of three metro networks, and two malicious attacks are employed to investigate the vulnerability of metro networks based on connectivity vulnerability and functionality vulnerability. Meanwhile, the networked characteristics and vulnerability of three metro networks are compared with each other. The results show that Shanghai metro network has the largest transport capacity, Beijing metro network has the best local connectivity and Guangzhou metro network has the best global connectivity, moreover Beijing metro network has the best homogeneous degree distribution. Furthermore, we find that metro networks are very vulnerable subjected to malicious attacks, and Guangzhou metro network has the best topological structure and reliability among three metro networks. The results indicate that the proposed methodology is feasible and effective to investigate the vulnerability and to explore better topological structure of metro networks.
Cyber Insurance for Heterogeneous Wireless Networks

OpenAIRE

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

2017-01-01

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

Energy Technology Data Exchange (ETDEWEB)

Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.

1996-08-01

Sandia National Laboratories has assembled an interdisciplinary team to explore the applicability of probabilistic logic modeling (PLM) techniques to model network reliability for a wide variety of communications network architectures. The authors have found that the reliability and failure modes of current generation network technologies can be effectively modeled using fault tree PLM techniques. They have developed a ``plug-and-play`` fault tree analysis methodology that can be used to model connectivity and the provision of network services in a wide variety of current generation network architectures. They have also developed an efficient search algorithm that can be used to determine the minimal cut sets of an arbitrarily-interconnected (non-hierarchical) network without the construction of a fault tree model. This paper provides an overview of these modeling techniques and describes how they are applied to networks that exhibit hybrid network structures (i.e., a network in which some areas are hierarchical and some areas are not hierarchical).
A Simplified Network Model for Travel Time Reliability Analysis in a Road Network

Directory of Open Access Journals (Sweden)

Kenetsu Uchida

2017-01-01

Full Text Available This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows. In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.
Network architecture in a converged optical + IP network

Science.gov (United States)

Wakim, Walid; Zottmann, Harald

2012-01-01

As demands on Provider Networks continue to grow at exponential rates, providers are forced to evaluate how to continue to grow the network while increasing service velocity, enhancing resiliency while decreasing the total cost of ownership (TCO). The bandwidth growth that networks are experiencing is in the form packet based multimedia services such as video, video conferencing, gaming, etc... mixed with Over the Top (OTT) content providers such as Netflix, and the customer's expectations that best effort is not enough you end up with a situation that forces the provider to analyze how to gain more out of the network with less cost. In this paper we will discuss changes in the network that are driving us to a tighter integration between packet and optical layers and how to improve on today's multi - layer inefficiencies to drive down network TCO and provide for a fully integrated and dynamic network that will decrease time to revenue.
Mapping network development of international new ventures with the use of company e-mails

NARCIS (Netherlands)

Wakkee, I.A.M.

2006-01-01

International new ventures use e-mail frequently to communicate with globally dispersed contacts. In this paper we present and discuss a qualitative research method to map international network development based on company e-mails. Our approach also allows for combinations of inductive and deductive
Networking as a strategy for innovation and marketing

DEFF Research Database (Denmark)

Rasmussen, Erik Stavnsager; Jørgensen, Jacob Høj; Goduscheit, René Chester

2007-01-01

Through two in-depth case studies we intend to show in this paper, that innovation, marketing, the search for new business opportunities are interlinked in what could be labeled as networking as strategy. But being entrepreneurial, innovative and searching for new business models is a quite...
Collaborative Clustering for Sensor Networks

Science.gov (United States)

Wagstaff. Loro :/; Green Jillian; Lane, Terran

2011-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative
ExScal Backbone Network Architecture

Science.gov (United States)

2005-01-01

802.11 battery powered nodes was laid over the sensor network. We adopted the Stargate platform for the backbone tier to serve as the basis for...its head. XSS Hardware and Network: XSS stands for eXtreme Scaling Stargate . A stargate is a linux-based single board computer. It has a 400 MHz
Controlling Depth of Cellular Quiescence by an Rb-E2F Network Switch

Directory of Open Access Journals (Sweden)

Jungeun Sarah Kwon

2017-09-01

Full Text Available Quiescence is a non-proliferative cellular state that is critical to tissue repair and regeneration. Although often described as the G0 phase, quiescence is not a single homogeneous state. As cells remain quiescent for longer durations, they move progressively deeper and display a reduced sensitivity to growth signals. Deep quiescent cells, unlike senescent cells, can still re-enter the cell cycle under physiological conditions. Mechanisms controlling quiescence depth are poorly understood, representing a currently underappreciated layer of complexity in growth control. Here, we show that the activation threshold of a Retinoblastoma (Rb-E2F network switch controls quiescence depth. Particularly, deeper quiescent cells feature a higher E2F-switching threshold and exhibit a delayed traverse through the restriction point (R-point. We further show that different components of the Rb-E2F network can be experimentally perturbed, following computer model predictions, to coarse- or fine-tune the E2F-switching threshold and drive cells into varying quiescence depths.
Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease

Science.gov (United States)

Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Leonardo, Cassandra D.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matthew A.; Thompson, Paul M.

2015-01-01

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD. PMID:26640830
Telecommunication networks

CERN Document Server

Iannone, Eugenio

2011-01-01

Many argue that telecommunications network infrastructure is the most impressive and important technology ever developed. Analyzing the telecom market's constantly evolving trends, research directions, infrastructure, and vital needs, Telecommunication Networks responds with revolutionized engineering strategies to optimize network construction. Omnipresent in society, telecom networks integrate a wide range of technologies. These include quantum field theory for the study of optical amplifiers, software architectures for network control, abstract algebra required to design error correction co
Incorporate Social Network Services in E-Government Solutions: The Case of Macedonia

OpenAIRE

Koste Budinoski; Vladimir Trajkovik

2012-01-01

This paper presents the state of e-Government sophistication in R. Macedonia. The survey is done using the 20 basic public e- services. A survey result showed that further progress will need to be made on two – way interaction. Social networks are seen as convenient mean for introducing two – way interaction, social capital, transparency, anti-corruption, democracy, law enforcement, and mainly trust and citizen inclusion and empowerment. We explored the potential impacts of social media in e-...
Generating random networks and graphs

CERN Document Server

Coolen, Ton; Roberts, Ekaterina

2017-01-01

This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of rand...
Requirements of the integration of renewable energy into network charge regulation. Proposals for the further development of the network charge system. Final report

International Nuclear Information System (INIS)

Friedrichsen, Nele; Klobasa, Marian; Marwitz, Simon; Hilpert, Johannes; Sailer, Frank

2016-01-01

In this project we analyzed options to advance the network tariff system to support the German energy transition. A power system with high shares of renewables, requires more flexibility of supply and demand than the traditional system based on centralized, fossil power plants. Further, the power networks need to be adjusted and expanded. The transformation should aim at system efficiency i.e. look at both generation and network development. Network tariffs allocate the network cost towards network users. They also should provide incentives, e.g. to reduce peak load in periods of network congestion. Inappropriate network tariffs can hinder the provision of flexibility and thereby become a barrier towards system integration of renewable. Against this background, this report presents a systematic review of the German network tariff system and a discussion of several options to adapt the network tarif system in order to support the energy transition. The following aspects are analyzed: An adjustment of the privileges for industrial users to increase potential network benefits and reduce barriers towards a more market oriented behaviour. The payments for avoided network charges to distributed generation, that do not reflect cost reality in distribution networks anymore. Uniform transmission network tariffs as an option for a more appropriate allocation of cost associated with the energy transition. Increased standing fees in low voltage networks as an option to increase the cost-contribution of users with self-generation to network financing. Generator tariffs, to allocate a share of network cost to generators and provide incentives for network oriented location choice and/or feed-in.
Community Broadband Networks and the Opportunity for E-Government Services

DEFF Research Database (Denmark)

Williams, Idongesit

2017-01-01

Community Broadband Networks (CBN) facilitate Broadband connectivity in underserved areas in many countries. The lack of Broadband connectivity is one of the reasons for the slow diffusion of e-government services in many countries.This article explains how CBNs can be enabled by governments...... to facilitate the delivery of e–government services in underserved areas in the developed and developing countries.The Community Based Broadband Mobilization (CBNM) models are used as explanatory tools....
Linear network theory

CERN Document Server

Sander, K F

1964-01-01

Linear Network Theory covers the significant algebraic aspect of network theory, with minimal reference to practical circuits. The book begins the presentation of network analysis with the exposition of networks containing resistances only, and follows it up with a discussion of networks involving inductance and capacity by way of the differential equations. Classification and description of certain networks, equivalent networks, filter circuits, and network functions are also covered. Electrical engineers, technicians, electronics engineers, electricians, and students learning the intricacies
Barreloid Borders and Neuronal Activity Shape Panglial Gap Junction-Coupled Networks in the Mouse Thalamus.

Science.gov (United States)

Claus, Lena; Philippot, Camille; Griemsmann, Stephanie; Timmermann, Aline; Jabs, Ronald; Henneberger, Christian; Kettenmann, Helmut; Steinhäuser, Christian

2018-01-01

The ventral posterior nucleus of the thalamus plays an important role in somatosensory information processing. It contains elongated cellular domains called barreloids, which are the structural basis for the somatotopic organization of vibrissae representation. So far, the organization of glial networks in these barreloid structures and its modulation by neuronal activity has not been studied. We have developed a method to visualize thalamic barreloid fields in acute slices. Combining electrophysiology, immunohistochemistry, and electroporation in transgenic mice with cell type-specific fluorescence labeling, we provide the first structure-function analyses of barreloidal glial gap junction networks. We observed coupled networks, which comprised both astrocytes and oligodendrocytes. The spread of tracers or a fluorescent glucose derivative through these networks was dependent on neuronal activity and limited by the barreloid borders, which were formed by uncoupled or weakly coupled oligodendrocytes. Neuronal somata were distributed homogeneously across barreloid fields with their processes running in parallel to the barreloid borders. Many astrocytes and oligodendrocytes were not part of the panglial networks. Thus, oligodendrocytes are the cellular elements limiting the communicating panglial network to a single barreloid, which might be important to ensure proper metabolic support to active neurons located within a particular vibrissae signaling pathway. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

2006-10-15

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

Science.gov (United States)

Wang, L.; Zhang, Y. Y.; Ding, L.

2006-10-01

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

International Nuclear Information System (INIS)

Wang, L; Zhang, Y Y; Ding, L

2006-01-01

The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module
Dynamic optical resource allocation for mobile core networks with software defined elastic optical networking.

Science.gov (United States)

Zhao, Yongli; Chen, Zhendong; Zhang, Jie; Wang, Xinbo

2016-07-25

Driven by the forthcoming of 5G mobile communications, the all-IP architecture of mobile core networks, i.e. evolved packet core (EPC) proposed by 3GPP, has been greatly challenged by the users' demands for higher data rate and more reliable end-to-end connection, as well as operators' demands for low operational cost. These challenges can be potentially met by software defined optical networking (SDON), which enables dynamic resource allocation according to the users' requirement. In this article, a novel network architecture for mobile core network is proposed based on SDON. A software defined network (SDN) controller is designed to realize the coordinated control over different entities in EPC networks. We analyze the requirement of EPC-lightpath (EPCL) in data plane and propose an optical switch load balancing (OSLB) algorithm for resource allocation in optical layer. The procedure of establishment and adjustment of EPCLs is demonstrated on a SDON-based EPC testbed with extended OpenFlow protocol. We also evaluate the OSLB algorithm through simulation in terms of bandwidth blocking ratio, traffic load distribution, and resource utilization ratio compared with link-based load balancing (LLB) and MinHops algorithms.
Dimensioning of 10 Gbit/s all-optical packet switched networks based on optical label swapping routers with multistage 2R regeneration.

Science.gov (United States)

Puerto, G; Ortega, B; Manzanedo, M D; Martínez, A; Pastor, D; Capmany, J; Kovacs, G

2006-10-30

This paper describes both the experimental and theoretical investigations on the cascadability of all-optical routers in optical label swapping networks incorporating a multistage wavelength conversion with 2R regeneration. A full description of a novel experimental setup allows the packet by packet measurement up to 16 hops with 10 Gb/s payload showing 1 dB penalty with 10(-12) bit error rate. Similarly, the simulations on the system allow a prediction on the cascadability of the router up to 64 hops.
From network structure to network reorganization: implications for adult neurogenesis

International Nuclear Information System (INIS)

Schneider-Mizell, Casey M; Zochowski, Michal R; Sander, Leonard M; Parent, Jack M; Ben-Jacob, Eshel

2010-01-01

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells
Liquidity and Counterparty Risks Tradeoff in Money Market Networks

NARCIS (Netherlands)

Leon Rincon, C.E.; Sarmiento, M.

2016-01-01

We examine how liquidity is exchanged in different types of Colombian money market networks (i.e. secured, unsecured, and central bank’s repo networks). Our examination first measures and analyzes the centralization of money market networks. Afterwards, based on a simple network optimization problem
Correlated network of networks enhances robustness against catastrophic failures.

Science.gov (United States)

Min, Byungjoon; Zheng, Muhua

2018-01-01

Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule.
Fixed Access Network Sharing

Science.gov (United States)

Cornaglia, Bruno; Young, Gavin; Marchetta, Antonio

2015-12-01

Fixed broadband network deployments are moving inexorably to the use of Next Generation Access (NGA) technologies and architectures. These NGA deployments involve building fiber infrastructure increasingly closer to the customer in order to increase the proportion of fiber on the customer's access connection (Fibre-To-The-Home/Building/Door/Cabinet… i.e. FTTx). This increases the speed of services that can be sold and will be increasingly required to meet the demands of new generations of video services as we evolve from HDTV to "Ultra-HD TV" with 4k and 8k lines of video resolution. However, building fiber access networks is a costly endeavor. It requires significant capital in order to cover any significant geographic coverage. Hence many companies are forming partnerships and joint-ventures in order to share the NGA network construction costs. One form of such a partnership involves two companies agreeing to each build to cover a certain geographic area and then "cross-selling" NGA products to each other in order to access customers within their partner's footprint (NGA coverage area). This is tantamount to a bi-lateral wholesale partnership. The concept of Fixed Access Network Sharing (FANS) is to address the possibility of sharing infrastructure with a high degree of flexibility for all network operators involved. By providing greater configuration control over the NGA network infrastructure, the service provider has a greater ability to define the network and hence to define their product capabilities at the active layer. This gives the service provider partners greater product development autonomy plus the ability to differentiate from each other at the active network layer.
The Network Completion Problem: Inferring Missing Nodes and Edges in Networks

Energy Technology Data Exchange (ETDEWEB)

Kim, M; Leskovec, J

2011-11-14

Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.
Network maintenance

CERN Multimedia

IT Department

2009-01-01

A site wide network maintenance has been scheduled for Saturday 28 February. Most of the network devices of the General Purpose network will be upgraded to a newer software version, in order to improve our network monitoring capabilities. This will result in a series of short (2-5 minutes) random interruptions everywhere on the CERN sites along this day. This upgrade will not affect: the Computer centre itself, building 613, the Technical Network and the LHC experiments dedicated networks at the pits. Should you need more details on this intervention, please contact Netops by phone 74927 or email mailto:Netops@cern.ch. IT/CS Group
Network reconfiguration and neuronal plasticity in rhythm-generating networks.

Science.gov (United States)

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.
Network modelling methods for FMRI.

Science.gov (United States)

Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

2011-01-15

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
Statistical network analysis for analyzing policy networks

DEFF Research Database (Denmark)

Robins, Garry; Lewis, Jenny; Wang, Peng

2012-01-01

and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs......To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social......), and stochastic actor-oriented models. We focus most attention on ERGMs by providing an illustrative example of a model for a strategic information network within a local government. We draw inferences about the structural role played by individuals recognized as key innovators and conclude that such an approach...
Animal transportation networks

Science.gov (United States)

Perna, Andrea; Latty, Tanya

2014-01-01

Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research. PMID:25165598
ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

Directory of Open Access Journals (Sweden)

Santosh Kumar Chaudhari

2011-06-01

Full Text Available A Network Management System (NMS plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware. We propose a Decision Management System (DMS framework which uses a machine learning technique called Bayesian Belief Networks (BBN, to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.
Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC

Directory of Open Access Journals (Sweden)

Yanzan Sun

2018-03-01

Full Text Available Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE. In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs and the pico Cell Range Expansion (CRE are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.
Reconfigurable optical implementation of quantum complex networks

Science.gov (United States)

Nokkala, J.; Arzani, F.; Galve, F.; Zambrini, R.; Maniscalco, S.; Piilo, J.; Treps, N.; Parigi, V.

2018-05-01

Network theory has played a dominant role in understanding the structure of complex systems and their dynamics. Recently, quantum complex networks, i.e. collections of quantum systems arranged in a non-regular topology, have been theoretically explored leading to significant progress in a multitude of diverse contexts including, e.g., quantum transport, open quantum systems, quantum communication, extreme violation of local realism, and quantum gravity theories. Despite important progress in several quantum platforms, the implementation of complex networks with arbitrary topology in quantum experiments is still a demanding task, especially if we require both a significant size of the network and the capability of generating arbitrary topology—from regular to any kind of non-trivial structure—in a single setup. Here we propose an all optical and reconfigurable implementation of quantum complex networks. The experimental proposal is based on optical frequency combs, parametric processes, pulse shaping and multimode measurements allowing the arbitrary control of the number of the nodes (optical modes) and topology of the links (interactions between the modes) within the network. Moreover, we also show how to simulate quantum dynamics within the network combined with the ability to address its individual nodes. To demonstrate the versatility of these features, we discuss the implementation of two recently proposed probing techniques for quantum complex networks and structured environments.
Critical field measurements in a superconducting networks

International Nuclear Information System (INIS)

Pannetier, B.; Chaussy, J.; Rammal, R.

1984-01-01

We have measured the critical field of a periodic two-dimensional network of superconducting indium. At low fields, the critical line Hsub(c)(T) reflects the network topology and exhibits well-defined cusps due to flux quantization corresponding to both integer and rational number of flux quanta phi 0 = h/2e per unit loop of the network [fr
Analyzing Multimode Wireless Sensor Networks Using the Network Calculus

Directory of Open Access Journals (Sweden)

Xi Jin

2015-01-01

Full Text Available The network calculus is a powerful tool to analyze the performance of wireless sensor networks. But the original network calculus can only model the single-mode wireless sensor network. In this paper, we combine the original network calculus with the multimode model to analyze the maximum delay bound of the flow of interest in the multimode wireless sensor network. There are two combined methods A-MM and N-MM. The method A-MM models the whole network as a multimode component, and the method N-MM models each node as a multimode component. We prove that the maximum delay bound computed by the method A-MM is tighter than or equal to that computed by the method N-MM. Experiments show that our proposed methods can significantly decrease the analytical delay bound comparing with the separate flow analysis method. For the large-scale wireless sensor network with 32 thousands of sensor nodes, our proposed methods can decrease about 70% of the analytical delay bound.
Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging

Directory of Open Access Journals (Sweden)

Maihemuti Maimaiti

2017-11-01

Full Text Available Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF, long short-term memory (LSTM, bidirectional LSTM networks (BI-LSTM, LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels and 0.48% (64 labels. The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages.
Sonata: Query-Driven Network Telemetry

KAUST Repository

Gupta, Arpit; Harrison, Rob; Pawar, Ankita; Birkner, Rü diger; Canini, Marco; Feamster, Nick; Rexford, Jennifer; Willinger, Walter

2017-01-01

Operating networks depends on collecting and analyzing measurement data. Current technologies do not make it easy to do so, typically because they separate data collection (e.g., packet capture or flow monitoring) from analysis, producing either too much data to answer a general question or too little data to answer a detailed question. In this paper, we present Sonata, a network telemetry system that uses a uniform query interface to drive the joint collection and analysis of network traffic. Sonata takes the advantage of two emerging technologies---streaming analytics platforms and programmable network devices---to facilitate joint collection and analysis. Sonata allows operators to more directly express network traffic analysis tasks in terms of a high-level language. The underlying runtime partitions each query into a portion that runs on the switch and another that runs on the streaming analytics platform iteratively refines the query to efficiently capture only the traffic that pertains to the operator's query, and exploits sketches to reduce state in switches in exchange for more approximate results. Through an evaluation of a prototype implementation, we demonstrate that Sonata can support a wide range of network telemetry tasks with less state in the network, and lower data rates to streaming analytics systems, than current approaches can achieve.

Sonata: Query-Driven Network Telemetry

KAUST Repository

Gupta, Arpit

2017-05-02

Operating networks depends on collecting and analyzing measurement data. Current technologies do not make it easy to do so, typically because they separate data collection (e.g., packet capture or flow monitoring) from analysis, producing either too much data to answer a general question or too little data to answer a detailed question. In this paper, we present Sonata, a network telemetry system that uses a uniform query interface to drive the joint collection and analysis of network traffic. Sonata takes the advantage of two emerging technologies---streaming analytics platforms and programmable network devices---to facilitate joint collection and analysis. Sonata allows operators to more directly express network traffic analysis tasks in terms of a high-level language. The underlying runtime partitions each query into a portion that runs on the switch and another that runs on the streaming analytics platform iteratively refines the query to efficiently capture only the traffic that pertains to the operator\\'s query, and exploits sketches to reduce state in switches in exchange for more approximate results. Through an evaluation of a prototype implementation, we demonstrate that Sonata can support a wide range of network telemetry tasks with less state in the network, and lower data rates to streaming analytics systems, than current approaches can achieve.
Inferring Phylogenetic Networks Using PhyloNet.

Science.gov (United States)

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.
Approaching human language with complex networks

Science.gov (United States)

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).
Local Social Networks

DEFF Research Database (Denmark)

Sapuppo, Antonio; Sørensen, Lene Tolstrup

2011-01-01

Online social networks have become essential for many users in their daily communication. Through a combination of the online social networks with opportunistic networks, a new concept arises: Local Social Networks. The target of local social networks is to promote social networking benefits...... in physical environment in order to leverage personal affinities in the users' surroundings. The purpose of this paper is to present and discuss the concept of local social networks as a new social communication system. Particularly, the preliminary architecture and the prototype of local social networks...
From Offline Social Networks to Online Social Networks: Changes in Entrepreneurship

Directory of Open Access Journals (Sweden)

Yang SONG

2015-01-01

Full Text Available The paper reviewed studies of entrepreneurship based on the emergency of online social networks. Similar to offline social networks, entrepreneurs’ online social networks have their own unique characteristics. We first reviewed the offline network based research on entrepreneurship. Then we reviewed the studies of entrepreneurship in the context of online social networks including those focusing on topics of network structures and network ties. We highlighted online network communities based on the data collected from LinkedIn, Facebook and Twitter. Our research implies that both researcher and entrepreneurs are facing new opportunities due to the emergence of online social networks.
Investigation of the network delay on Profibus-DP based network

OpenAIRE

Yılmaz, C.; Gürdal, O.; Sayan, H.H.

2008-01-01

The mathematical model of the network-induced delay control systems (NDCS) is given. Also the role of the NDCS’s components such as controller, sensor and network environment on the network-induced delay are included in the mathematical model of the system. The network delay is investigated on Profibus-DP based network application and experimental results obtained are presented graphically. The experimental results obtained show that the network induced delay is randomly changed according to ...
Designing in-building optical fiber networks

NARCIS (Netherlands)

Koonen, A.M.J.; Boom, van den H.P.A.; Tangdiongga, E.; Jung, H.D.; Guignard, P.

2010-01-01

Optical fiber in-building networks carrying wired and wireless services can outperform CAT-5E networks regarding versatility and installation costs. POF-based point-to-point architectures are optimum for small buildings, and (optically routed) SMF-based bus architectures for larger buildings.
Towards a proof of the Kahn principle for linear dynamic networks

NARCIS (Netherlands)

A. de Bruin (Arie); S-H. Nienhuys-Cheng (Shan-Hwei)

1994-01-01

textabstractWe consider dynamic Kahn-like data flow networks, i.e. networks consisting of deterministic processes each of which is able to expand into a subnetwork. The Kahn principle states that such networks are deterministic, i.e. that for each network we have that each execution provided with
Random walks on generalized Koch networks

International Nuclear Information System (INIS)

Sun, Weigang

2013-01-01

For deterministically growing networks, it is a theoretical challenge to determine the topological properties and dynamical processes. In this paper, we study random walks on generalized Koch networks with features that include an initial state that is a globally connected network to r nodes. In each step, every existing node produces m complete graphs. We then obtain the analytical expressions for first passage time (FPT), average return time (ART), i.e. the average of FPTs for random walks from node i to return to the starting point i for the first time, and average sending time (AST), defined as the average of FPTs from a hub node to all other nodes, excluding the hub itself with regard to network parameters m and r. For this family of Koch networks, the ART of the new emerging nodes is identical and increases with the parameters m or r. In addition, the AST of our networks grows with network size N as N ln N and also increases with parameter m. The results obtained in this paper are the generalizations of random walks for the original Koch network. (paper)
Vulnerability of complex networks

Science.gov (United States)

Mishkovski, Igor; Biey, Mario; Kocarev, Ljupco

2011-01-01

We consider normalized average edge betweenness of a network as a metric of network vulnerability. We suggest that normalized average edge betweenness together with is relative difference when certain number of nodes and/or edges are removed from the network is a measure of network vulnerability, called vulnerability index. Vulnerability index is calculated for four synthetic networks: Erdős-Rényi (ER) random networks, Barabási-Albert (BA) model of scale-free networks, Watts-Strogatz (WS) model of small-world networks, and geometric random networks. Real-world networks for which vulnerability index is calculated include: two human brain networks, three urban networks, one collaboration network, and two power grid networks. We find that WS model of small-world networks and biological networks (human brain networks) are the most robust networks among all networks studied in the paper.
Friendly network robotics; Friendly network robotics

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

This paper summarizes the research results on the friendly network robotics in fiscal 1996. This research assumes an android robot as an ultimate robot and the future robot system utilizing computer network technology. The robot aiming at human daily work activities in factories or under extreme environments is required to work under usual human work environments. The human robot with similar size, shape and functions to human being is desirable. Such robot having a head with two eyes, two ears and mouth can hold a conversation with human being, can walk with two legs by autonomous adaptive control, and has a behavior intelligence. Remote operation of such robot is also possible through high-speed computer network. As a key technology to use this robot under coexistence with human being, establishment of human coexistent robotics was studied. As network based robotics, use of robots connected with computer networks was also studied. In addition, the R-cube (R{sup 3}) plan (realtime remote control robot technology) was proposed. 82 refs., 86 figs., 12 tabs.
Networked Identities

DEFF Research Database (Denmark)

Ryberg, Thomas; Larsen, Malene Charlotte

2008-01-01

of CoPs we shall argue that the metaphor or theory of networked learning is itself confronted with some central tensions and challenges that need to be addressed. We then explore these theoretical and analytic challenges to the network metaphor, through an analysis of a Danish social networking site. We......In this article we take up a critique of the concept of Communities of Practice (CoP) voiced by several authors, who suggest that networks may provide a better metaphor to understand social forms of organisation and learning. Through a discussion of the notion of networked learning and the critique...... argue that understanding meaning-making and ‘networked identities’ may be relevant analytic entry points in navigating the challenges....
A source-controlled data center network model.

Science.gov (United States)

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.
A source-controlled data center network model

Science.gov (United States)

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925
Dynamical Response of Networks Under External Perturbations: Exact Results

Science.gov (United States)

Chinellato, David D.; Epstein, Irving R.; Braha, Dan; Bar-Yam, Yaneer; de Aguiar, Marcus A. M.

2015-04-01

We give exact statistical distributions for the dynamic response of influence networks subjected to external perturbations. We consider networks whose nodes have two internal states labeled 0 and 1. We let nodes be frozen in state 0, in state 1, and the remaining nodes change by adopting the state of a connected node with a fixed probability per time step. The frozen nodes can be interpreted as external perturbations to the subnetwork of free nodes. Analytically extending and to be smaller than 1 enables modeling the case of weak coupling. We solve the dynamical equations exactly for fully connected networks, obtaining the equilibrium distribution, transition probabilities between any two states and the characteristic time to equilibration. Our exact results are excellent approximations for other topologies, including random, regular lattice, scale-free and small world networks, when the numbers of fixed nodes are adjusted to take account of the effect of topology on coupling to the environment. This model can describe a variety of complex systems, from magnetic spins to social networks to population genetics, and was recently applied as a framework for early warning signals for real-world self-organized economic market crises.
Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

Science.gov (United States)

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with
Contingent approach to Internet-based supply network integration

Science.gov (United States)

Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon

2001-10-01

The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.
Location based Network Optimizations for Mobile Wireless Networks

DEFF Research Database (Denmark)

Nielsen, Jimmy Jessen

selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...
Startup : Philippine Community eCentres Network | IDRC ...

International Development Research Centre (IDRC) Digital Library (Canada)

... served as a model for networking the telecentres and pointing future directions. ... long-term climate action to reduce social inequality, promote greater gender parity, ... including heat stress, water management, and climate-related migration.
Network Performance Improvement under Epidemic Failures in Optical Transport Networks

DEFF Research Database (Denmark)

Fagertun, Anna Manolova; Ruepp, Sarah Renée

2013-01-01

In this paper we investigate epidemic failure spreading in large- scale GMPLS-controlled transport networks. By evaluating the effect of the epidemic failure spreading on the network, we design several strategies for cost-effective network performance improvement via differentiated repair times....... First we identify the most vulnerable and the most strategic nodes in the network. Then, via extensive simulations we show that strategic placement of resources for improved failure recovery has better performance than randomly assigning lower repair times among the network nodes. Our OPNET simulation...... model can be used during the network planning process for facilitating cost- effective network survivability design....

Enhanced just-in-time plus protocol for optical burst switching networks

Science.gov (United States)

Rodrigues, Joel J. P. C.; Gregório, José M. B.; Vasilakos, Athanasios V.

2010-07-01

We propose a new one-way resource reservation protocol for optical burst switching (OBS) networks, called Enhanced Just-in-Time Plus (E-JIT+). The protocol is described in detail, and its formal specification is presented, following an extended finite state machine approach. The performance evaluation of E-JIT+ is analyzed in comparison with other proposed OBS protocols (JIT+ and E-JIT) for the following network topologies: rings; degree-two, degree-three, and degree-four chordal rings; mesh-torus; NSFNET; ARPANET; FCCN-NET; and the European Optical Network. We evaluate and compare the performance of the different protocols in terms of burst loss probability, taking into account the most important OBS network parameters. It was shown that E-JIT+ performs better than available one-way resource reservation protocols for all the evaluated network topologies. Moreover, the scalability of E-JIT+ was observed, and when the network traffic increases, the burst loss probability also increases, leading to a worse network performance.
Complex quantum network geometries: Evolution and phase transitions

Science.gov (United States)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.
Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Directory of Open Access Journals (Sweden)

Laisen Nie

2018-01-01

Full Text Available Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
Multiple Social Networks, Data Models and Measures for

DEFF Research Database (Denmark)

Magnani, Matteo; Rossi, Luca

2017-01-01

Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...
Risk-Aware Design of Value and Coordination Networks

NARCIS (Netherlands)

Fatemi, Hassan

2012-01-01

A collaborative network is a network consisting of a set of autonomous actors (e.g. enterprizes, organizations and people) that collaborate to achieve common or compatible goals. In a collaborative network each enterprize contributes with its own specific products or services to satisfy the consumer
Networks around entrepreneurs

DEFF Research Database (Denmark)

Bertelsen, Rasmus Gjedssø; Ashourizadeh, Shayegheh; Jensen, Kent Wickstrøm

2017-01-01

Purpose: Entrepreneurs are networking with others to get advice for their businesses. The networking differs between men and women; notably, men are more often networking in the public sphere and women are more often networking in the private sphere. The aim here is to account for how such gender......Purpose: Entrepreneurs are networking with others to get advice for their businesses. The networking differs between men and women; notably, men are more often networking in the public sphere and women are more often networking in the private sphere. The aim here is to account for how...... such gendering of entrepreneurs’ networks differ between societies and cultures. Research Design: Based on survey data from the Global Entrepreneurships Monitor, a sample of 16,365 entrepreneurs is used to compare the gendering of entrepreneurs’ networks in China, and five countries largely located around...... the Persian Gulf, namely Yemen, Iran, Saudi Arabia, Qatar and United Arab Emirates. Findings: Analyses show that female entrepreneurs tend to have slightly larger private sphere networks than male entrepreneurs. The differences between male and female entrepreneurs’ networking in the public sphere...
The E-Business Research Network: summary of the results of the Dutch pilot survey

NARCIS (Netherlands)

A. van der Wiele (Ton); A.R.T. Williams (Roger); J.D. van Iwaarden (Jos); M. Wilson (Melanie); B.G. Dale (Barry)

2001-01-01

textabstractA project has been started with the intention to develop an E-Business Research Network on E-business related research in business and management. The initiative has been taken in co-operation between Erasmus University and UMIST to develop a project in which the first stage concerns the
Networks: structure and action : steering in and steering by policy networks

NARCIS (Netherlands)

Dassen, A.

2010-01-01

This thesis explores the opportunities to build a structural policy network model that is rooted in social network theories. By making a distinction between a process of steering in networks, and a process of steering by networks, it addresses the effects of network structures on network dynamics as
Cascading Generative Adversarial Networks for Targeted

KAUST Repository

Hamdi, Abdullah

2018-01-01

Abundance of labelled data played a crucial role in the recent developments in computer vision, but that faces problems like scalability and transferability to the wild. One alternative approach is to utilize the data without labels, i.e. unsupervised learning, in learning valuable information and put it in use to tackle vision problems. Generative Adversarial Networks (GANs) have gained momentum for their ability to model image distributions in unsupervised manner. They learn to emulate the training set and that enables sampling from that domain and using the knowledge learned for useful applications. Several methods proposed enhancing GANs, including regularizing the loss with some feature matching. We seek to push GANs beyond the data in the training and try to explore unseen territory in the image manifold. We first propose a new regularizer for GAN based on K-Nearest Neighbor (K-NN) selective feature matching to a target set Y in high-level feature space, during the adversarial training of GAN on the base set X, and we call this novel model K-GAN. We show that minimizing the added term follows from cross-entropy minimization between the distributions of GAN and set Y. Then, we introduce a cascaded framework for GANs that try to address the task of imagining a new distribution that combines the base set X and target set Y by cascading sampling GANs with translation GANs, and we dub the cascade of such GANs as the Imaginative Adversarial Network (IAN). Several cascades are trained on a collected dataset Zoo-Faces and generated innovative samples are shown, including from K-GAN cascade. We conduct an objective and subjective evaluation for different IAN setups in the addressed task of generating innovative samples and we show the effect of regularizing GAN on different scores. We conclude with some useful applications for these IANs, like multi-domain manifold traversing.
Cascading Generative Adversarial Networks for Targeted

KAUST Repository

Hamdi, Abdullah

2018-04-09

Abundance of labelled data played a crucial role in the recent developments in computer vision, but that faces problems like scalability and transferability to the wild. One alternative approach is to utilize the data without labels, i.e. unsupervised learning, in learning valuable information and put it in use to tackle vision problems. Generative Adversarial Networks (GANs) have gained momentum for their ability to model image distributions in unsupervised manner. They learn to emulate the training set and that enables sampling from that domain and using the knowledge learned for useful applications. Several methods proposed enhancing GANs, including regularizing the loss with some feature matching. We seek to push GANs beyond the data in the training and try to explore unseen territory in the image manifold. We first propose a new regularizer for GAN based on K-Nearest Neighbor (K-NN) selective feature matching to a target set Y in high-level feature space, during the adversarial training of GAN on the base set X, and we call this novel model K-GAN. We show that minimizing the added term follows from cross-entropy minimization between the distributions of GAN and set Y. Then, we introduce a cascaded framework for GANs that try to address the task of imagining a new distribution that combines the base set X and target set Y by cascading sampling GANs with translation GANs, and we dub the cascade of such GANs as the Imaginative Adversarial Network (IAN). Several cascades are trained on a collected dataset Zoo-Faces and generated innovative samples are shown, including from K-GAN cascade. We conduct an objective and subjective evaluation for different IAN setups in the addressed task of generating innovative samples and we show the effect of regularizing GAN on different scores. We conclude with some useful applications for these IANs, like multi-domain manifold traversing.
Factors determining nestedness in complex networks.

Directory of Open Access Journals (Sweden)

Samuel Jonhson

Full Text Available Understanding the causes and effects of network structural features is a key task in deciphering complex systems. In this context, the property of network nestedness has aroused a fair amount of interest as regards ecological networks. Indeed, Bastolla et al. introduced a simple measure of network nestedness which opened the door to analytical understanding, allowing them to conclude that biodiversity is strongly enhanced in highly nested mutualistic networks. Here, we suggest a slightly refined version of such a measure of nestedness and study how it is influenced by the most basic structural properties of networks, such as degree distribution and degree-degree correlations (i.e. assortativity. We find that most of the empirically found nestedness stems from heterogeneity in the degree distribution. Once such an influence has been discounted - as a second factor - we find that nestedness is strongly correlated with disassortativity and hence - as random networks have been recently found to be naturally disassortative - they also tend to be naturally nested just as the result of chance.
Cognitive Relay Networks: A Comprehensive Survey

Directory of Open Access Journals (Sweden)

Ayesha Naeem

2015-07-01

Full Text Available Cognitive radio is an emerging technology to deal with the scarcity and requirement of radio spectrum by dynamically assigning spectrum to unlicensed user . This revolutionary technology shifts the paradigm in the wireless system design by all owing unlicensed user the ability to sense, adapt and share the dynamic spectrum. Cognitive radio technology have been applied to different networks and applications ranging from wireless to public saf ety, smart grid, medical, rela y and cellular applications to increase the throughput and spectrum efficiency of the network. Among these applications, cognitive relay networks is one of the application where cognitive radio technology has been applied. Cognitiv e rela y network increases the network throughput by reducing the complete pa th loss and also by ensuring cooper ation among secondary users and cooperation among primary and secondary users. In this paper , our aim is to provide a survey on cognitive relay network. We also provide a detailed review on existing schemes in cognitive relay networks on the basis of relaying protocol, relay cooperation and channel model.
Effects of multi-state links in network community detection

International Nuclear Information System (INIS)

Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

2017-01-01

A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.
Network information provision to potential generators: Appendices

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-07-01

This Code of Practice (CoP) has been prepared to outline the standard of information that Distribution Network Operators (DNOs) should be required to produce in relation to the provision of network maps, schematic diagrams and specific network data. Network information from DNOs may be required by generators (and other customers) in order to assess the potential opportunities available for the connection of new generation plant. Seven Year Statements are published annually by the Transmission Licensees operating in Great Britain, i.e. The National Grid Company, Scottish Power and Scottish Hydro Electric, and contain all the network information relating to each transmission system, e.g. Generation Capacities, System Parameters and Plant Fault Levels. A similar arrangement for DNOs has been outlined in the Electricity Distribution Licence published by Ofgem. Under Condition 25 of the licence, 'The Long Term Development Statement', distribution licence holders are required to make available historic and planned network data. By providing sufficient network information, competition in generation will be improved. At the time of writing, any party interested in assessing distribution network information needs to make contact with the appropriate DNO, identifying the correct department and person. Written applications are then sent to that person, describing the type of network information that is required. Information required from embedded generators by DNOs is specified in detail in both of The Distribution Codes of England and Wales, and Scotland. However, there are no guidelines or details of network information to be provided by DNOs. This Code of Practise is designed to balance this situation and help DNOs, prospective generators and other applicants for information to achieve satisfaction by clarifying expectations. (Author)
Analysis of robustness of urban bus network

Science.gov (United States)

Tao, Ren; Yi-Fan, Wang; Miao-Miao, Liu; Yan-Jie, Xu

2016-02-01

In this paper, the invulnerability and cascade failures are discussed for the urban bus network. Firstly, three static models(bus stop network, bus transfer network, and bus line network) are used to analyse the structure and invulnerability of urban bus network in order to understand the features of bus network comprehensively. Secondly, a new way is proposed to study the invulnerability of urban bus network by modelling two layered networks, i.e., the bus stop-line network and the bus line-transfer network and then the interactions between different models are analysed. Finally, by modelling a new layered network which can reflect the dynamic passenger flows, the cascade failures are discussed. Then a new load redistribution method is proposed to study the robustness of dynamic traffic. In this paper, the bus network of Shenyang City which is one of the biggest cities in China, is taken as a simulation example. In addition, some suggestions are given to improve the urban bus network and provide emergency strategies when traffic congestion occurs according to the numerical simulation results. Project supported by the National Natural Science Foundation of China (Grant Nos. 61473073, 61374178, 61104074, and 61203329), the Fundamental Research Funds for the Central Universities (Grant Nos. N130417006, L1517004), and the Program for Liaoning Excellent Talents in University (Grant No. LJQ2014028).
Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

Directory of Open Access Journals (Sweden)

Kim Hyun

2011-12-01

Full Text Available Abstract Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.
Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

Science.gov (United States)

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.
SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

International Nuclear Information System (INIS)

Kalet, A; Phillips, M; Gennari, J

2014-01-01

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the
SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

Energy Technology Data Exchange (ETDEWEB)

Kalet, A; Phillips, M; Gennari, J [UniversityWashington, Seattle, WA (United States)

2014-06-01

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the
Neural network classification of quark and gluon jets

International Nuclear Information System (INIS)

Graham, M.A.; Jones, L.M.; Herbin, S.

1995-01-01

We demonstrate that there are characteristics common to quark jets and to gluon jets regardless of the interaction that produced them. The classification technique we use depends on the mass of the jet as well as the center-of-mass energy of the hard subprocess that produces the jet. In addition, we present the quark-gluon separability results of an artificial neural network trained on three-jet e + e - events at the Z 0 mass, using a back-propagation algorithm. The inputs to the network are the longitudinal momenta of the leading hadrons in the jet. We tested the network with quark and gluon jets from both e + e - →3 jets and bar pp→2 jets. Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts

Clustering Coefficients for Correlation Networks

Directory of Open Access Journals (Sweden)

Naoki Masuda

2018-03-01

Full Text Available Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients
Clustering Coefficients for Correlation Networks.

Science.gov (United States)

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly
Clustering Coefficients for Correlation Networks

Science.gov (United States)

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly
Virtualized Network Function Orchestration System and Experimental Network Based QR Recognition for a 5G Mobile Access Network

Directory of Open Access Journals (Sweden)

Misun Ahn

2017-12-01

Full Text Available This paper proposes a virtualized network function orchestration system based on Network Function Virtualization (NFV, one of the main technologies in 5G mobile networks. This system should provide connectivity between network devices and be able to create flexible network function and distribution. This system focuses more on access networks. By experimenting with various scenarios of user service established and activated in a network, we examine whether rapid adoption of new service is possible and whether network resources can be managed efficiently. The proposed method is based on Bluetooth transfer technology and mesh networking to provide automatic connections between network machines and on a Docker flat form, which is a container virtualization technology for setting and managing key functions. Additionally, the system includes a clustering and recovery measure regarding network function based on the Docker platform. We will briefly introduce the QR code perceived service as a user service to examine the proposal and based on this given service, we evaluate the function of the proposal and present analysis. Through the proposed approach, container relocation has been implemented according to a network device’s CPU usage and we confirm successful service through function evaluation on a real test bed. We estimate QR code recognition speed as the amount of network equipment is gradually increased, improving user service and confirm that the speed of recognition is increased as the assigned number of network devices is increased by the user service.
Relabeling exchange method (REM) for learning in neural networks

Science.gov (United States)

Wu, Wen; Mammone, Richard J.

1994-02-01

The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.
Yucca Mountain licensing support network archive assistant.

Energy Technology Data Exchange (ETDEWEB)

Dunlavy, Daniel M.; Bauer, Travis L.; Verzi, Stephen J.; Basilico, Justin Derrick; Shaneyfelt, Wendy

2008-03-01

This report describes the Licensing Support Network (LSN) Assistant--a set of tools for categorizing e-mail messages and documents, and investigating and correcting existing archives of categorized e-mail messages and documents. The two main tools in the LSN Assistant are the LSN Archive Assistant (LSNAA) tool for recategorizing manually labeled e-mail messages and documents and the LSN Realtime Assistant (LSNRA) tool for categorizing new e-mail messages and documents. This report focuses on the LSNAA tool. There are two main components of the LSNAA tool. The first is the Sandia Categorization Framework, which is responsible for providing categorizations for documents in an archive and storing them in an appropriate Categorization Database. The second is the actual user interface, which primarily interacts with the Categorization Database, providing a way for finding and correcting categorizations errors in the database. A procedure for applying the LSNAA tool and an example use case of the LSNAA tool applied to a set of e-mail messages are provided. Performance results of the categorization model designed for this example use case are presented.
Competitive cluster growth in complex networks.

Science.gov (United States)

Moreira, André A; Paula, Demétrius R; Costa Filho, Raimundo N; Andrade, José S

2006-06-01

In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.
COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks

NARCIS (Netherlands)

Sie, Rory

2012-01-01

Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.
Critical behavior and correlations on scale-free small-world networks: Application to network design

Science.gov (United States)

Ostilli, M.; Ferreira, A. L.; Mendes, J. F. F.

2011-06-01

We analyze critical phenomena on networks generated as the union of hidden variable models (networks with any desired degree sequence) with arbitrary graphs. The resulting networks are general small worlds similar to those à la Watts and Strogatz, but with a heterogeneous degree distribution. We prove that the critical behavior (thermal or percolative) remains completely unchanged by the presence of finite loops (or finite clustering). Then, we show that, in large but finite networks, correlations of two given spins may be strong, i.e., approximately power-law-like, at any temperature. Quite interestingly, if γ is the exponent for the power-law distribution of the vertex degree, for γ⩽3 and with or without short-range couplings, such strong correlations persist even in the thermodynamic limit, contradicting the common opinion that, in mean-field models, correlations always disappear in this limit. Finally, we provide the optimal choice of rewiring under which percolation phenomena in the rewired network are best performed, a natural criterion to reach best communication features, at least in noncongested regimes.
A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks.

Science.gov (United States)

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-01-16

IEEE 802.15.4e standard proposes Deterministic and Synchronous Multichannel Extension (DSME) mode for wireless sensor networks (WSNs) to support industrial, commercial and health care applications. In this paper, a new channel access scheme and beacon scheduling schemes are designed for the IEEE 802.15.4e enabled WSNs in star topology to reduce the network discovery time and energy consumption. In addition, a new dynamic guaranteed retransmission slot allocation scheme is designed for devices with the failure Guaranteed Time Slot (GTS) transmission to reduce the retransmission delay. To evaluate our schemes, analytical models are designed to analyze the performance of WSNs in terms of reliability, delay, throughput and energy consumption. Our schemes are validated with simulation and analytical results and are observed that simulation results well match with the analytical one. The evaluated results of our designed schemes can improve the reliability, throughput, delay, and energy consumptions significantly.
SEADE: Countering the Futility of Network Security

Science.gov (United States)

2015-10-01

guards, and computer cages) and logical security measures (network firewall and intrusion detection). However, no matter how many layers of network...security built-in and with minimal security dependence on network security appliances (e.g., firewalls ). As Secretary of Defense Ashton Carter...based analysis that assumes nothing bad will happen to applications/data if those defenses prevent malware transactions at the entrance. The
Managing Network Partitions in Structured P2P Networks

Science.gov (United States)

Shafaat, Tallat M.; Ghodsi, Ali; Haridi, Seif

Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to scale, tolerate failures, and self-manage. Any long-lived Internet-scale distributed system is destined to face network partitions. Consequently, the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems. This makes it a crucial requirement for building any structured peer-to-peer systems to be resilient to network partitions. Although the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems, it has hardly been studied in the context of structured peer-to-peer systems. Structured overlays have mainly been studied under churn (frequent joins/failures), which as a side effect solves the problem of network partitions, as it is similar to massive node failures. Yet, the crucial aspect of network mergers has been ignored. In fact, it has been claimed that ring-based structured overlay networks, which constitute the majority of the structured overlays, are intrinsically ill-suited for merging rings. In this chapter, we motivate the problem of network partitions and mergers in structured overlays. We discuss how a structured overlay can automatically detect a network partition and merger. We present an algorithm for merging multiple similar ring-based overlays when the underlying network merges. We examine the solution in dynamic conditions, showing how our solution is resilient to churn during the merger, something widely believed to be difficult or impossible. We evaluate the algorithm for various scenarios and show that even when falsely detecting a merger, the algorithm quickly terminates and does not clutter the network with many messages. The algorithm is flexible as the tradeoff between message complexity and time complexity can be adjusted by a parameter.
OPTIMAL NETWORK TOPOLOGY DESIGN

Science.gov (United States)

Yuen, J. H.

1994-01-01

This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Link prediction in multiplex online social networks

Science.gov (United States)

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

2017-02-01

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

Science.gov (United States)

Reddy, Anupama; Huang, C Chris; Liu, Huiqing; Delisi, Charles; Nevalainen, Marja T; Szalma, Sandor; Bhanot, Gyan

2010-01-01

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily
Dual connectivity for LTE-advanced heterogeneous networks

DEFF Research Database (Denmark)

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

2016-01-01

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

DEFF Research Database (Denmark)

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

2016-01-01

The energy of the node in the Wireless Sensor Networks (WSNs) is scare and causes the variation in the lifetime of the network. Also, the throughput and delay of the network depend on how long the network sustains i.e. energy consumption. One way to increase the sustainability of network...
Special issue on network coding

Science.gov (United States)

Monteiro, Francisco A.; Burr, Alister; Chatzigeorgiou, Ioannis; Hollanti, Camilla; Krikidis, Ioannis; Seferoglu, Hulya; Skachek, Vitaly

2017-12-01

Future networks are expected to depart from traditional routing schemes in order to embrace network coding (NC)-based schemes. These have created a lot of interest both in academia and industry in recent years. Under the NC paradigm, symbols are transported through the network by combining several information streams originating from the same or different sources. This special issue contains thirteen papers, some dealing with design aspects of NC and related concepts (e.g., fountain codes) and some showcasing the application of NC to new services and technologies, such as data multi-view streaming of video or underwater sensor networks. One can find papers that show how NC turns data transmission more robust to packet losses, faster to decode, and more resilient to network changes, such as dynamic topologies and different user options, and how NC can improve the overall throughput. This issue also includes papers showing that NC principles can be used at different layers of the networks (including the physical layer) and how the same fundamental principles can lead to new distributed storage systems. Some of the papers in this issue have a theoretical nature, including code design, while others describe hardware testbeds and prototypes.
Topological Rankings in Communication Networks

DEFF Research Database (Denmark)

Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Træholt, Chresten

2015-01-01

In the theory of communication the central problem is to study how agents exchange information. This problem may be studied using the theory of connected spaces in topology, since a communication network can be modelled as a topological space such that agents can communicate if and only...... if they belong to the same path connected component of that space. In order to study combinatorial properties of such a communication network, notions from algebraic topology are applied. This makes it possible to determine the shape of a network by concrete invariants, e.g. the number of connected components...
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Directory of Open Access Journals (Sweden)

Haiyang Yu

2017-06-01

Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

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